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Analytics

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This white paper discusses the findings of a study conducted by SAS and Health Data Management in an effort to shed light on the attitudes of senior actuarial executives toward the use of analytics. According to study findings, SAEs are not fully aware of tools that offer advanced functionality, such as forecasting, predictive modeling and optimization capacity. The study, which was conducted via in-depth interviews with health plan actuarial executives from across the country, uncovers opinions on the use of analytics in managing and leveraging data, forecasting, trend analysis, reserve management and related matters.
Building customer trust and value through improved contact policy management
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This paper details challenges that affect how communications are planned and deployed, including limiting factors such as budget caps, campaign volumes and channel capacities. Communications often can't be anticipated until a trigger-based or real-time interaction uncovers a need and the opportunity for an additional communication. Successful processes utilize the latest analytical techniques and consider a company's corporate objectives and business rules. This paper discusses a process called adaptive contact planning as a way to create more effective, and thus more profitable, marketing campaigns.
Optimize Your Network Investments & Transform Customer Experience
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For communications service providers, delivering a good customer experience is paramount. And good network performance is one of the best ways to keep customers happy. But most service providers struggle to get insight into their own network data and put it to strategic use. Heavy Reading reveals how predictive analytics gives service providers real-time visibility into their networks – boosting revenue potential and ensuring more satisfied customers, better targeted marketing, fine-tuned capacity planning and top-notch service assurance.
How to optimally allocate resources in alignment with enterprise-level objectives
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This white paper provides five steps to resource optimization, with a visual model and a variety of real-world examples to help business leaders understand how to allocate resources in alignment with enterprise-level objectives. You'll also learn about the technology required to support resource optimization.
Highlights from a panel discussion at the 2013 SAS Financial Services Executive Summit
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In the age of big data, business units of banks and insurance companies can no longer toss a request over the wall to IT just in time and hope IT resources will be available. IT has to be involved early in strategic and proactive planning. At the same time, business leaders need to be more technology-aware, so they are less likely to ask for the impossible. This paper summarizes a panel discussion at the 2013 SAS Financial Services Executive Summit. Industry leaders from large banking and insurance companies shared their ideas about how IT and the business should work together and what the organization gains when both sides perform as strategic partners.
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The unprecedented rise of health care costs makes price transparency critical to health care reform. So that states can identify and understand variation in payment and quality across plans and providers, all-payer claims databases (APCDs) are being created that provide further benefits for health care decision makers. When data is used for decision making we see cost containment and quality improvement efforts come to fruition. This paper looks at where transparency will lead us; what an APCD looks like with its many uses; how big analytics is made possible through the database; and best practices in advocating and implementing an APCD.
Profitable Analytic Strategies for Life and Annuity Carriers
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In the insurance industry, the companies that focus on product lines typically under the diversified insurance umbrella (such as group and individual life, annuities, retirement plans, financial services and supplemental health products) have been slow to adopt predictive analytics within their organizations. Other industries, including property and casualty insurers, continually demonstrate success in using analytics to grow their businesses more profitably and increase revenue while managing risk. Life insurance executives are beginning to recognize the need to evaluate analytics as a way to innovate, differentiate and improve their organizations. This paper discusses business strategies enabled by analytics and provides examples of analytic innovation that insurers can introduce into their business processes.
Using analytics to optimize business performance
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More than ever, insurance companies need to optimize their business processes. But what does this mean in practice? At SAS, we believe that the "optimized insurer" or "analytical insurer" is one that can integrate analytics into its daily business processes to gain competitive advantage by reducing operational expenses, increasing premium revenue and ensuring regulatory compliance. This white paper will discuss how Property & Casualty (P&C) insurers are embracing analytics throughout their organizations to increase their operational efficiency, while minimizing losses and maximizing profits.
Transform data from existing systems into predictive insights that dramatically increase effectiveness, efficiency and revenue
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Natural resources agencies in the Americas must understand how to enhance the effectiveness of programs and policies while maintaining an adequate level of funding. This white paper describes how analytics can provide optimized solutions to these challenges. The paper first describes the use of analytics in resource management, education and CRM, operations and performance management. Ultimately, you will learn about SAS' analytic capabilities for natural resource agencies.
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This research brief from IIA (International Institute for Analytics) discusses how to bring value to your organization. Written from the viewpoint of a CIO, learn how and why business intelligence and analytical tools are a CIOs best friend, how implementing a decision-support approach can truly transform your business, and one of the most important questions you can ask executive team members and their staff when implementing new IT systems. 
New Visual and Wizard-Driven Paradigms for Exploring Data and Developing Analytic Workflows
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Analytic expertise is in short supply in most organizations. Quantitative specialists carry a heavy workload and need to focus on the most critical business issues. As a result, other decisions that may be important or influential to the organization—but not deemed critical—are made based on suboptimal information. But what if the power and sophistication of analytics could be made simpler, faster, and more intuitive and repeatable than ever? What would that do for the quality and timeliness of your organization's decision making? That was the topic of a SAS webinar in the Applying Business Analytics series. This paper provides a summary of that webinar and describes new capabilities for visual data exploration, visual programming of structured and unstructured data, and wizard-driven shortcuts for rapid model development.
How the healthcare industry will uncover the real value of electronic medical records and the emerging electronic health record (EHR) initiative
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This white paper describes ways the healthcare industry can use the data at its disposal to evolve toward more personalized medicine. In particular, you will learn about the promising possibilities of electronic medical records and powerful analytic solutions for healthcare providers from SAS. The paper also includes some real-life situations in which SAS analytics are being used to improve patient care and research.
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An entire industry of academic research and practitioner conferences has arisen around sports analytics. However, practitioner-oriented research on how teams in all sports can be successful in applying analytics is sparse. Analytics thought leader Thomas H. Davenport sought to fill in the gaps with a five-month independent research study that included interviews with more than 30 leading sports organizations and several leading vendor organizations in the sports industry, such as companies that do video analysis and companies that monitor athletes with biometric devices. The findings, published in this International Institute for Analytics research report, cover three major areas: • Analytics in player and game performance. • Business analytics in sports. • Player health and injury analysis.
Better answers faster through analytic technologies
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What if your business could run an analytical process in 90 seconds instead of 176 hours? That's what happened to one company when it switched from using existing hardware and processing paradigms to using in-memory processing with high-performance analytics from SAS and Teradata. This paper shares insights from a webinar that includes a demo of in-memory analytics and case studies from key industries. Learn how this technology can help you solve brand new problems – and solve existing problems faster and more accurately.
An examination of your company's analytic physique
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Anatomy of An Analytic Enterprise by Kimberly Nevala defines what it means to foster an analytics culture by highlighting the critical characteristics of and barriers to success. Based on some real-life success stories, the paper focuses on cultural and organizational dimensions of analytics programs, using the "anatomy" metaphor to highlight foundational solutions and skills.
Exploiting proven data integration and analytics to ensure healthy returns on sales and marketing investments
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Pharmaceutical sales and marketing professionals face a unique set of challenges in their efforts to boost sales enough to recoup staggering development costs. This white paper explores the possibilities of data integration and advanced analytics in marketing and selling pharmaceuticals. The paper also includes a list of key analytical techniques and several industry case studies.
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Predictive maintenance technologies can play a vital role in helping manufacturers achieve higher asset utilization and reliability – and ultimately greater competitiveness. This paper provides the business case for investing in predictive maintenance solutions and how they work to lower maintenance costs and minimize maintenance-related disruptions across operations and what's required to get started with predictive maintenance today.
The architecture building blocks for empowering strategic business decisions within your existing technology environment
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This paper describes five business analytics styles used today and the building blocks required in implementing these styles. Building a business analytics architecture to create a competitive advantage requires multiple styles as the needs of the organization change. Organizations are seeing new opportunities to incorporate business analytics into operational systems and processes.
In-source analytics-driven intelligence to go beyond decile-based targeting
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Most pharmaceutical companies focus their sales and marketing activities on the top-decile prescribing physicians for a given therapeutic area, based on purchased data. But such practices are no longer yielding desired results. This white paper discusses why now is the right time to make sales and marketing decisions based on deeper analytic insights - using predictive modeling - and how organizations that take control over their own physician targeting will get more timely insights, targeting decisions aligned with business issues and real competitive advantage.
Eight metrics that matter
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It's no secret that using analytics to uncover meaningful insights from data is crucial for fact-based decision making. Yet when it comes to making analytics work, not all organizations are equal. The management of information – including the analytics used to transform it – is an evolutionary process, and organizations are at various levels of this evolution. Those wanting to advance analytics to a new level need to understand the analytics activities across their organization, from both an IT and business perspective. Toward that end, an assessment focusing on eight key analytics metrics can be used to identify strengths and areas for improvement in the analytics life cycle. SAS conducted a benchmark survey of more than 400 US companies across all industries to determine each organization's proficiency across the key metrics. The results of this survey are presented in this paper to provide guidance for short- and long-term efforts needed to enhance analytical effectiveness.
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While many areas of government have successfully employed aspects of fact-based decision making, current initiatives to reduce waste and expenses correspond with what business analytics offers organizations. This white paper produced by BusinessWeek looks at how government agencies are struggling to embrace the spirit of initiatives like the President's Management Agenda and how business analytics can give them an anchor on which to operate as they search for operational agility based on deeper insights, better answers and faster reaction times geared toward the future. 
Profiling the Use of Analytical Platforms in User Organizations
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This report examines the rise of big data and the use of analytics to mine that data, especially focusing on the application of analytical platforms in organizations from both a business and technical perspective. Learn how advances in analytical technology and new architecture are changing the way that organizations stage, store and process large volumes of structured and unstructured data.
Dispelling the myths, uncovering the opportunities
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Big data requires new technologies and new management approaches to facilitate fast, continuous decision making. This Harvard Business Review Key Learning Summary describes how organizations can build big data capabilities through the use of Professor Thomas Davenport's DELTTA framework. Readers will learn how to capitalize on the data they already have and how to integrate big data into everyday business operations.
State of the Industry Research Series
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EKN's second annual report, State of the Industry Research Series: Big Data in Retail, provides a benchmark on how retailers are approaching big data as a business strategy, based on a survey of more than 70 retail industry executives about the challenges they faced building deeper insights from massive amounts of data. Learn how retailers can position themselves for competitive advantage with big data and advanced analytics.
Three key technologies for extracting real-time business value from the big data that threatens to overwhelm traditional computing architectures
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How do organizations know what they know? Do they know all they should about their data, and do they know what to do with it? The answers to those questions are being fundamentally reshaped by the concept of big data. Big data is about high-velocity data capture, discovery and analysis to extract meaningful insights from apparent chaos.

In this white paper, Mark Troester of SAS presents three technologies that can help organizations get a handle on big data – and more importantly, enable them to extract real business value from massive data volumes with big data analytics.
Combine SAS® world-class analytic strength with Hadoop’s low-cost, distributed data storage to uncover hidden opportunities
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Want to get even more value from your Hadoop implementation? Hadoop is an open-source software framework for running applications on large clusters of commodity hardware. As a result, it delivers fast processing and the ability to handle virtually limitless concurrent tasks and jobs, making it a remarkably low-cost complement to a traditional enterprise data infrastructure. This white paper presents the SAS portfolio of solutions that enable you to bring the full power of business analytics to Hadoop. These solutions span the entire analytic life cycle – from data management to data exploration, model development and deployment.
A Guide for the Insurance Industry
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In insurance companies, many decisions hinge on finding the right balance between what's best for the customer and what's best for the organization. Data and analytics can shore up the relationship between customer experience and profits, ensuring that decisions remain in balance. An enterprisewide commitment to data and analytics is the key to achieving this balance. This white paper explains how, by putting the building blocks of a strategic analytic culture in place, insurance companies can move from reactive to proactive decision making – resulting in visibility, executive buy-in and competitive advantage.
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At hospitality organizations, many decisions hinge on finding the right balance between what's best for the guest and what's best for the organization. For example, organizations may have to weigh the desire to eliminate customer wait times with goals like reducing labor costs. Data and analytics can shore up the relationship between guest experience and profits, ensuring that decisions remain in balance. An enterprisewide commitment to data and analytics is the key to achieving this balance. By putting the building blocks of a strategic analytic culture in place, hospitality organizations can move from reactive to proactive decision making – resulting in visibility, executive buy-in and competitive advantage.
SAS provides a unified, agile and more effective information infrastructure to support evidence-based decision making across the enterprise
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This white paper discusses some of the key infrastructure challenges that IT faces in meeting the ever-increasing demands for intelligence across their organizations. It provides an overview of how the platform for SAS Business Analytics can help overcome those challenges. It also describes SAS strengths within each of the platform components -- data integration, analytics, and reporting. Most importantly, it outlines how SAS is here to help organizations achieve success through analytic solutions built upon an integrated framework. 
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This paper, based on research by nGenera Corporation, provides answers to six key questions that executives should be asking about how to use business analytics to improve performance and compete successfully -- from "Where should we leverage business analytics?" and "What's the payoff?" to "What kinds of people do we need?" and more.
Navigate the path to success in a changing data landscape
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The health care data landscape is changing: technology advances, regulatory mandates and government incentives have accelerated the move from paper to digital health records. This brings new possibilities for providers and payers to generate deep insights that can streamline clinical workflows, optimize care, strengthen doctor-patient relationships, cut costs and improve outcomes. To gain these insights, you need analytics solutions and robust infrastructures that can handle the volume, variety, and velocity of big data and generate results rapidly. SAS and Intel are proving that queries that used to take days now take minutes. Learn how to get started and discover proven success stories.
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When deciding which games to offer or replace, casinos may look at historic results and assume that games that were popular in the past will continue to be so in the future. But optimizing the slot floor is much more complex than just assuming the future will look like the past and patron behavior will remain the same.

This conclusions paper presents highlights of a SAS webinar showing how a unique combination of data profiling, statistical forecasting and optimization can improve slot floor performance, utilization and patron satisfaction.
Accurately identify and prioritize tax returns that have a high likelihood of underreporting and a high magnitude of potential collections
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More sophisticated state government revenue departments are accurately identifying and prioritizing tax returns that have a high likelihood of under-reporting and a high magnitude of potential collection by augmenting their rules-based tax auditing systems with scores generated by analytical modeling – improving audit success by as much as 20 to 50 percent. This paper describes the benefits of advanced, non-linear modeling over traditional rules-based approaches, for more precision and better rank ordering, fewer false positives, higher revenues and faster ROI.
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One of the most powerful ways to use data mining and predictive analytics is to apply analytic models and results on a production scale. These models can be applied to help make critical business decisions, or improve decisions. This research brief from IIA (International Institute for Analytics) discusses the technology and organizational challenges businesses must address in order to succeed in using analytics this way, specifically, organizational and attitudinal differences, compliance, time to deploy and data consistency differences. 
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This report examines how data visualization can help organizations of all sizes unleash the full value of information, and outlines key considerations to guide the solution evaluation process. Readers can learn how to enhance their existing reporting and analytics tools, as well as move to more sophisticated analytics solutions.
Case studies in reducing warranty costs
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Globally manufacturers spend more than $70 billion to cover warranty expenses each year, but that is only the beginning of the true costs associated with poor product quality and suspect warranty claims. Increased government scrutiny, tarnished brand image, reduced stock prices, dissatisfied customers and lost sales can quickly dwarf the direct monetary losses. To reduce claim costs and increase customer satisfaction, forward-thinking manufacturers are applying automated analytics across the warranty chain. Detecting and preventing fraud, finding emerging quality problems sooner and accelerating the problem-solving process can reduce warranty costs by more than 20 percent while keeping customers happy. This paper shows how some companies are applying analytics at multiple points across the warranty timeline and the results they have achieved in both issue and fraud detection.
The last-click paradigm erodes as marketers turn to fractional attribution
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Digital attribution — the measurement of the value of each digital marketing contact that contributed to a desired outcome — allows marketers to more clearly understand what's working and what's not. This Forrester Consulting Thought Leadership Paper highlights the state of digital attribution, its various approaches, and its benefits and challenges for marketers and publishers. Commissioned by the Interactive Advertising Bureau, this paper is based on interviews with 15 agencies, service providers and publishers.
Insights from a webcast sponsored by SAS and Casino Journal
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Even for a brand that is marginally recognized, the volume of comments coming from social media and other online sources can reach up to a terabyte a day. Add to that tens of thousands of call center records, guest surveys, emails and other internal data, and you have a rich view into customer opinion. How can hotels and casinos manage, analyze and use this fast-moving flood of unstructured text to make better business decisions?

That was the subject of a webcast co-sponsored by SAS and Casino Journal. This paper summarizes the discussion about how natural language processing and text analytics work to help you classify and understand what is in those text records and what is being said about you and your competitors on the Internet.
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This white paper reviews a portion of a research program conducted by BusinessWeek Research Services designed to understand how companies can optimize business analytics to improve fact-based decision making and to determine the attitudes and opinions of C-level executives with regard to the use and value of business analytics. It is part of a series of white papers for C-level executives intended to facilitate sharing the most important insights from the research. 
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It all started with direct mail. Today, we connect with customers online and offline, and the information businesses collect has increased exponentially. The volume, velocity, variety and complexity of all this information has created the era of what experts call “big data.” Predictive analytics help harness this influx of information and use it to create a competitive advantage. This paper defines predictive analytics, then details ways this type of analytics can be applied to marketing, risk, operations and more. It also includes information relevant to a wide variety of industries – from manufacturing to hospitals.
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Amid the current climate of greater demand for both environmental awareness and corporate accountability, organizations are finding that success is increasingly being measured not only by financial performance, but also by ecological and social accomplishments as well. In addition, the current economic climate has reinforced the need to plan for long-term success. This white paper, based on a launched research program conducted by BusinessWeek Research Services, looks at how the most forward-thinking enterprises are using analytics to their advantage by applying it to sustainability.
Insights from a webinar with Electric Light & Power
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The new compositions of source power, meter infrastructure and grid design, and regulatory requirements place increased pressure on utilities' planning and execution capabilities. With the proper application of business analytics, a set of technology solutions that optimizes the energy portfolio, utilities can convert their challenges into opportunities. For best results, utilities should consider four key areas of business analytics: • Advanced forecasting. • Data management. • Optimization. • Energy commodity risk aggregation and analysis.
What makes companies that are great at analytics different from everyone else.
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Based on a survey of 2,500 respondents in two dozen industries, this research report from MIT Sloan Management Review and SAS examines the characteristics, beliefs and practices of analytical innovators – and why these organizations are different from others. In addition, it offers a framework that shows how companies – regardless of their current analytical sophistication – can move toward analytical innovation.
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SAS and Accenture recently conducted market research with business leaders in North America to explore the effectiveness of their company's business analytics. This paper illuminates the findings from that study, segments the respondents by analytic ROI and offers three important components for a successful analytics strategy.
A Health + Human Services Special Report
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The ACA has forced everyone to re-think what is possible in every corner of HHS operations and has often brought unlikely partners together to find new solutions, create new technologies and lay the groundwork for a better future. By working together, HHS leaders have crafted creative ways to address Medicaid reform, data sharing, eligibility integration, and fraud, waste and abuse reform. This special report in conjunction with the Governing Institute and the Center for Digital Government, addresses the continuing effort to update or replace outdated and outmoded technology with powerful new systems designed to help everyone work smarter and more efficiently.
Finding new ways to add value and insights to big data
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How can we make use of all the data? That is the question the DOD is asking as it strives to find new and better ways to make use of the vast amounts of information it collects from ERP systems, vehicle diagnostics, the frontline and more. The military has a long history of using predictive analytics in its decision making. But with future budget cuts looming, it's looking for new ways to use that technology to reduce costs and operate more efficiently. One of the answers may be to train military leaders how to become complete analysts – analysts who understand not just how to collect data in a way that's useful for analytics, but also how to apply analytics to gain deeper insights than ever before.
Increasing your information turnaround speed exponentially while achieving vastly superior data integrity and accurate results
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Until now, most companies have found it difficult to deliver the reliable, accurate and near real-time insight required to support critical decisions. This paper explains how you can use in-database analytics to not only accelerate your time to insight, but also gain greater confidence in the accuracy of your decisions and maintain the security and integrity of your data.
Featuring Net Lift Modeling and Survival Data Mining
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The key to making the best decisions is to stay on top of the latest techniques in the field of data mining and predictive analytics.  This conclusions paper summarizes a webinar that explained the business value of two techniques that are now prebuilt nodes in SAS Enterprise Miner 7.1 – survival data mining and net lift modeling – and gave hands-on demonstrations of these techniques in action for two use cases.
The Analytical Center of Excellence
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To truly exploit analytics enterprisewide for a competitive edge, an organization must have a centralized group that provides core expertise, supports users, enforces standards and drives performance. The author dubs this group an "analytical center of excellence" (ACE). After laying out his recommended ACE infrastructure, the author prepares you to engage your organization in establishing an ACE. He describes three primary phases of infrastructure, the different levels of enterprise analytical maturity that determine ACE requirements, and the analytical maturity assessment that must occur in order to develop an implementation plan.
Gleaning insight from unstructured big data
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Text analytics uses linguistic rules and statistical methods to automatically assess, analyze and act on the insight buried in electronic text – such as content from social media, call center logs, survey data, emails, loan applications, service notes, and insurance or warranty claims. This paper summarizes a webinar in the Applying Business Analytics Webinar Series where presenters described text analytics tools and process – and shared their experiences using text analytics in four real-world cases in government, public policy, insurance and media.
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Current trends justify utilities gaining every cash flow improvement option available, including setting a framework for the use of smart meter data to improve collections. By identifying and predicting the conditions when a customer may have trouble paying their bills and then developing plans for helping those customers using the tremendous data already present, utilities can create advanced, customized bill payment plans. This white paper describes how utilities can use predictive analytics to optimize their bad debt collections, with inherent business value that is quantifiable and often significant.
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This paper reviews a portion of the research done by BusinessWeek Research Services to determine the attitudes and opinions of C-level executives with regard to the use and value of business analytics and provides analysis and insights on the topic of business analytics.
Analytics, Decision Making & Teams That Do It Right
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Most organizations still have a way to go in relying less on gut feel and becoming more data-driven. Through the webinar summarized in this paper, based on a survey of executives by Harvard Business Review Analytic Services, the panelists – including Professor Thomas Davenport – argue that while barriers still remain to becoming more data driven, these barriers are surmountable. Most organizations see the value of analytics and have embarked on the journey to become more analytical by using data to make decisions.
Leveraging the SAS Business Analytics Framework to accelerate implementations and minimize risk
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This white paper discusses the drivers and the challenges in implementing a longitudinal data system. It then explores how, with SAS' Business Analytics Framework, information gaps among key educational agencies need no longer exist, and decision makers can be armed with the accurate data they need to make proactive decisions and effective education policies.
Tips for establishing an analytic center of excellence
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For organizations to make the most of their data assets and create value, they need to consider how analytic talent is deployed and equipped to drive continuous improvements. As a result, many organizations have begun enterprise initiatives that focus on applying analytics to set future directions, survive in troubled economic times and identify opportunities that set them apart from competitors. But these initiatives need more than just technology. An enterprise strategy is required to coordinate and align key players. Analytic centers of excellence (CoEs) can do just that. This paper provides a comprehensive overview of the need, structure and benefits of establishing and analytic center of excellence. It discusses best practices and explores the various organizational aspects that should be considered to help effectively deploy analytics across your organization.
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Competitive pressures have made it imperative for organizations to master analytics. But many analytics teams are tied up in managing production analytic processes, so they lack a framework for ensuring process quality and efficiency. How can such teams quickly identify, prioritize and fix problem areas – and free up critical analytics capacity? The answer is Lean Six Sigma, which is the marriage of two improvement methodologies, Lean and Six Sigma. As this paper illustrates, many analytics teams have gone "lean" to effectively and efficiently manage all the people, processes, data and technology involved in taking their insights from conception to deployment.
Lessons from effective analytics users
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A global survey of 930 business executives was conducted by Bloomberg Businessweek Research Services in 2011 to determine the current state of business analytics in the organization. While the majority of companies surveyed reported reliance on analytics in day-to-day decision making, only one in four deemed its use of analytics “very effective.” However, the survey also revealed behaviors or characteristics common among the most effective users of analytics. These users have developed pro-analytics practices, have built a business infrastructure to support analytics, and have cultivated a company culture that emphasizes analytics and encourages its use.
From Data to Decision
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Analytical models are at the heart of critical business decisions – for finding new opportunities or managing uncertainty and risks – so they must be treated as high-value organizational assets. A "predictive analytics factory" approach provides a robust framework for managing models for optimal performance throughout the life cycle, from data preparation and model development to model testing, validation, deployment, monitoring and recalibration.
Predictive Analytics
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As a worldwide leader in vacation exchange, RCI Group – a division of Wyndham Exchange and Rentals – faces complex business issues, including inventory that can be highly variable and potential demand that is constrained. The company uses predictive analytics to improve the value of its network, make more effective revenue management decisions in less time, and deliver a better customer experience. RCI Group talked about how advanced analytics have helped improve the efficiency of matching up demand and supply – which has had tangible effects on the value of their network and its transactions – in a webinar in the SAS Applying Business Analytics Series. This paper presents insights from that webinar.
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This white paper illustrates a new patent-pending approach that may be helpful in certain new product forecasting situations.  It combines human judgment with time series mining and statistical modeling.  This "structured analogy" approach helps automate the selection of analogous products ("like items"), facilitates review and clustering of past new product introductions, and generate statistical forecasts.  Users can make manual overrides to the statistical forecasts, and get a better sense of the risks and uncertainties in new product forecasts through visualization of past new product introductions.
The True State of Predictive Modeling in Insurance
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Insurers are seeking new insights to gain a competitive advantage. While there are still many opportunities to improve operations using traditional business intelligence, the focus is shifting to advanced analytics. This white paper provides specific details about the latest research from Strategy Meets Action (SMA) that polled life and P&C insurers to learn the true state of predictive modeling in insurance. You'll learn exactly how insurers are currently developing and managing analytics models, which business problems are being addressed and what plans to expand analytics are expected in the future.
How SAS for Patron Value Optimization helps you glean competitive advantage from deeper patron insight
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Casinos are no longer strictly gaming establishments, but offer a wide range of other entertainment options like restaurants, spa, golf, theater and shopping. Patrons have more choices than ever before, and more incentive to "shop around" for the organization that best matches their desires. Gaming companies need to match patrons' increasingly selective spending habits with highly targeted offers that demonstrate an understanding of preferences and value. To ensure these offers strike the right financial balance to drive profits, gaming companies must base their campaigns on a true understanding of patron worth, both today and in the future. Find out how SAS can provide a 360-degree view of the patron, which is essential to achieving this vision.
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What issues are keeping industry executives up at night? What will it take to keep motivating guests in 2012 and beyond? What research is changing the face of the hospitality and gaming industries? How will the global economy shape the future of gaming and hospitality?

These questions have been nagging at hospitality and gaming management professionals for years. In a webcast co-sponsored by the Cornell University Center for Hospitality Research (CHR) and SAS, our panel of industry executives, thought leaders and researchers discussed five top trends for 2012 and what hospitality organizations should do about them.
How analytics can help overcome challenges facing the Canadian healthcare system
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Canada offers a basic foundation of universal-access healthcare. However, the demand for complex-system analysis has never been greater to assist healthcare management professionals, at all levels, to provide evidence-based, forward-looking healthcare delivery. This paper argues that health authorities that adopt an analytics-based approach to executing their mandates are better positioned to deliver needs-based, quality care today and to anticipate and meet needs that are likely to arise tomorrow.
Enterprise Drives Customer Value to the Next Level
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Who are your best customers? How can you make good customers better? Who should you try to lure away from the competition? Once you win them over, how can you secure their loyalty? Which customers are likely to defect, and how can you prevent that? Those are perennial questions for most any organization. The answers can be elusive if your customer base includes millions of individual and business accounts, more than a million transactions a week, and the largest fleet of passenger vehicles in the world. This paper describes how Enterprise Holdings, the world's largest rental car company, continually seeks to improve its value to customers and in turn, customers' value to the company.
By Stephen Few, Perceptual Edge
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What-if scenarios that predict what might happen given different business conditions and decisions are most enlightening when we understand the relationships between the variables that influence potential results. Data visualization expert Stephen Few describes the characteristics of good visual analytics and describes how to use the JMP Prediction Profiler to build predictive business models and interact with data and graphs to observe how changes in one variable influence changes in the others.
Transforming the insurance claims life cycle using analytics
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Claims are by far the biggest expense for insurance companies. Claims payouts and loss-adjustment expenses can account for up to 80 percent of an insurance company's revenue. The way an insurance company manages the claims process is fundamental to its profits and long-term sustainability. And the role claims processing plays in customer satisfaction, renewal and retention is just as important. Predictive insurance claims processing, or claims analytics, helps insurers make the right decision, at the right time, for the right party – helping them to differentiate themselves from competitors, achieve measurable ROI and enjoy intangible benefits like better customer satisfaction.
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This IDC paper, sponsored by Platform Computing and SAS, focuses on the value of deploying business analytics solutions on grid computing platforms. It discusses high-performance computing environments (evolution is moving from clusters to grids to cloud computing), the reasons for choosing business analytics software on grid computing platforms and the benefits achieved by three organizations. These case studies illustrate how SAS Business Analytics and grid computing technologies can enable competitive differentiation, even with increasing data volumes, challenging and ever-changing decision-support requirements, and pressure on IT departments to do more with less.
SAS delivers solution building blocks for empowering strategic business decisions within your existing technology environment
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This white paper describes how SAS provides a single framework to meet all business analytics requirements, without the need to continually install new components. The SAS architecture provides a core set of capabilities that work together out of the box and can be easily extended to add more functionality or integrated with other parts of an IT infrastructure.
How analytics can transform masses of data into competitive differentiation
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The benefits of subscriber data management (SDM) techniques are relatively well known, but providers could significantly extend the value of SDM by adding a layer of analytics. Analytics can bridge the gaps between the telco and IT domains in a service provider's data architecture to create new insights based on a more comprehensive view. In this white paper, Ken King of SAS discusses six key ways service providers can use analytics to develop more enduring and profitable customer relationships.
Maximizing Recovery for the Betterment of State Citizenry
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For state governments, the American Recovery and Reinvestment Act (ARRA) is creating unprecedented management challenges in reporting, transparency and accountability. To meet the President's five crucial objectives for the stimulus funding, governors, state budget officers, controllers and stimulus czars can apply a business analytics approach to managing grants; SAS for recovery optimization and management for state governments provides data integration, reporting and advanced analytics that can be quickly deployed to complement existing grants management systems with minimum disruption.
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Statistical intervals can be confusing, even in the minds of those who use them often. This paper uses an easy-to-understand manufacturing example to describe the differences between confidence, prediction and tolerance (enclosure) intervals. The author provides formulas plus the simple steps for implementing each interval type using JMP menus.
How to identify high-value opportunities for embedding analytics into your business processes
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Organizations are getting much more interested in how analytical decisions can be embedded into everyday business processes. But where do you start? This white paper, based on research by nGenera Corporation, provides practical advice on how businesses can identify the best opportunities for making their processes more analytical, and how to assess whether a proposed business analytics application will succeed. 
A Strategic Approach to Creating Significant Economic Value
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Economic conditions are reinforcing the mandate for tighter, more demand-driven supply chains.  Supply chain executives are searching for new value-add and cost reduction vehicles. In this paper, experts from SAS and HAVI Global Solutions argue that because demand management has become such a critical tool for carving out economic value, companies whose core competency is not supply chain management should outsource their demand management functions. The authors explain the benefits, discuss the conditions and technologies that have converged to make those benefits significantly outweigh the risks, and provide tips on assessing if outsourcing demand management is the right strategy for your organization.
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Are standard silver-gold-platinum customer loyalty programs doing all they could?  Are daily deals such as Groupon discounts helping or hurting restaurants?  How can an organization benchmark its IT maturity and develop a logical roadmap for evolution? These questions have been nagging at hospitality and gaming management professionals for years.  In this webcast conclusions paper, experts and researchers from the Cornell University Center for Hospitality Research present their findings on these hot issues that could spell the difference between an organization thriving or simply surviving.
Transforming the way health plans do business
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In the wake of health care reform, health insurance plans face tremendous challenges and opportunities, many of which may require them to transform the way they do business. Ultimately, the future of individual health plans – and potentially the US health insurance industry as a whole – will be determined by how nimbly and effectively they are able to address these challenges.  Recent research conducted by Stonegate Advisors LLC and sponsored by SAS explored how advanced analytics can help health plans address emerging business needs. This paper summarizes the results of that research.
Insights from a webinar in the SAS Applying Business Analytics Webcast Series
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The numeric information one typically thinks of as “data” represents only the tip of the data iceberg.  Anywhere from 70 percent to 85 percent of an organization’s available data is unstructured data – freeform text. It is captured from websites, call center records, online reviews, social media, research archives, clinical notes and more. Imagine what an organization could gain with the ability to automatically categorize that content, identify meaningful patterns in it and use it to enrich all forms of business analysis. This paper summarizes a webinar in the Applying Business Analytics series, where Denise Bedford of Kent State University and Fiona McNeill of SAS described the emerging science of text analytics and presented some real-world examples of text analytics in action.
Develop your untapped reserves of unstructured data for health, safety and environmental improvements
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This paper illustrates how analytic-driven reporting systems and text mining software can help improve the safety of workers and mining processes in the oil and gas industries. Analytic-driven reporting systems and text mining can  identify quickly and accurately the key metrics that are captured from accident and hazard reports. This can shorten review cycles by automating the manual tasks of reading detailed comment blocks and textual fields, and help discover factors that may have been overlooked in the pursuit of safety improvements.
As analytics becomes a common path to business value, many companies are changing how they make decisions, operate and strategize
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Based on interviews with more than 90 executives, this research paper examines how organizations can step up their analytics game by rethinking their organizational culture. Readers will learn how leading companies have improved their decision making with analytics, as well as actions they can take to develop a stronger analytics-based culture.
How data management and analytics can help reinsurers
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Insurance is a tough marketplace, but in many respects reinsurance is much tougher in its unprecedented number of challenges – be they natural disasters, regulatory issues, a continuing global soft market, and legacy issues such as exposure to mold and asbestos claims. To combat these challenges, reinsurers are turning to technology for catastrophe modeling, data analytics and geospatial information systems (GIS) to better understand the data and their risk exposure. This white paper explains how reinsurers can gain a competitive advantage by using data management and analytics. 
Using Big Data and Analytics as a Ticket to Strategic Relevance
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This white paper describes the results of a study conducted by IDC that examines the role that the CIO and the IT group play in enabling, supporting, and leading analytics programs and projects within their organizations. Insights about analytics strategy and investment drivers, analytics culture, and outcomes of analytics projects are presented along with recommendations for CIO/IT organizations.
What is the future of SAS® Business Analytics from a technology perspective?
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This paper describes the future of SAS® Business Analytics from a technology perspective. During the next few years, SAS will continue driving innovation and delivering new capabilities, technologies and solutions. At the same time, SAS will adapt to the ever-changing landscape and organizational requirements of customers and the marketplace. This paper outlines some, but not all, areas where SAS will focus in the next few years to build on today's solid base.
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This article reviews a portion of the research done by BusinessWeek Research Services to determine the attitudes and opinions of C-level executives with regard to the use and value of business analytics for gaining insight into customers' motivations and behaviors. 
Advance warning and problem avoidance in complex industrial processes
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By examining a project conducted with ConocoPhilips to find early indicators of problems in complex industrial processes, this paper presents an innovative statistical method to aid in diagnosing situations and solving problems, leading to the result of decreasing costs and increasing productivity through predictive maintenance.
Key Research Findings
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The health care landscape continues to rapidly evolve, fueled by change from reform and the unstable economic environment. Health insurance plans face new challenges, seeking to develop go-to-market strategies that define their future position and answer critical questions surrounding growth, profitability and sustainability. This white paper summarizes key findings from a series of interviews with 40 influential marketing, sales, medical and IT executives from leading health insurance plans across the country.
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Imagine the benefits if your organization was certain that the performance factors and variables it monitored were actually the right ones – the ones that make a difference to financial success. These benefits are offered by analytical performance management, a quantitative approach to understanding and predicting performance that is a real possibility today for many firms.

In this research report, the concept of analytical performance management is described both in theory and in practice. The research provides insight into leading practices in analytical performance management and barriers to achieving it.   
Prognosis Positive
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Government health care industry faces a wide variety of challenges – from budget troubles to congressional scrutiny. Plus, the industry is moving towards adopting electronic health records, meaningful use standards and health information exchanges. Managing all this change doesn't mean the quality of care can drop. This paper provides an in-depth report on the status of analytics in the government health care industry, along with an overview of key obstacles to adoption, plus big opportunities in store for those organizations that use analytics to its fullest.
The hottest trends and biggest issues for the hospitality and gaming industries
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Amid inconsistent indicators – roller-coaster stock values and high unemployment rates – hotel and casino operators are rightfully wondering, when will recovery really take hold? How will the hard lessons from lean economic times shape the industry in coming years? What will it take to restore rates, occupancy and revenues to pre-recession levels? How can we wisely apply new technologies to address these business issues? These questions were the focus of a webcast sponsored by the Cornell University Center for Hospitality Research and SAS. This paper provides a summary of that webcast.
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Education leaders know it is no longer enough to collect data just to deliver mandated reports. It is time to use diverse data sources to make better, fact-based decisions that improve research, enrollment and administration. The field of education has only scratched the surface of what can be done with analytics and business intelligence, said Dr. Susan Grajek from the non-profit organization EDUCAUSE. Speaking at the Education brunch and discussion at the 2012 AT&T Pebble Beach National Pro-Am in California, Grajek described the cultural and policy changes that must take place for P-20 educational institutions to capitalize on analytics, while Dr. Sean Mulvenon of the University of Arkansas showed how his team has used SAS to implement data integration, analytics and reporting projects for P-20 educational statistics for the local, state and national levels.
Increasing the business impact of customer insights and analytics
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The Translation Layer is the first of a three-paper series titled Increasing the Business Impact of Customer Insights and Analytics. The translation layer is defined as the role that analytical people play, or ought to play, within organizations to bridge the gap between information and powerful business applications. This paper illustrates why the analytics community must evolve to increase the impact they have on the organizations where they work.
Practical advice for applying advanced analytics in hospitality and gaming.
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This paper presents insights and practical advice from a webcast on applying advanced analytics in hospitality and gaming. It describes where advanced analytics can have real value in the industry from forecasting and simulation to optimizing results by choosing the right path.
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This paper by James Taylor, CEO of Decision Management Solutions, discusses how to focus on decisions to ensure the right problems get solved and what kind of analytic technologies can help achieve this.   Organizations are increasingly adopting predictive analytics. Many use thousands of analytic models each day in real-time decision making and in operational, production systems. However, many analytic teams rely on approaches and tools that do not scale to the level needed. These teams need a repeatable, efficient process for creating and deploying predictive analytic models into production. They must operationalize analytics. This paper discusses the three elements needed to operationalize analytics: a collaborative environment for problem definition, a repeatable, large-scale process for developing analytic models, and a reliable architecture for deploying and managing models in production systems.
SAS provides solutions to streamline deployment
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Organizations spend a significant amount of time operationalizing models. But, the less time you have to spend on model deployment issues, the more time you will have to address critical organizational challenges. SAS has accelerated data mining model deployment throughout the analytical life cycle by providing key integration points across SAS solutions, enabling customers to reap immediate benefits, including reduced data movement, increased productivity of analytic and IT teams, and faster time to results. This white paper will help readers understand the analytical modeling life cycle and who in their organization is involved in each step. It also shows how SAS can simplify modeling operations with interfaces for data preparation; model building, registration, validation and promotion; automated model deployment and scoring; and monitoring of results.
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Along with the financial crisis of 2009 comes an opportunity for funding through the federal stimulus package. This white paper explores the foundation for education's successful future by outlining a model for sustainable education. It also details four key areas (instructional methods, campus operations, workforce development and infrastructure) essential to reshaping the US educational system in response to this crisis and in preparation for a bright future. Tomorrow is today, and extraordinary things are about to happen. Let's get started!
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Understanding customer comments is a hot topic in the text mining world, and mining audio data is gathering momentum. Combining voice capture data with business intelligence, analytics and text mining provides valuable customer intelligence for marketing and competitive intelligence. This paper helps you understand how to take advantage of analytical technologies that combine data mining methods with emerging linguistic techniques to find patterns and meaning in the words captured in conversations and documents. The case study presented in this paper is based on MSNTV call center audio data. A sample of more than 10,000 individual audio files and their associated transcriptions were used to understand customer issues and the likelihood that customers would cancel their MSNTV subscriptions.
Achieve better business results through faster, more accurate decisions
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Assessing the risk of a loan applicant, detecting fraud before the close of a transaction, and making live, customer-specific offers are just a few business scenarios that require secure, accurate and near real-time analytical insight. This paper explains how in-database analytics solutions jointly offered by SAS and Teradata can accelerate your time to insight, as well as increase the accuracy of your decisions and the security of your data. The paper concludes with four real-world accounts of companies that have achieved competitive advantage by implementing in-database analytics.
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Every small and midsize business (SMB) is looking for ways to gain competitive advantage, improve productivity – and ultimately increase revenue. But as explored in this paper, many don’t realize that their data is a huge asset that can be used to help them achieve their goals. The key is implementing the right analytics software. In this paper, learn how innovative companies like PSKW, a leading pharmaceutical couponing business, are using SAS analytics to turn vast amounts of enterprise data into valuable insights – and using these insights to differentiate their services and grow their business. Explore lessons learned and best practices that can help other SMBs leverage analytics as a game changer. 
An Innovation in Time-Series Analysis
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By putting data in motion, people can spot trends and see details they might otherwise miss. That's a guiding premise of this white paper from data visualization expert Stephen Few. Find out how interactive graphs lead to valuable analytical insights, illustrating not just the degree of change from one point in time to the next, but also the shape, velocity and direction of change.
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Data Mining

Optimize Your Network Investments & Transform Customer Experience
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For communications service providers, delivering a good customer experience is paramount. And good network performance is one of the best ways to keep customers happy. But most service providers struggle to get insight into their own network data and put it to strategic use. Heavy Reading reveals how predictive analytics gives service providers real-time visibility into their networks – boosting revenue potential and ensuring more satisfied customers, better targeted marketing, fine-tuned capacity planning and top-notch service assurance.
The promise and challenges of implementing Radio Frequency Identification (RFID) systems across the extended supply chain
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Radio frequency identification (RFID) is spreading quickly through the global network of supply chains. The technology promises improved efficiency and more accurate tracking--along with untold amounts of data. Read this white paper to get a brief introduction to RFID and learn about a variety of SAS technologies that can help you harness the value of your RFID-related data. Such technologies include data quality, business intelligence, predictive analytics and even retail-specific applications.
A SAS Best Practices Paper
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This paper illlustrates how Credit Scoring for SAS Enterprise Miner software is used to build credit scoring models for the retail credit industry. It discusses the benefits of performing credit scoring and the advantages of building credit scoring models in-house using SAS Enterprise Miner. It goes on to discuss the advantages and disadvantages of three important model types: the scorecard, the decision tree and the neural network. Finally, it presents a case study where an application scoring model is built with SAS Enterprise Miner, beginning with reading the development sample, through classing and selecting characteristics, fitting a regression model, calculating score points, assessing scorecard quality (in comparison to a decision tree model built on the same sample) and going through a reject inference process to arrive at a model for scoring the new customer applicant population.
Best practices for government agencies.
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Read this white paper to learn how SAS helps governments reduce the taxpayer burden for fraud, error and abuse, ultimately allowing governments to better fund efforts for improving the lives of citizens. This paper focuses on three key areas: improper payments, purchase card fraud and Medicare/Medicaid fraud. The paper discusses how governments can develop anti-fraud strategies to improve collection rates and reduce improper payments, fraud, waste and abuse.
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The ability to make effective, fact-based decisions is not based on how much data you have. In fact, most organizations are awash in too much data. Consequently, organizations that can discover meaningful and predictive insights from all the data they capture are the ones most likely to succeed. This paper shows how you can use predictive analytics and data mining to reveal new insights from your data and achieve competitive advantage.
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Data mining is past the hype stage and has proven that it can produce significant bottom-line results. This paper discusses the many measurable benefits that data mining delivers, including solving complex needle in the haystack problems, eliminating the bad (such as fraud), pinpointing the good (opportunities), streamlining decision processes, and used on qualitative data. Learn how organizations are using data mining to solve their problems, including a $1 billion decision that produced positive results. 
Applying data mining, predictive modeling and real-time analytics in oil and gas operations
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Mining large reservoirs of data in oil and gas operations involves committing to key processes and technologies – and embracing new ways of thinking about problem solving. To extract value from vast data stores and change the way decisions are made, many operators have turned to advanced data mining techniques along with real-time analytical and data processing capabilities. This paper explores practical approaches, workflows and techniques that are used in oil and gas operations. It also examines the role of exploratory data analysis; model development and modeling techniques; and approaches to putting models into production.
Insights from a webcast sponsored by SAS and Casino Journal
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Even for a brand that is marginally recognized, the volume of comments coming from social media and other online sources can reach up to a terabyte a day. Add to that tens of thousands of call center records, guest surveys, emails and other internal data, and you have a rich view into customer opinion. How can hotels and casinos manage, analyze and use this fast-moving flood of unstructured text to make better business decisions?

That was the subject of a webcast co-sponsored by SAS and Casino Journal. This paper summarizes the discussion about how natural language processing and text analytics work to help you classify and understand what is in those text records and what is being said about you and your competitors on the Internet.
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This white paper outlines the flexible architecture of SAS Enterprise Miner. It shows how the SAS architecture enables users to create data mining projects for interactive or batch execution and share projects with other business users and decision makers across the organization.  
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This white paper illustrates a new patent-pending approach that may be helpful in certain new product forecasting situations.  It combines human judgment with time series mining and statistical modeling.  This "structured analogy" approach helps automate the selection of analogous products ("like items"), facilitates review and clustering of past new product introductions, and generate statistical forecasts.  Users can make manual overrides to the statistical forecasts, and get a better sense of the risks and uncertainties in new product forecasts through visualization of past new product introductions.
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This paper by James Taylor, CEO of Decision Management Solutions, discusses how to focus on decisions to ensure the right problems get solved and what kind of analytic technologies can help achieve this.   Organizations are increasingly adopting predictive analytics. Many use thousands of analytic models each day in real-time decision making and in operational, production systems. However, many analytic teams rely on approaches and tools that do not scale to the level needed. These teams need a repeatable, efficient process for creating and deploying predictive analytic models into production. They must operationalize analytics. This paper discusses the three elements needed to operationalize analytics: a collaborative environment for problem definition, a repeatable, large-scale process for developing analytic models, and a reliable architecture for deploying and managing models in production systems.
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Data Visualization

State of the Industry Research Series
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EKN's second annual report, State of the Industry Research Series: Big Data in Retail, provides a benchmark on how retailers are approaching big data as a business strategy, based on a survey of more than 70 retail industry executives about the challenges they faced building deeper insights from massive amounts of data. Learn how retailers can position themselves for competitive advantage with big data and advanced analytics.
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This eBook uses a fictional company, ProCorp Bank, to tell a true story about a company with a data problem – and how data visualization can solve it. Complete with a cast of characters, sharp dialogue and a compelling narrative, the book blends together experiences of actual companies to show the challenges and benefits of implementing data visualization.
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Data visualization enables organizations to clearly communicate critical information in the face of increasing data volumes, complexity and sources. Written by SAS thought leader Analise Polsky, this report describes how organizations can use it to bring together dimensions of data from disparate sources and deliver lightning-fast insights. Plus, it lays out seven important considerations when deploying data visualization.
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This report examines how data visualization can help organizations of all sizes unleash the full value of information, and outlines key considerations to guide the solution evaluation process. Readers can learn how to enhance their existing reporting and analytics tools, as well as move to more sophisticated analytics solutions.
From basics to big data with SAS® Visual Analytics
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A picture is worth a thousand words – especially when you are trying to find relationships and understand your data – which could include thousands or even millions of variables. To create meaningful visuals of your data, there are some basic tips and techniques you should consider. Data size and composition play an important role when selecting graphs to represent your data. This paper, filled with graphics and explanations, discusses some of the basic issues concerning data visualization and provides suggestions for addressing those issues. From there, it moves on to the topic of big data and discusses those challenges and potential solutions as well. It also includes a section on SAS® Visual Analytics, software that was created especially for quickly visualizing very large amounts of data. Autocharting and "what does it mean" balloons can help even novice users create and interact with graphics that can help them understand and derive the most value from their data.
A TDWI Checklist Report
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Text files and unstructured data offer insights that can help organizations reduce costs, improve customer relationships and act more quickly and decisively. This TDWI Checklist Report discusses the relevance of text analytics for improving user experiences with the semi-structured and unstructured textual big data now at their disposal.
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Obtaining a holistic view of customers is a classic big data problem that requires you to capture, prepare, manage, integrate and analyze huge amounts of digital data. This paper – based on a webinar hosted by the Direct Marketing Association (DMA) and sponsored by SAS – takes a look at how advancements in analytics, data visualization and big data processing power can help you become more predictive and prescriptive in your digital and integrated marketing efforts.
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This white paper illustrates a new patent-pending approach that may be helpful in certain new product forecasting situations.  It combines human judgment with time series mining and statistical modeling.  This "structured analogy" approach helps automate the selection of analogous products ("like items"), facilitates review and clustering of past new product introductions, and generate statistical forecasts.  Users can make manual overrides to the statistical forecasts, and get a better sense of the risks and uncertainties in new product forecasts through visualization of past new product introductions.
By Stephen Few, Perceptual Edge
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What-if scenarios that predict what might happen given different business conditions and decisions are most enlightening when we understand the relationships between the variables that influence potential results. Data visualization expert Stephen Few describes the characteristics of good visual analytics and describes how to use the JMP Prediction Profiler to build predictive business models and interact with data and graphs to observe how changes in one variable influence changes in the others.
With better visualization tools, big data could actually become useful data
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How can you make big data become useful data? This InformationWeek article, The Data That Matters, describes why simply having the right data isn't enough – you must be able to see it in the right way. Learn why data visualization is the key to delivering meaningful insights from big data – and why companies have no excuse for not taking advantage of the tools and data sources available to them.
Insights from The Premier Business Leadership Series 2013 in Amsterdam, presented by SAS
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The forces of globalisation and technology – particularly the Internet and big data analytics – are reshaping everything about how humans and organisations work. How business leaders define, understand and prepare for this transformation will be central to their success in the coming years. This white paper presents highlights from the Amsterdam edition of the conference series, particularly Lynda Gratton's keynote address on "The Promise of Technology" and two panel discussions featuring leaders of companies that deal with the challenges of big data analytics every day.
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Forecasting

Optimize Your Network Investments & Transform Customer Experience
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For communications service providers, delivering a good customer experience is paramount. And good network performance is one of the best ways to keep customers happy. But most service providers struggle to get insight into their own network data and put it to strategic use. Heavy Reading reveals how predictive analytics gives service providers real-time visibility into their networks – boosting revenue potential and ensuring more satisfied customers, better targeted marketing, fine-tuned capacity planning and top-notch service assurance.
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The ability to anticipate, track and control behavior of a group of accounts established during the same time period (i.e. "vintage") underpins marketing activity and risk management policy at financial institutions. This paper presents a technique that treats the components of the vintage as a set of predictions and forecasts from a cross-vintage data stream. This methodology includes a variety of analytical techniques, including time series analysis, dynamic segmentation and clustering, vintage "profiling" and forecast reconciliation. An integrated approach to vintage curve modeling unifies internal bank drivers, external economic factors and past performance into one cohesive strategy – free from internal biases and more closely aligned with market reality. This methodology can serve to provide critical insight that supplements an institution's sales and operations planning process.
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This paper presents a methodology for conducting a forecasting strategic value assessment for your organization.
Strategies for Demand-Driven Forecasting and Planning
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In today's unstable economy, many organizations are finding out that they have inadequate processes to handle demand planning, and traditional methods of predicting demand aren't efficient in a fluctuating market. The paper makes specific recommendations for organizations striving to move up the demand forecasting maturity curve by providing them an assessment framework to evaluate their current stage, and highlighting the characteristics common to best-class companies.
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SAS Forecasting for Desktop provides organizations with comprehensive forecasting capabilities within the PC environment. The solution enables users to automatically generate forecasts, without any need for manual programming. Equipped with a comprehensive model repository based on a full range on forecasting methods including time series, ARIMA, dynamic regression and UCM, the solution helps users by automatically selecting the appropriate forecasting model and optimizing parameters to generate reliable forecasts. Because it is largely automated, SAS Forecasting for Desktop addresses the needs of novice forecasters, yet still meets the requirements of more experienced analysts by providing layers of sophistication that can be accessed as needed.
Insights from a webinar with Electric Light & Power
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The new compositions of source power, meter infrastructure and grid design, and regulatory requirements place increased pressure on utilities' planning and execution capabilities. With the proper application of business analytics, a set of technology solutions that optimizes the energy portfolio, utilities can convert their challenges into opportunities. For best results, utilities should consider four key areas of business analytics: • Advanced forecasting. • Data management. • Optimization. • Energy commodity risk aggregation and analysis.
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How effectively an organization manages its supply chain depends on many factors. An organization can have excellent business processes, yet lack the ability to successfully align supply with demand. Read this white paper to learn how advanced forecasting technology and business intelligence can enhance Sales and Operations Planning while supporting better communications and collaboration throughout an enterprise.
How to get more accurate forecasts with less cost and effort
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Organizations spend much time and money searching for a magic formula for the perfect forecast, yet still get bad forecasts. But it doesn't have to be this way. This paper provides a summary of a webinar in the SAS “Applying Business Analytics” series originally broadcast in July 2010 that discussed how businesses can achieve better accuracy and forecasting process efficiency by understanding the nature of demand patterns and where forecasting process is adding value — or not.
A Case Study
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This case study describes how SAS helped the Ontario Ministry of Health and Long-Term Care achieve a solution to predict hip and knee replacement demand – enabling the Ministry to solve what had become a crisis in long wait-times for the surgery. Based on powerful SAS® Forecast Server, this solution now provides the forecasts that guide Ontario’s ongoing investment in orthopedic staff, facilities and services.
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The latest findings on four critical issues that could spell the difference between a hospitality organization thriving or simply surviving is presented by Cornell University and SAS experts. Topics include pragmatic advice for optimizing customer reward programs; survey findings about engaging with customers online; some common myths about forecasting, and creative new approaches in the hospitality field.
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This white paper discusses fundamental issues that impact an organization's ability to forecast accurately. These issues include the operational definition of "demand," what to forecast, how to measure performance, organizational practices and demand volatility. Without addressing these issues, the investment may yield no return.
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Whether it's curbing greenhouse gas emissions or increasing the supply of renewable and distributed power generation and storage, today's utilities are under intense environmental and regulatory pressure. To maximize investments in smart grid infrastructure, utilities must have the right combination of forecasting tools and information technology. This paper examines the new challenges affecting forecasters and describes opportunities for harnessing smart grid data to get the most out of forecasting.
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There is no such thing as the perfect forecast. But you can achieve better accuracy and forecasting process efficiency by understanding the nature of your demand patterns and where your forecasting process is adding value – or not. This white paper (based on a webinar in the Applying Business Analytics series), examines how Forecast Value Added (FVA) analysis is being used at major corporations to identify and eliminate waste in the forecasting process, reduce costs and achieve the best forecast possible, given the nature of the patterns or behaviors being forecast.
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This white paper discusses multi-causal analysis as a way of integrating consumer demand information with shipment forecasts to capture the impact of marketing activities on shipments. With improvements in technology, data collection, data storage and analytical knowledge, CPG companies are now looking to integrate consumer demand with their shipment forecasts to capture the impact of marketing activities on shipments. As a result, multi-tiered causal analysis (MTCA) is receiving renewed interest. This paper explains the MTCA process, including an anonymous beverage industry case study that describes the process used to develop and link the Consumer Demand and Factory Shipment models. The by-product of this process was a more accurate forecast that reflected the company's marketing investment strategy.
A demonstrated technique for efficiently producing forecasts for millions of time series
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Web sites and transactional databases collect large quantities of time-stamped data. Businesses often want to make future predictions based on numerous sets of time-stamped data (sets of transactions). The number of time series to forecast, however, may be enormous or the forecasts may need to be updated frequently, making human interaction impractical. This detailed white paper proposes a technique for automating large-scale forecasting using SAS Forecast Server. You will learn about time series data, forecast modeling and statistics of fit. The paper also provides a step-by-step explanation of the automated forecasting technique and a brief discussion of implementation.
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This white paper provides a detailed overview of SAS Forecast Studio, a key component of SAS Forecast Server. The paper walks you through the process of generating automatic forecasts, viewing results, building models, publishing results, reporting and more. Read this paper to learn how SAS Forecast Server speeds the statistical forecasting process by providing a convenient, user-friendly interface for all the forecasting options available in SAS.
Analytic insights for more confident, proactive decision making
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This white paper offers a broad look at the process of managing future uncertainty in business. It first explores the four main aspects of managing the future: forecasting, risk management, decision making and planning. The paper then presents specific SAS solutions, designed for each of these areas, that help organizations manage their future.
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This white paper illustrates a new patent-pending approach that may be helpful in certain new product forecasting situations.  It combines human judgment with time series mining and statistical modeling.  This "structured analogy" approach helps automate the selection of analogous products ("like items"), facilitates review and clustering of past new product introductions, and generate statistical forecasts.  Users can make manual overrides to the statistical forecasts, and get a better sense of the risks and uncertainties in new product forecasts through visualization of past new product introductions.
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In times of economic uncertainty, corporate decision makers need more high quality information to make well-informed decisions. So said the speakers and  delegates at CFO Publishing's recent conference, "Predictive Analytics in Perilous Times." The conference, held in San Francisco in February of 2009, featured leading voices in corporate finance -- including Thomas Redman, president of Navesink Consulting, Arthur Kordon, a leader in the data mining and modeling group at Dow Chemical and Dr. David Friend, chairman of Palladium Group Inc., among others. This conclusions paper presents the highlights of the conference program.
CIO Insight Executive Brief
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Forecasting is essential for small and midsize businesses (SMBs) to meet new demands such as greater competition, changing customer behavior, social media influence and more. Yet many SMBs lack the resources and skills to make the best possible use of forecasting. Instead, they rely on familiar, ineffective tools like spreadsheets, and are hesitant to invest new technologies or strategies. This paper outlines how to find and invest in a statistically sound forecasting solution that reflects the reality of your business.
Maximizing Recovery for the Betterment of State Citizenry
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For state governments, the American Recovery and Reinvestment Act (ARRA) is creating unprecedented management challenges in reporting, transparency and accountability. To meet the President's five crucial objectives for the stimulus funding, governors, state budget officers, controllers and stimulus czars can apply a business analytics approach to managing grants; SAS for recovery optimization and management for state governments provides data integration, reporting and advanced analytics that can be quickly deployed to complement existing grants management systems with minimum disruption.
A Strategic Approach to Creating Significant Economic Value
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Economic conditions are reinforcing the mandate for tighter, more demand-driven supply chains.  Supply chain executives are searching for new value-add and cost reduction vehicles. In this paper, experts from SAS and HAVI Global Solutions argue that because demand management has become such a critical tool for carving out economic value, companies whose core competency is not supply chain management should outsource their demand management functions. The authors explain the benefits, discuss the conditions and technologies that have converged to make those benefits significantly outweigh the risks, and provide tips on assessing if outsourcing demand management is the right strategy for your organization.
Eliminating waste and inefficiency from the forecasting process
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By identifying and eliminating waste in the forecasting process, it is possible to achieve better results with much less effort. This white paper provides simple and practical methods for applying the lean approach to forecasting at your organization. You will learn about data requirements, forecasting performance metrics and setting expectations for accuracy.
Big Data & Utility Analytics for Smart Grid
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Utilities will soon begin adopting analytics technologies that will allow them to become more proactive in decision making and to adjust their strategy based on predictive views of the future. As a result, utilities will be able to capitalize on smart grid technologies, side-step potential problems and better handle the steep challenges facing an industry in transition. Learn more in this excerpt from the December 2012 GTM Research report titled The Soft Grid 2013-2020: Big Data and Utility Analytics for Smart Grid. Research for this report was conducted over a six-month span and included primary and secondary research as well as extensive interviews with industry players and utilities.
Practical advice for applying advanced analytics in hospitality and gaming.
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This paper presents insights and practical advice from a webcast on applying advanced analytics in hospitality and gaming. It describes where advanced analytics can have real value in the industry from forecasting and simulation to optimizing results by choosing the right path.
Accessing SAS® Forecast Server from Microsoft Excel
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This paper shows how to access large-scale automated forecasting and strong analytics through a Microsoft Office interface. SAS Forecast Server can be easily accessed from Microsoft Excel using three new forecasting wizards in SAS Add-In for Microsoft Office (included with SAS Enterprise BI Server). These wizards provide users with an alternative to SAS Forecast Studio to drive and automate the forecasting process.
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Forecasts are never as accurate as you need them to be, and lots of forecasting consultants and software vendors are willing to take your money in exchange for unfulfilled promises. This white paper explores why forecasts are often wrong and some ways to improve them.
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Utility forecasters cannot assume that one methodology will provide the best forecast from one year to the next. To improve forecast performance, reduce uncertainties and generate value in the new data-intensive environment, they must be able to decide which models, or combinations of models, are best. And they must be able to determine more indicators of the factors that affect load. This paper uses a case study to illustrate how utility forecasters can take advantage of hourly or sub-hourly data from millions of smart meters by using new types of forecasting methodologies. It investigates how a number of approaches using geographic hierarchy and weather station data can improve the predictive analytics used to determine future electric usage. It also demonstrates why utilities need to use geographic hierarchies, and why their solutions should allow them to retrain models multiple times each year.
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Organizations rely on good forecasts to make better decisions. But if nobody trusts the forecast, people will try to adjust it, override it or ignore it. Fostering trust is therefore a crucial aspect of forecasting. In this conclusions paper from a SAS/Foresight webinar, Paul Goodwin of the University of Bath explains what it takes to build trust in forecasts, and why so many so-called forecasts don't meet the criteria.
The Mechanics of Forecasting
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There is no shortage of articles, books, consultants and vendors telling you (or selling) their version of forecasting best practices. This white paper takes a different angle. Instead of talking about the so-called "best practices" in forecasting, we will instead expose the seamy underbelly of the forecasting profession. Rather than asking you to implement all the various things that really good forecasting organizations do, we want to help you avoid the really bad forecasting practices that some organizations fall prey to. Perhaps the surest way to achieve process improvements is by identifying and eliminating the worst practices in forecasting.
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Kwaliteitsverbetering

How an enterprise-wide quality platform can turn existing data into substantial and sustainable revenue growth and cost savings for global manufacturers.
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This Industry Week report explains how an enterprise analytical quality platform (EAQP) works and how using an EAQP can accelerate time-to-market for new products; reduce the negative effects of existing problems by resolving them faster; and enable long-term sustainment of quality improvements. This report also ties these benefits to the results of the IW/SAS Enterprise Quality Survey, which reveal a need for such a platform in all sectors and sizes of manufacturing enterprises.
SAS® Quality Lifecycle Analysis
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This paper describes how your organization can harness the power of analytics to demystify hardware reliability, software reliability and other customer quality data in an effort to fully understand factors that influence total customer experience. Insights on a phased approach for implementing a customer-driven quality solution are discussed in detail. In addition, this paper provides a vision for the future of analytics in numerous areas of the organization.
A Solution for Design and Analysis of Experiments
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In this paper, we will show how ADX guides you through the steps of designing a statistical experiment, analyzing the data and then creating Web-based results that you can share with your co-workers. We will give an overview of how to accelerate process knowledge discovery with ADX through automated design construction algorithms and model-fitting techniques; interactive graphics for exploration and optimization; and HTML report generation. We will illustrate some of these tools by analyzing data from a fractional factorial with multiple responses.
The Perceptual Power of Social Media: Gaining Insights from Social Media on Product Quality
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In the past, a dissatisfied customer told 10 friends; now they tell 10,000. People are talking about their experiences with your products – and your competitors' products. Some may be ambassadors and advocates, others are detractors and malcontents – but all of their voices are in the mix, shaping customers' buying decisions. At a time when product recalls, government intervention and lawsuits are front-page news, customers are questioning brands that seemed to have been above reproach. Descriptive statistics, text analytics and social network analysis provide a way for you to determine how customers purchasing decisions are affected by what they see on the Internet. This paper describes how analytics can be applied to social media and provides some best practices for monitoring and addressing perceptual quality.
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Text Analytics

Gilt Groupe
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Online retailer Gilt Groupe understands its customers and what they want, which in turn enables them to better segment customers and drive them to the sales most relevant to them. This paper provides a summary of a webcast in the SAS "Applying Business Analytics Webcast Series" that explored how Gilt Groupe uses analytics to create a signature shopping experience with distinctive appeal for each of its millions of members, even though members have different tastes, needs and values.
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How are government agencies using analytics to generate new intelligence from text and social media? What tactics, techniques and best practices should they know about? How do you address emerging concerns about appropriate use and privacy of the intelligence gleaned from social media? Those were prominent questions at the 2013 SAS Government Leadership Summit. This conclusions paper summarizes a panel discussion where experts from federal intelligence agencies, the military and academia discuss "the art of the possible" with analytics of unstructured data.
An analysis of the Enron emails using text analytics and case management
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What if investigators could have solved a case like the 2001 Enron scandal more efficiently? By integrating SAS® Enterprise Case Management with SAS Text Analytics, fraud investigators gain an analytical advantage. This paper demonstrates the power of integrating SAS Text Analytics and SAS Enterprise Case Management by using both to analyze the Enron email corpus. SAS Enterprise Case Management provides an organized environment for managing investigation workflows, documentation and case notes. SAS Text Analytics indexes and categorizes documents to further enhance search, uncover root causes and save time and money during time-sensitive investigations and audits.
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In our fast-paced world, consumer opinion can make or break your company's products or services virtually overnight. To understand consumer sentiment and respond quickly to opinions, you must continually monitor and evaluate relevant Web content. Most companies rely on automated techniques to help them quickly characterize the sentiment of documents in a variety of domains. This white paper compares two different approaches to sentiment analysis and illustrates why combining domain-specific linguistic rules with data mining methods improves both the effectiveness of models and the efficiency of model builders.
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Data mining is past the hype stage and has proven that it can produce significant bottom-line results. This paper discusses the many measurable benefits that data mining delivers, including solving complex needle in the haystack problems, eliminating the bad (such as fraud), pinpointing the good (opportunities), streamlining decision processes, and used on qualitative data. Learn how organizations are using data mining to solve their problems, including a $1 billion decision that produced positive results. 
A TDWI Checklist Report
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Text files and unstructured data offer insights that can help organizations reduce costs, improve customer relationships and act more quickly and decisively. This TDWI Checklist Report discusses the relevance of text analytics for improving user experiences with the semi-structured and unstructured textual big data now at their disposal.
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Recent developments in semantic technologies add a new level of intelligence and meaning to enterprise content management and promise to improve information access across any organization. This paper describes how a semantic infrastructure is a platform for building a broad variety of applications and enabling them to communicate with each other – at a higher level than simply exchanging data. To get the full value from semantic technologies, organizations cannot take the typical "project" mindset toward development and implementation. Instead, they must devise a strategic vision of how automating and associating text data fits within – and ultimately transforms – their organization.
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Most businesses lose staggering amounts of time and money searching for information and recreating information that can't be found. Text analytics capabilities, particularly in the area of enterprise content categorization, can dramatically improve how organizations find information. By adding a sophisticated language and semantic component to a whole range of processes within business and government organizations, enterprise content categorization has the potential to solve all those information overload problems that enterprise search and enterprise content management promised to solve, but didn't. This paper describes how to create the foundation for building a semantically integrated enterprise so that you'll have smarter access to the information you need, no matter where it resides.
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Text analytics is a fast-growing area, but it is still new to most organizations. Before jumping in too quickly to select a solution and vendor, it pays to understand exactly what text analytics can do for your business – and how to choose the best solution and vendor. Written by Tom Reamy of KAPS Group, this paper describes text analytics, how it works and why it is so valuable across different organizational areas. It also provides guidelines to steer you through the various stages of the decision process – so you can be confident about your final choice.
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By some estimates, as much as 90 percent of data in the digital universe is unstructured text, images, audio and video.  A webinar hosted by KMWorld Magazine and SAS addressed how organizations can exploit the tidal wave of unstructured data, how big data technologies redefine what is possible, and how to blend huge volumes of structured and unstructured data for exponentially faster and more informed decision making.
Using text analysis and predictive modeling to improve promoter scores
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Two assets significantly influence success or failure of a company. Those are customers and their continued patronage, and employees and their knowledge (as well as their work productivity). This paper describes how to evaluate the likelihood of continued customer patronage versus the risk of losing it. It also acknowledges corresponding loyalty, measured in the form of promoter scores. The paper proposes a strategic analytic roadmap for how SAS enables you to use promoter score survey results to identify customer migration value. The strategic value your organization gains from such analyses enables you not only to understand why customers are or are not likely to promote your product or service – but also their likelihood to do so in the future.
Five characteristics of successful, customer-centric companies - and how health insurers can adopt them to succeed in the new retail marketplace
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With the transformations the health insurance industry is undergoing, more people will be accessing insurance as individuals rather than through employer plans, creating a new consumer-driven retail market of increasingly savvy consumers of health insurance and health care. Success will involve understanding consumers at a deeper level and engaging directly with them as they participate in a more consumer-directed health care environment. Insurers would do well to learn from other industries where key players have mastered the art and science of consumer engagement. SAS has identified five major characteristics of customer-centric organizations that are essential to success. This paper explores each characteristic and discusses how health insurance companies can use them as a guide to help them make operational, organizational and technology decisions.
A TDWI Checklist Report
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Companies realize that text is an important source of data that can improve and provide new insights. This Checklist Report focuses on helping organizations understand how to get started with text analytics. Readers will learn basic definitions of text analytics, how unstructured data can be used and its importance to data, and what resources are needed to get the maximum value from text.
Gleaning insight from unstructured big data
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Text analytics uses linguistic rules and statistical methods to automatically assess, analyze and act on the insight buried in electronic text – such as content from social media, call center logs, survey data, emails, loan applications, service notes, and insurance or warranty claims. This paper summarizes a webinar in the Applying Business Analytics Webinar Series where presenters described text analytics tools and process – and shared their experiences using text analytics in four real-world cases in government, public policy, insurance and media.
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Social media influences many major business decisions today. To cope with these escalating volumes of data, your organization may be developing a social media strategy. But do you recognize why data management should be part of that strategy? It's because social media data simply adds more noise to the clutter of information at hand unless you use the right tools and processes to sort out what is relevant to your business. By aligning data quality with unstructured data, this prolific, valuable data source can be an asset for your business – instead of a detriment.
Improve Decision-Making by Incorporating Unstructured Data - Words and Images - into Analytic Processes
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Organizations are awash in data churned out daily by operational/transactional systems, imported from purchased databases and propagated through analysis and reporting. But that's only the tip of the data iceberg. By some estimates, a minimum of 70 percent of data is actually unstructured data – freeform text, images, audio and video captured from online and offline sources. That's where text analytics comes in. But how can a machine interpret the nuances of human language and other freeform information and use it for meaningful structured analysis? That was the topic of a SAS webinar in the Applying Business Analytics series, originally broadcast in April 2010. This paper provides a summary of that webcast.
Insights from a webinar in the SAS Applying Business Analytics Webcast Series
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The numeric information one typically thinks of as “data” represents only the tip of the data iceberg.  Anywhere from 70 percent to 85 percent of an organization’s available data is unstructured data – freeform text. It is captured from websites, call center records, online reviews, social media, research archives, clinical notes and more. Imagine what an organization could gain with the ability to automatically categorize that content, identify meaningful patterns in it and use it to enrich all forms of business analysis. This paper summarizes a webinar in the Applying Business Analytics series, where Denise Bedford of Kent State University and Fiona McNeill of SAS described the emerging science of text analytics and presented some real-world examples of text analytics in action.
Evolving Tools for an Evolving Environment
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This paper by internationally known writer and speaker, Jim Sterne, is for marketing professionals who want to understand the practical side of text analytics as a competitive advantage. This is neither a technical treatise nor a how-to handbook. It is a relevant guide to a rapidly changing technology that can have a direct impact on your sales top line and financial bottom line.
Using Automated Analytics to Unlock the Hidden Secrets of Unstructured Data
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How can federal, state and local government agencies capture, manage, and use the massive volumes of unstructured text data available to them? Chances are, your agency has a lot of it; the collection and interpretation of unstructured text data is essential to many government processes and problem-solving activities. This paper explores how agencies can invest in technologies such as text analysis to help them unlock insights hidden in their "big data." See how you can use it to accurately gauge public sentiment, identify unmet needs, influence opinion, shape policy, identify hidden risks, stop fraud and collusive crime, and more.
What High Performance Really Means
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The volume of unstructured content available from forums, blogs, emails and product review sites is staggering. Within 10 years, experts predict that 90 percent of the digital universe will comprise unstructured data. All this content provides ample opportunities to analyze, monitor and predict what your customers and constituents are thinking, saying and doing. But it takes more time and computing power than ever before to analyze this data while maintaining solid performance. SAS® High-Performance Text Mining uses in-memory analytic processing to provide significantly increased computation power. So you can process greater amounts of unstructured text data in less time, to improve your predictive models. And get more accurate insights in minutes rather than hours.
Develop your untapped reserves of unstructured data for health, safety and environmental improvements
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This paper illustrates how analytic-driven reporting systems and text mining software can help improve the safety of workers and mining processes in the oil and gas industries. Analytic-driven reporting systems and text mining can  identify quickly and accurately the key metrics that are captured from accident and hazard reports. This can shorten review cycles by automating the manual tasks of reading detailed comment blocks and textual fields, and help discover factors that may have been overlooked in the pursuit of safety improvements.

Applicatie Integratie

Registratie verplicht
A Roadmap
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Accurate, timely and relevant information saves companies both time and money through increased efficiency, improved productivity and an improved ability to make knowledge-based decisions. So why do many companies fail to realize the benefits of all the information in their organization? This white paper answers that question by focusing on the capabilities of SAS Integration Technologies, which are used to tap into SAS solutions from other enterprise applications. You will get an overview including key terminology, as well as a detailed description of the individual components of SAS Integration Technologies. The paper also outlines how many SAS solutions already use the capabilities of SAS Integration Technologies.

Applicatie Ontwikkeling

Registratie verplicht
Making the Most Out of Data Assets Using Web Services
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While Web services have great potential as a technology for implementing applications such as stock price delivery or inventory ordering over the Internet, there is another key business benefit — the delivery of analytical services and business intelligence functionality. This white paper provides a technical overview of how Web services fit into the SAS Intelligence Architecture and shows how to develop applications that make use of the Web services framework. It gives in-depth code examples of how to implement a simple, real-world scenario. The paper describes how to implement Web services using both the.NET and J2EE frameworks and provides an appendix outlining a basic Web services model.
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It's the lure of easy accessibility that makes the Web so compelling. Anyone with a browser can access an application if it's Web-enabled. The data for the application is under centralized control, minimizing the effect of programmatic changes on the user community. The benefits seem endless but how do you put the pieces together for a successful application? This paper provides a framework for answering implementation questions about Web-enabled applications. It also points to the components of the SAS System that are most appropriate for addressing the needs of specific application scenarios. The paper provides three patterns for meeting specific application needs with available technology and includes an annotated list of key terms and technology.

Business Intelligence

Registratie verplicht
Unlocking Hidden Business Insights to Drive Profit
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Based on feedback from SMB organizations from around the globe, this report from Aberdeen Group examines the processes, methodologies and technologies that enable companies to leverage Business Intelligence for fact-based insight into decision making. Learn what criteria distinguished certain companies as top performers within the SMB sector, the factors to consider when assessing your organization's BI competency and the required actions to achieve best-in-class performance.
Dispelling the myths, uncovering the opportunities
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Big data requires new technologies and new management approaches to facilitate fast, continuous decision making. This Harvard Business Review Key Learning Summary describes how organizations can build big data capabilities through the use of Professor Thomas Davenport's DELTTA framework. Readers will learn how to capitalize on the data they already have and how to integrate big data into everyday business operations.
State of the Industry Research Series
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EKN's second annual report, State of the Industry Research Series: Big Data in Retail, provides a benchmark on how retailers are approaching big data as a business strategy, based on a survey of more than 70 retail industry executives about the challenges they faced building deeper insights from massive amounts of data. Learn how retailers can position themselves for competitive advantage with big data and advanced analytics.
Unlock the Business Intelligence Hidden in Company Databases
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In spite of the promise of business analytics – and new technologies that make analytic power more accessible than ever – few organizations have taken full advantage. They have questions about where to focus their efforts, how to address data management issues, and what types of analytic techniques to use where. That was the topic of a SAS-sponsored webinar in the "Applying Business Analytics" series, in which participants offered practical advice about how to make analytics pervasive in the organization, predictive for maximum value, and proactive to drive meaningful action. This paper provides a summary of that webinar.
SAS provides a unified, agile and more effective information infrastructure to support evidence-based decision making across the enterprise
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This white paper discusses some of the key infrastructure challenges that IT faces in meeting the ever-increasing demands for intelligence across their organizations. It provides an overview of how the platform for SAS Business Analytics can help overcome those challenges. It also describes SAS strengths within each of the platform components -- data integration, analytics, and reporting. Most importantly, it outlines how SAS is here to help organizations achieve success through analytic solutions built upon an integrated framework. 
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In an effort to provide better, cost-effective care, health care providers are increasingly turning to IT-enabled business strategies. This white paper by Health Industry Insights, an IDC company, and sponsored by SAS presents the findings and analysis of in-depth interviews conducted with nine senior executives and system architects at three prestigious teaching hospitals acknowledged to be industry leaders in their use of health information technology, in general, and BI applications, in particular.
A white paper by Claudia Imhoff
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The implementation of a business intelligence (BI) environment is not simple, but it does yield tremendous benefits for companies that want to receive the most value from their data resources. Read this paper by BI visionary Dr. Claudia Imhoff to understand the fundamental questions anyone must ask to ensure a successful BI implementation.

You will learn to identify what you have, build a business case for the BI environment, establish the technical infrastructure to support it, maintain data quality and, ultimately, to expand the capabilities of BI through predictive and embedded analytics.
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This white paper reviews a portion of a research program conducted by BusinessWeek Research Services designed to understand how companies can optimize business analytics to improve fact-based decision making and to determine the attitudes and opinions of C-level executives with regard to the use and value of business analytics. It is part of a series of white papers for C-level executives intended to facilitate sharing the most important insights from the research. 
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How effectively an organization manages its supply chain depends on many factors. An organization can have excellent business processes, yet lack the ability to successfully align supply with demand. Read this white paper to learn how advanced forecasting technology and business intelligence can enhance Sales and Operations Planning while supporting better communications and collaboration throughout an enterprise.
The Analytical Center of Excellence
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To truly exploit analytics enterprisewide for a competitive edge, an organization must have a centralized group that provides core expertise, supports users, enforces standards and drives performance. The author dubs this group an "analytical center of excellence" (ACE). After laying out his recommended ACE infrastructure, the author prepares you to engage your organization in establishing an ACE. He describes three primary phases of infrastructure, the different levels of enterprise analytical maturity that determine ACE requirements, and the analytical maturity assessment that must occur in order to develop an implementation plan.
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This TDWI report focuses on the Hadoop technologies currently available, the common types of analytic applications that Hadoop technologies enable and the vendor platforms and tools that support Hadoop.
Law enforcement's opportunity to prevent crime with data integration, reporting and predictive analysis
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From the patrol officer to the chief executive, law enforcement faces myriad challenges that push them into necessity-driven, reactive approaches. With the lack of resources, changes of scope and nonintegrated information, new opportunities to innovate and advance policing will have to emanate from the maximized utilization of the resources that remain: existing staff and accessible information. By pulling together operationally relevant intelligence from vast sources of internal and external data, law enforcement agencies can see the big picture and the critical information necessary to do their jobs effectively.
Leveraging the SAS Business Analytics Framework to accelerate implementations and minimize risk
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This white paper discusses the drivers and the challenges in implementing a longitudinal data system. It then explores how, with SAS' Business Analytics Framework, information gaps among key educational agencies need no longer exist, and decision makers can be armed with the accurate data they need to make proactive decisions and effective education policies.
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It’s not a question of whether small to midsize businesses will implement their own business intelligence (BI) strategy anymore – it’s when. And how. This white paper outlines key steps to a successful BI launch, including starting with quick wins early on to generate support. It also introduces you to the tools needed to effectively mine and share data across the enterprise. 
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This IDC paper, sponsored by Platform Computing and SAS, focuses on the value of deploying business analytics solutions on grid computing platforms. It discusses high-performance computing environments (evolution is moving from clusters to grids to cloud computing), the reasons for choosing business analytics software on grid computing platforms and the benefits achieved by three organizations. These case studies illustrate how SAS Business Analytics and grid computing technologies can enable competitive differentiation, even with increasing data volumes, challenging and ever-changing decision-support requirements, and pressure on IT departments to do more with less.
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Regardless of size, today's businesses are bombarded with structured and unstructured data from a variety of sources. Flowing in faster than ever before, the data is messy and overwhelming. Particularly at small to midsize businesses, strained IT resources simply cannot keep up. How can you take advantage of all this data? With in-memory analytics and data visualization techniques, everyone can get day-to-day insights for making better decisions. Even users with limited analytical and technical skills can quickly and easily explore data for hidden trends and discover new opportunities. Learn how to get blazing-fast insights from all your data, without subsetting or sampling. So you can make more precise decisions – and succeed faster than ever before.
Insights from a webinar in the Applying Business Analytics Webcast Series
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The California Independent System Operator is a nonprofit corporation charged with operating most of California's high-voltage wholesale power grid. Within the organization, the Department of Market Monitoring keeps a close watch on market performance to identify potential anti-competitive market behavior or market inefficiencies. In this paper, Jeff McDonald, Manager of Market Analysis and Mitigation, describes how his group customized its SAS® Business Intelligence solution to mirror the department's unique way of working and provide a self-service analysis and reporting environment, with only minimal reliance on IT.
Maximizing Recovery for the Betterment of State Citizenry
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For state governments, the American Recovery and Reinvestment Act (ARRA) is creating unprecedented management challenges in reporting, transparency and accountability. To meet the President's five crucial objectives for the stimulus funding, governors, state budget officers, controllers and stimulus czars can apply a business analytics approach to managing grants; SAS for recovery optimization and management for state governments provides data integration, reporting and advanced analytics that can be quickly deployed to complement existing grants management systems with minimum disruption.
A Strategic Approach to Creating Significant Economic Value
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Economic conditions are reinforcing the mandate for tighter, more demand-driven supply chains.  Supply chain executives are searching for new value-add and cost reduction vehicles. In this paper, experts from SAS and HAVI Global Solutions argue that because demand management has become such a critical tool for carving out economic value, companies whose core competency is not supply chain management should outsource their demand management functions. The authors explain the benefits, discuss the conditions and technologies that have converged to make those benefits significantly outweigh the risks, and provide tips on assessing if outsourcing demand management is the right strategy for your organization.
What is the future of SAS® Business Analytics from a technology perspective?
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This paper describes the future of SAS® Business Analytics from a technology perspective. During the next few years, SAS will continue driving innovation and delivering new capabilities, technologies and solutions. At the same time, SAS will adapt to the ever-changing landscape and organizational requirements of customers and the marketplace. This paper outlines some, but not all, areas where SAS will focus in the next few years to build on today's solid base.
Benchmark Report 2011
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These days, a retailer needs its organization to respond to the market as a single entity, not a collection of disparate departments. This report highlights an important tool to enable this responsiveness – an enterprise-wide BI strategy. The value of this strategy is to ensure that each department is operating from the same set of data, delivered at the same time. Delivery mechanisms can and will likely differ depending on the physical location of the data consumer, but the data itself is consistent across channels, geographies, departments and roles.
Industry Point of View
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Retailers have a never-before opportunity to get to know their customers better, but this great opportunity has created a great data privacy challenge. While customers are sharing a good deal of personal information, and retailers are readily acquiring it, it's imperative that businesses create a sound privacy plan to instill trust in customers and protect their sensitive, private data. EKN's latest report, The Rising Importance of Customer Data Privacy in a SoLoMo Retailing Environment, details the steps you can take to successfully manage customer data security and privacy, and still provide value.
Enabling BI consolidation and standardization without compromise
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Read this white paper to learn about SAS Business Intelligence, a component of the SAS Enterprise Intelligence Platform. The paper outlines the challenges to delivering true business intelligence and then discusses the benefits of business intelligence technology from SAS. Benefits include faster, better decisions aligning IT and business, data consistency and control, vendor consolidation, and lower total cost of ownership.
The Premier Business Leadership Series, Amsterdam, The Netherlands, May 2012
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The fifth annual European edition of The Premier Business Leadership Series, which was held in Amsterdam, focused on the revolutions taking place in core decision-making processes, with an emphasis on how big data is being used through technological innovations and the application of high-performance analytics. More than 700 international executives in the audience answered on-the-spot survey questions via an electronic voting system, which enabled speakers and panelists to respond to and comment on the feedback. This report summarises each session and includes audience survey results.
The Premier Business Leadership Series, Antwerp, Belgium, June 2011
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The paper forms one of the largest and most recent global surveys conducted among international business leaders related to the impact of analytics on business. It provides insight from business leaders around the world. 
State of the Industry Research Series
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EKN's second annual report, State of the Industry Research Series: Big Data in Retail, provides a benchmark on how retailers are approaching big data as a business strategy, based on a survey of more than 70 retail industry executives about the challenges they faced building deeper insights from massive amounts of data. Learn how retailers can position themselves for competitive advantage with big data and advanced analytics.
An Introduction and Overview
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OLAP has become a standard requirement for almost any BI-related project because it enables users to navigate quickly through complex business data. This white paper provides an overview of several integrated SAS products that can be used for OLAP. You will learn how these products, all part of SAS Enterprise BI Server, allow different types of users to perform analysis of varying sophistication, from simple reporting and exploration to advanced visualization and publishing of results.
An Introduction and Overview
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OLAP has become a standard requirement for almost any BI-related project because it enables users to navigate quickly through complex business data. This white paper provides an overview of several integrated SAS products that can be used for OLAP. You will learn how these products, all part of SAS Enterprise BI Server, allow different types of users to perform analysis of varying sophistication, from simple reporting and exploration to advanced visualization and publishing of results.
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Enterprises are experiencing information overload. Dozens of systems generate reports, slice and dice data and collect it from every possible data point within the enterprise. With all this data, enterprises are beginning to see how effective delivery and sharing of information improves the process of making informed business decisions.

This white paper provides a detailed overview of the SAS Information Delivery Portal, a powerful vehicle for delivering enterprise information to the right people at the right time. The paper describes the state of enterprise information and provides a roadmap and business scenario for implementing the SAS Information Delivery Portal. You will learn about the flexibility of the portal and about its architecture, functionality and administration.
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Regardless of size, today's businesses are bombarded with structured and unstructured data from a variety of sources. Flowing in faster than ever before, the data is messy and overwhelming. Particularly at small to midsize businesses, strained IT resources simply cannot keep up. How can you take advantage of all this data? With in-memory analytics and data visualization techniques, everyone can get day-to-day insights for making better decisions. Even users with limited analytical and technical skills can quickly and easily explore data for hidden trends and discover new opportunities. Learn how to get blazing-fast insights from all your data, without subsetting or sampling. So you can make more precise decisions – and succeed faster than ever before.
Insights from a webinar in the Applying Business Analytics Webcast Series
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The California Independent System Operator is a nonprofit corporation charged with operating most of California's high-voltage wholesale power grid. Within the organization, the Department of Market Monitoring keeps a close watch on market performance to identify potential anti-competitive market behavior or market inefficiencies. In this paper, Jeff McDonald, Manager of Market Analysis and Mitigation, describes how his group customized its SAS® Business Intelligence solution to mirror the department's unique way of working and provide a self-service analysis and reporting environment, with only minimal reliance on IT.
Accessing SAS® Forecast Server from Microsoft Excel
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This paper shows how to access large-scale automated forecasting and strong analytics through a Microsoft Office interface. SAS Forecast Server can be easily accessed from Microsoft Excel using three new forecasting wizards in SAS Add-In for Microsoft Office (included with SAS Enterprise BI Server). These wizards provide users with an alternative to SAS Forecast Studio to drive and automate the forecasting process.
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Visualization

Optimize Your Network Investments & Transform Customer Experience
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For communications service providers, delivering a good customer experience is paramount. And good network performance is one of the best ways to keep customers happy. But most service providers struggle to get insight into their own network data and put it to strategic use. Heavy Reading reveals how predictive analytics gives service providers real-time visibility into their networks – boosting revenue potential and ensuring more satisfied customers, better targeted marketing, fine-tuned capacity planning and top-notch service assurance.
A guided tour of the latest information dashboard capabilities
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For many business users, the search for useful, valuable information is time-consuming and inefficient. Business visualization can transform how you see, discover and share insights hidden in your data. This paper offers highlights from a September 2010 webinar that showed new ways business users can bring movement, graphics and data together to explore data and share insights, right from the desktop.
A research report detailing how organizations are using data visualization to succeed with big data
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This research report details how organizations are combining data visualization with the power of analytics to improve decision making and promote self-service capabilities that drive collaboration. Read about how employees who aren't data scientists or analysts are able to explore data quickly and easily, and get answers to queries in only minutes or seconds. They can then instantly create reports and share the information on the Web or mobile devices, while the IT staff is focusing on other projects.
From basics to big data with SAS® Visual Analytics
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A picture is worth a thousand words – especially when you are trying to find relationships and understand your data – which could include thousands or even millions of variables. To create meaningful visuals of your data, there are some basic tips and techniques you should consider. Data size and composition play an important role when selecting graphs to represent your data. This paper, filled with graphics and explanations, discusses some of the basic issues concerning data visualization and provides suggestions for addressing those issues. From there, it moves on to the topic of big data and discusses those challenges and potential solutions as well. It also includes a section on SAS® Visual Analytics, software that was created especially for quickly visualizing very large amounts of data. Autocharting and "what does it mean" balloons can help even novice users create and interact with graphics that can help them understand and derive the most value from their data.
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Regardless of size, today's businesses are bombarded with structured and unstructured data from a variety of sources. Flowing in faster than ever before, the data is messy and overwhelming. Particularly at small to midsize businesses, strained IT resources simply cannot keep up. How can you take advantage of all this data? With in-memory analytics and data visualization techniques, everyone can get day-to-day insights for making better decisions. Even users with limited analytical and technical skills can quickly and easily explore data for hidden trends and discover new opportunities. Learn how to get blazing-fast insights from all your data, without subsetting or sampling. So you can make more precise decisions – and succeed faster than ever before.
An Innovation in Time-Series Analysis
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By putting data in motion, people can spot trends and see details they might otherwise miss. That's a guiding premise of this white paper from data visualization expert Stephen Few. Find out how interactive graphs lead to valuable analytical insights, illustrating not just the degree of change from one point in time to the next, but also the shape, velocity and direction of change.

Data Integration

Registratie verplicht
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Health and human service agencies can comprise up to 40 percent of state budgets. Yet, in spite of this large investment and the best intentions of these agencies, individuals and families regularly fall through the cracks. This white paper explores ways to use data integration to fill the gaps in service delivery.
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This paper discusses a hybrid approach to transliterating and matching Arabic names, as implemented in a knowledge base (KB) used by data management software systems. The approach to transliteration relies on a lexicon of names with their corresponding transliterations as its primary method, and falls back on Perl regular expression rules to transliterate any names that do not exist in the lexicon. Transliteration in the KB is bidirectional; the technology transliterates Arabic names written in the Arabic script to the Latin script, and transliterates Arabic names written in the Latin script to Arabic. Arabic name matching takes a similar approach and relies on a lexicon of Arabic names and their corresponding transliterations, falling back on phonetic transliteration rules to transliterate names into the Latin script. All names are ultimately rendered in the Latin script before matching takes place. Thus, the technology is capable of matching names across the Arabic and Latin scripts, as well as within the Arabic script or within the Latin script.
A white paper by David Loshin
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In this paper, we look at taking the concepts of data governance into general practice as a byproduct of the processes of inspecting and managing data quality control. By considering how the business is affected by poor data quality – and establishing measurable metrics that correlate data quality to business goals – organizational data quality can be quantified and reported within the context of a scorecard that describes the level of trustworthiness of enterprise data.
SAS provides a unified, agile and more effective information infrastructure to support evidence-based decision making across the enterprise
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This white paper discusses some of the key infrastructure challenges that IT faces in meeting the ever-increasing demands for intelligence across their organizations. It provides an overview of how the platform for SAS Business Analytics can help overcome those challenges. It also describes SAS strengths within each of the platform components -- data integration, analytics, and reporting. Most importantly, it outlines how SAS is here to help organizations achieve success through analytic solutions built upon an integrated framework. 
Recounting the CTO Telecom Summit’s peer-to-peer roundtable, this paper shares CIOs’ challenges and best practices in effective customer data management.
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Recounting the CTO Telecom Summit's peer-to-peer roundtable facilitated by SAS CIO Suzanne Gordon, this paper shares the challenges and best practices in effective customer data management from the CIOs and CTOs of leading service providers. Participants addressed cultural issues, including building collaboration and information sharing, obtaining clear business objectives and bridging the business-IT divide. They also discussed technology issues, such as ensuring data quality and integration to provide a holistic customer view to business leaders.
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This paper summarizes a webcast that describes how Courtyard by Marriott redesigned the lobbies of their properties to be more stylish and functional. Analytics played a role during all phases of design by providing insight from focus groups, surveys and concept testing.
Why a Comprehensive Data Management Platform Supersedes the Data Integration Toolbox
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Most organizations have spent the last decade acquiring data integration tools from different sources to manage, govern and utilize data. This generally means they now possess a nonintegrated toolbox of technologies. Organizations need a solution that enables employees to focus on better managing data instead of integrating disparate technologies. A comprehensive data management platform would address all aspects of data integration, data quality and master data management, be underpinned by adapters and a federation capability, and share technical and business metadata. Ultimately, a single user interface should surface all of the data management capabilities. This white paper describes the evolution of data integration tools and the benefits that can be achieved with a comprehensive data management platform.
Using ETL, EAI, and EII Tools to Create an Integrated Enterprise
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The challenge of integrating enterprise data is not getting any easier, according to this research report published by TDWI. This paper analyzes the results of a survey designed to provide insight into the development of data integration technologies and techniques. The paper also explores requirements for developing an enterprise data integration strategy.
Top Trends, Ideas and Best Practices in a Maturing Data Management Practice
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Data integration involves combining data residing in different sources to provide a unified view or to make it analysis-ready. It improves the flow of accurate, enterprisewide information, enhances data quality and enables collaboration across the organization. This paper looks at the best practices involved in a maturing data management practice, as outlined in a SAS-sponsored webinar in the Applying Business Analytics series.
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Banks and regulators are all about data quality these days – and for good reason. Without meaningful data quality management, big data only leads to faster analysis of "data debris" and inaccurate risk insights. From a risk perspective, high-quality data is essential to implementing effective risk strategies and ensuring compliance. BCBS 239 is just one of many regulatory communications with requirements predicated upon banks having quality data. This paper explains how effective data quality management should be considered a basic requirement for modern-day bank management. It focuses on five best practices for improving data quality that are derived from successful SAS projects within the financial services industry.
A white paper by David Loshin
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Organizations that have a high reliance on information to successfully run their business should have an awareness of the ways that flawed data can increase costs. When times are tough and the boss is looking to reduce expenses, or when times are good and management is seeking greater margins and increased profits, having a process to establish quality data will reveal opportunities where relatively low investments can lead to relatively high returns. This paper will review aspects of cost reduction by examining some typical financial accounting expense categories. The paper then selects some specific examples and assesses their reliance on high-quality data.
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This white paper reviews a portion of a research program conducted by BusinessWeek Research Services designed to understand how companies can optimize business analytics to improve fact-based decision making and to determine the attitudes and opinions of C-level executives with regard to the use and value of business analytics. It is part of a series of white papers for C-level executives intended to facilitate sharing the most important insights from the research. 
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As data volumes and complexities continue to grow, organizations are turning to master data management (MDM) solutions to bring a more coherent view of their various data domains. MDM systems focus on accessing data in disparate systems, bringing it together using matching and cleansing techniques, and presenting the data in a consolidated hub. Data federation, or data virtualization, technologies provide the ability to view data from multiple sources through an integrated, virtual data view. While the data remains stored in original sources, multiple systems can "see" integrated data that appears as a single view. Organizations often implement MDM and data federation technologies in isolation, without regard to the potential power of using them in tandem. This paper explores two scenarios where using SAS® MDM technology and SAS® Federation Server together can enhance the overall value of information while addressing typical master data management requirements.
Critical steps for creating data migration solutions that balance cost and rapid delivery.
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Data migration projects often fail because of an underestimation of the effort required or a lack of planning. This paper focuses on the main areas associated with data migration: source system exploration, data assessment, migration design, migration build, execution, transition and production, and provides recommendations to enhance your chances for successful data migration projects. SAS Enterprise Data Integration Server provides a complete suite of functionality to complete all types of data migrations, as well as a platform that is reliable and scalable. It helps accelerate the delivery of data migration projects and also facilitates component reuse between projects.
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This report from Bloor Research evaluates SAS ETLQ, outlining a number of key findings about the SAS offering. In particular, this report highlights that, while SAS has not traditionally been well known in the ETL marketplace, SAS ETL products are mature and worth consideration. In addition to outlining key findings, this report provides some background on SAS, its available products, and its capabilities in data access and data quality. The paper also includes a discussion of SAS ETL Studio, the primary development environment for ETL processes in SAS ETLQ.
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Healthcare Informatics Research surveyed 418 health care providers to find out whether and how they're integrating payer and provider data now, and how they might proceed in the near future. This special report, based on the results of that survey, takes a look at how looming health reform law is lending urgency to the need for health organizations to streamline their processes to lower costs and optimize quality, and how access to essential data remains a hurdle for many providers.
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Big data is just about everywhere these days. With exponential increase in data volume, variety and velocity comes the question: How do you maintain the quality of all that data? The traditional recipes for data quality success – data profiling, data cleansing and data monitoring – don't always lead to success in today's big data world. This white paper summarizes a webinar in which experts from Knowledge Integrity Inc. and SAS describe an uncommon but highly effective approach to managing big data quality.
Law enforcement's opportunity to prevent crime with data integration, reporting and predictive analysis
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From the patrol officer to the chief executive, law enforcement faces myriad challenges that push them into necessity-driven, reactive approaches. With the lack of resources, changes of scope and nonintegrated information, new opportunities to innovate and advance policing will have to emanate from the maximized utilization of the resources that remain: existing staff and accessible information. By pulling together operationally relevant intelligence from vast sources of internal and external data, law enforcement agencies can see the big picture and the critical information necessary to do their jobs effectively.
Leveraging the SAS Business Analytics Framework to accelerate implementations and minimize risk
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This white paper discusses the drivers and the challenges in implementing a longitudinal data system. It then explores how, with SAS' Business Analytics Framework, information gaps among key educational agencies need no longer exist, and decision makers can be armed with the accurate data they need to make proactive decisions and effective education policies.
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This IDC paper, sponsored by Platform Computing and SAS, focuses on the value of deploying business analytics solutions on grid computing platforms. It discusses high-performance computing environments (evolution is moving from clusters to grids to cloud computing), the reasons for choosing business analytics software on grid computing platforms and the benefits achieved by three organizations. These case studies illustrate how SAS Business Analytics and grid computing technologies can enable competitive differentiation, even with increasing data volumes, challenging and ever-changing decision-support requirements, and pressure on IT departments to do more with less.
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There is a gap between the need for data integration and fully integrated systems. To find out why, SAS and Pharmaceutical Executive conducted confidential telephone interviews with senior pharmaceutical executives and CROs. This article discusses recommendations on how to achieve optimal clinical data integration, based on those interviews.
Revolutionizing the data integration platform
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Organizations depend on data. Regardless of industry, revenue size or the market it serves, every company relies on its data to produce information for business decision making, yet billions of dollars are lost by businesses every year to poor data quality. This white paper discusses the process of improving data quality, starting with the business impact of poor quality data and then outlining the challenges of creating trustworthy data. The paper then lists the four elements that constitute data quality and explains how to achieve them. Ultimately you will learn about the importance of data quality and how technologies like SAS ETLQ can save businesses billions of dollars.
Maximizing Recovery for the Betterment of State Citizenry
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For state governments, the American Recovery and Reinvestment Act (ARRA) is creating unprecedented management challenges in reporting, transparency and accountability. To meet the President's five crucial objectives for the stimulus funding, governors, state budget officers, controllers and stimulus czars can apply a business analytics approach to managing grants; SAS for recovery optimization and management for state governments provides data integration, reporting and advanced analytics that can be quickly deployed to complement existing grants management systems with minimum disruption.
A Strategic Approach to Creating Significant Economic Value
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Economic conditions are reinforcing the mandate for tighter, more demand-driven supply chains.  Supply chain executives are searching for new value-add and cost reduction vehicles. In this paper, experts from SAS and HAVI Global Solutions argue that because demand management has become such a critical tool for carving out economic value, companies whose core competency is not supply chain management should outsource their demand management functions. The authors explain the benefits, discuss the conditions and technologies that have converged to make those benefits significantly outweigh the risks, and provide tips on assessing if outsourcing demand management is the right strategy for your organization.
A Brave New World for the CIO
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Organizations continue to recognize that data holds significant value, especially as new data types and sources become part of the IT infrastructure. As data sets are collected and combined for reporting or analysis, their use and reuse introduces new demands – and uncovers new constraints – in the information infrastructure. Written by thought leader and author David Loshin, this paper provides an overview of the key drivers facing information management teams. By examining the roles of information management in different industries and the types of data management technologies that underpin these efforts, the paper outlines a strategy to help CIOs and other IT leaders prepare for the next wave of information.
Moving beyond ad-hoc ETL to an enterprise data integration strategy
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For years, the key to success for any business solution has been data. Selecting the right tool to bring data from disparate sources and transform it before loading to a target destination was critical. Many organizations struggled with the process, adding new tools as limitations in chosen tools became obvious. Departments often operated in silos when selecting tools, which, coupled with mergers and acquisitions, resulted in ETL tools that weren't integrated. In addition to increases in maintenance and training costs, using different tools can lead to fragmented metadata, which turns compliance into a huge chore. It is time to move away from an ad hoc approach and look for a comprehensive solution that can execute a variety of data integration programs, including data cleansing and enrichment, ETL and ELT, data synchronization, migration, and master data management. This paper provides an overview of requisite data integration capabilities and explains why they are important.
Industry Point of View
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Retailers have a never-before opportunity to get to know their customers better, but this great opportunity has created a great data privacy challenge. While customers are sharing a good deal of personal information, and retailers are readily acquiring it, it's imperative that businesses create a sound privacy plan to instill trust in customers and protect their sensitive, private data. EKN's latest report, The Rising Importance of Customer Data Privacy in a SoLoMo Retailing Environment, details the steps you can take to successfully manage customer data security and privacy, and still provide value.
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Metadata provides a means for both the technical documentation of data and the business communication of its meaning. In an integrated metadata environment this information is automatically used to drive your applications, without having to replicate this information for various applications.

This white paper discusses the role of metadata, or "data about data," to create a single version of enterprise truth and shows how to reduce the total cost of ownership for IT by leveraging a SAS metadata framework.
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This paper discusses how business intelligence complements analytic processes and applications—and how both of these are complemented by data management technologies. For years, organizations have viewed these as separate systems, often with different philosophy. In this paper, Bloor Research discusses the logical connection between the analytics world and data governance (including data quality and master data management) and data integration (including data virtualization, data replication as well as extract, transform and load).
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Using Acme Foods as a fictional case study, this white paper describes a general approach for planning your organization's efforts to improve product data quality. It provides a data-example-driven perspective of some of the unique challenges of product data quality and discusses the three critical steps to improving product data quality.
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Along with the financial crisis of 2009 comes an opportunity for funding through the federal stimulus package. This white paper explores the foundation for education's successful future by outlining a model for sustainable education. It also details four key areas (instructional methods, campus operations, workforce development and infrastructure) essential to reshaping the US educational system in response to this crisis and in preparation for a bright future. Tomorrow is today, and extraordinary things are about to happen. Let's get started!
Bring repeatability and automation to the data integration process with SAS® Clinical Data Integration
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The life sciences industry is under pressure to accelerate time-to-market for new compounds – at lower cost. Traditionally, the process of managing clinical trials data has been cumbersome and resource-intensive. Industry analysts have stated that automated data integration and validation can trim 30 to 50 percent from the clinical trial cycle. This white paper makes a case for implementing data standards and applying automated processes for managing data throughout the clinical trials process, from study design to regulatory submissions. It also describes how SAS Clinical Data Integration provides value for sponsors, CROs and regulatory authorities through mature data transformation capabilities, embedded CDISC capabilities, the ability to automate repeatable processes and the flexibility to support the evolution of both new and custom models.
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While the hospitality and gaming industry is showing signs that it may be starting to emerge from the recent economic downturn, there’s still a long way to go. To that end, industry leaders must work toward optimizing every aspect of the business in order to emerge from the downturn as strong as possible. The key to that goal is to get new intelligence from all the data generated by customer behaviors and operational transactions. Unfortunately, customer interactions are usually captured in a wide range of formats and a multitude of disparate systems, and data quality is suspect. The challenges surrounding data quality and data integration were the focus of an October 2009 Webcast sponsored by the Cornell University Center for Hospitality Research and SAS. This paper provides a summary of that Webcast.
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Master Data Management

A SAS Best Practices white paper
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As the phrase "managing data as a corporate asset" is increasingly repeated in executive board rooms, it's clear that data stewardship is more important than ever. However, is it really a critical success factor? This SAS Best Practices white paper examines how organizations can better manage and maintain their corporate information by making the best decisions about data quality tools, integration architectures and data standards.
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In this Q&A with Gavin Day, Senior Director of Data Management at SAS, he discusses the increasing importance of data quality and governance and how Master Data Management can solve a number of problems. Day talks about CIO's fundamental lack of trust with data, how data can meet business expectations and the changing volume and complexity of data.
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Master data management is an activity that goes beyond the needs of any single business function, so it is important to finesse any recognized barriers to success. In this paper by data quality and MDM thought leader David Loshin, we look at how data consolidation (the typical approach to master data management) can fail to meet data consumption needs. By transitioning from a consolidation approach to a data utilization approach, you will see how MDM can contribute to a long-term information strategy that uses best practices to take advantage of shared repurposed enterprise information.
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This paper helps inform those organizations interested in developing a master data management program regarding the methods that should be used to govern the program once it is in place.
Effective, unified data management strategies depend on IT and business users working together
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Based on a recent IDG Research study, this white paper describes how to get the most out of your data management efforts by making sure your IT and business teams work together. Learn practical tips for creating a unified strategy that supports both the operational and analytical systems
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As data volumes and complexities continue to grow, organizations are turning to master data management (MDM) solutions to bring a more coherent view of their various data domains. MDM systems focus on accessing data in disparate systems, bringing it together using matching and cleansing techniques, and presenting the data in a consolidated hub. Data federation, or data virtualization, technologies provide the ability to view data from multiple sources through an integrated, virtual data view. While the data remains stored in original sources, multiple systems can "see" integrated data that appears as a single view. Organizations often implement MDM and data federation technologies in isolation, without regard to the potential power of using them in tandem. This paper explores two scenarios where using SAS® MDM technology and SAS® Federation Server together can enhance the overall value of information while addressing typical master data management requirements.
Tactics for loading SAS® High-Performance Analytics Server and SAS® Visual Analytics
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Traditional data management strategies will not scale to effectively govern big data for high-performance analytics. As a result, many organizations are evolving their enterprise architectures to address specific business analytics needs. To quickly maximize return on investment from SAS® In-Memory Analytics products, it's important to devise and deploy appropriate information management strategies. This paper discusses tactics, best practices and architecture options associated with loading analytics-ready data required for SAS High-Performance Analytics Server or SAS Visual Analytics. By using a flexible data management approach to get the most out of your analytic data warehouse, you will be well prepared to address a multitude of evolving business, operational and technical requirements.
How one luxury resort company brought its data together
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Ever wondered how you could change your business if you could just get real-time insight from all of your organization's key data resources at once? This e-book reveals in detail how one luxury resort company managed to integrate the tangled web of data from its headquarters and 22 resort locations. Follow the fictional firm's journey from a string of revenue-losing quarters to a record-breaking year, thanks to data virtualization and more.
A white paper by David Loshin
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In this paper, we explore the root cause of the dual challenges of identity resolution, examine how parsing and standardization contribute to the process and review different ways that similarity scoring and approximate matching algorithms help determine identical entities despite variations in data.
TDWI Best Practices Report
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This TDWI Best Practices Report focuses on how organizations can evaluate and deploy data visualization, visual analytics, and data discovery technologies to improve decision making, collaboration and operational execution. The report provides analysis of an in-depth research survey and user stories to reveal current strategies and future plans for data visualization and analysis.
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Manufacturers often sell through multiple channels, from various manufacturing facilities across far-reaching geographies. They coordinate global deliveries and use supply chains comprising large enterprises, distributors and resellers, as well as consumers. The information that feeds back into their businesses becomes increasingly granular as it grows in volume, variety and complexity. To manage it all while empowering decision makers to be more productive, manufacturers must be able to take advantage of leading-edge technologies like cloud, big data and analytics, social business, and mobility. Learn how these technologies can help you harness information and use it to make proactive decisions rather than just reacting to situations as they arise.
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This eBook tells a fictional story about data governance implementation that is based on real-life situations. Complete with a cast of characters, sharp dialogue and a compelling narrative, the book asks (and answers) questions like "What does 'managing data as an asset' really mean? And why do so many data governance efforts start with a bang – only to end with a whimper?"
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Master data management (MDM) is designed to deliver a consistent view of a customer, product, asset or other data type. As the MDM market matured, the depth and breadth of MDM solutions increased, while at the same time big data initiatives placed a premium on technology that could manage large amounts of data. In this Bloor Research report, Philip Howard examines the MDM market, including industry use cases and a view of common MDM components. The paper also includes a review of SAS MDM capabilities and what makes this approach unique in the market.
How to build an effective master data capability as the cornerstone of an enterprise data management program
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Deploying enterprise Master Data Management initiatives requires different levels of maturity and capability, but achieving significant value and tangible benefits early in the process is very possible. Since many of the benefits and value concentrate on simplifying and standardizing semantics, managing metadata and improving data quality, this paper suggests that starting with tasks that address those fundamental needs will add value and prepare your organization to take the steps needed to incrementally build the master data capability. This paper presents concrete actions that you can take to position those fundamentals as the first step in growing a long-term, business-oriented, enterprise information strategy.
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Many of the challenges to master data management (MDM) are organizational and collaborative issues—not technical ones. Luckily, many of MDM's challenges can be remedied by a well-designed and mature program for data governance (DG). In fact, MDM can suffer without DG's processes for collaboration, stewardship, and change management. DG programs are usually founded on a strong mandate, which it can share with MDM to provide much-needed executive sponsorship and a business case. Furthermore, an MDM program won't get far without ample collaboration, and a mature DG program is inherently collaborative, providing processes for cross-functional cooperation and coordination with other data disciplines, especially data integration and quality. DG's collaborative process can help get MDM past many roadblocks. There are good technology and business reasons why master data management needs data governance. This TDWI Checklist Report drills into seven of these reasons as well as use cases and organizational situations where DG and MDM work well together.
Integrated with Data Quality and Data Governance
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While master data management can be practiced in many different ways using a broad array of technologies, this TDWI Checklist Report examines the specific practice of unified MDM. Topics include definitions of data disciplines and the seven leading characteristics typical of business programs and technical solutions for unified MDM.
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Master data management (MDM) is a transformative effort, often requiring organizations to rethink their human resources, business policies and internal processes. In this paper by data quality and MDM thought leader David Loshin, we examine how an incremental approach to MDM can reframe the implementation to focus on near-term business user expectations as well as the long-term needs of the organization. By assessing your existing data governance, metadata, data quality, identity management and change management capabilities, you can prioritize these components within your MDM implementation and establish a more achievable path to MDM.
A SAS Best Practices white paper
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What's the secret to creating a data governance program that stands the test of time? Don't be so focused on the here and now. Anticipate change and build flexibility into your data governance program from day one. This white paper explores what it means to be sustainable, the ecosystem for data governance, sustainable data governance practices and more.
A SAS Best Practices white paper
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Written by Jill Dyche and Kimbery Nevala, Ten Mistakes to Avoid When Launching your Data Governance Program lays out a comprehensive list of issues that can trip up an organization as it gets started with DG. Learn how to use good judgment and sound decision making to sidestep these common issues.
A white paper by David Loshin
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This paper examines some standard examples for product data to highlight some reasons for differentiating product data from other master data domains and then discusses the interplay between product data and other data activities in a multidomain environment. The paper looks at the semantic aspects of product data that enable tagging, description, classification and segmentation. Good product MDM frameworks will address some of the important issues, and we will explore how product MDM helps improve the types of applications described. Lastly, this paper presents some practical steps to take prior to purchasing a product MDM system that will help in assessing business needs and reviewing where product MDM can have the most effect on an organization.
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This e-book describes why a data quality program is critical, as well as the steps needed to achieve data quality improvement, including how to assess the impact of business issues on operations and decision making and monitor for future events. Using a "blended case study" of a health care provider as an illustration, the e-book describes how to overcome data challenges that can affect any company across industry.
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Metadata Management

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Data governance combines the disciplines of data quality, data management, data policy management, business process management and risk management into a methodology that ensures important data assets are formally managed throughout an enterprise. SAS has developed a cohesive suite of technologies that can be used to implement efficient and effective data governance initiatives, thereby improving an enterprise's overall data management efficiency. This paper discusses data governance best practices. It explains where and how SAS® capabilities (such as the business data network, reference data management, data federation, data quality, data management and master data management) can be used to help data governance initiatives remain successful, continue to deliver overall return on investment and gain acceptance across the enterprise.
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Metadata provides a means for both the technical documentation of data and the business communication of its meaning. In an integrated metadata environment this information is automatically used to drive your applications, without having to replicate this information for various applications.

This white paper discusses the role of metadata, or "data about data," to create a single version of enterprise truth and shows how to reduce the total cost of ownership for IT by leveraging a SAS metadata framework.
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This paper discusses how business intelligence complements analytic processes and applications—and how both of these are complemented by data management technologies. For years, organizations have viewed these as separate systems, often with different philosophy. In this paper, Bloor Research discusses the logical connection between the analytics world and data governance (including data quality and master data management) and data integration (including data virtualization, data replication as well as extract, transform and load).
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Migratie en Synchronisatie

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This paper provides information on key highlights of SAS 9.2 that have been released thus far, and is intended to help existing SAS customers understand SAS 9.2 software enhancements. It includes a short section on why customers should upgrade and provides a general overview of what is included with the release. It discusses new software modules and provides enhancement information for many other SAS products. It also includes a section on system management and security features, and a section on installing, configuring and migrating to SAS 9.2.
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This paper discusses how business intelligence complements analytic processes and applications—and how both of these are complemented by data management technologies. For years, organizations have viewed these as separate systems, often with different philosophy. In this paper, Bloor Research discusses the logical connection between the analytics world and data governance (including data quality and master data management) and data integration (including data virtualization, data replication as well as extract, transform and load).
Scenario: AIX 5.3 environment with WebSphere 6.1
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Migration of the SAS 9.1.3 platform environment to SAS 9.2 requires careful and deliberate planning, which includes a migration process to introduce SAS 9.2 into your computing environment. Ideally, you should configure SAS 9.2 on different physical servers from your 9.1.3 environment to ensure your existing production environment remains stable and available. However, if additional hardware is not available, you can configure SAS 9.2 on the same machine(s) that are running SAS 9.1.3. It is also possible to utilize virtualization technologies to simulate the use of different hardware. This paper provides an example of how virtualization can be used to support migration to SAS 9.2 on the same hardware that is running SAS 9.1.3

Data Management

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Highlights from a panel discussion at the 2013 SAS Financial Services Executive Summit
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In the age of big data, business units of banks and insurance companies can no longer toss a request over the wall to IT just in time and hope IT resources will be available. IT has to be involved early in strategic and proactive planning. At the same time, business leaders need to be more technology-aware, so they are less likely to ask for the impossible. This paper summarizes a panel discussion at the 2013 SAS Financial Services Executive Summit. Industry leaders from large banking and insurance companies shared their ideas about how IT and the business should work together and what the organization gains when both sides perform as strategic partners.
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This collection of articles, which originally appeared in Wall Street & Technology (WS&T), explores how the global financial crisis and resulting regulatory scrutiny have changed the capital markets landscape, including how companies look at data management. These pieces were featured in the January 2013 edition and include:
  • The thoughts of WS&T senior editor Melanie Rodier on the reasons data management is getting a "top-to-bottom makeover."
  • Larry Tabb of the Tabb Group, and his take on post-financial crisis data management.
  • David Wallace, Global Financial Services Marketing Manager at SAS, who writes about the benefits of pairing event stream processing (ESP) with high-performance analytics. Wallace also explains how real-time transparency is revolutionizing data management.
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BCBS 239 requires that banks make correct, complete, consistent information available to decision makers in ways that meet their individual needs. Achieving compliance will require significant investment and changes in processes, organizational structure, and technology. The good news is, these investments will pay for themselves by enabling major advantages for banks, not just the national economy. This paper explores how forward-looking finance executives are using high-performance technologies to create fundamentally superior, compliant risk reporting processes – and ultimately help executives realize the goal of sustainable profitability.
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This paper discusses capabilities and techniques for optimizing SAS Data Management products for big data. It demonstrates how SAS supports Apache Hadoop technologies, including details on the various languages available in the platform and how to use them with SAS. An overview of SAS Data Management integration with the SAS® LASR™ platform is also provided. Practical tips and techniques are presented, including case studies that illustrate how to best use the SAS Data Management feature set with big data.
Dispelling the myths, uncovering the opportunities
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Big data requires new technologies and new management approaches to facilitate fast, continuous decision making. This Harvard Business Review Key Learning Summary describes how organizations can build big data capabilities through the use of Professor Thomas Davenport's DELTTA framework. Readers will learn how to capitalize on the data they already have and how to integrate big data into everyday business operations.
Three key technologies for extracting real-time business value from the big data that threatens to overwhelm traditional computing architectures
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How do organizations know what they know? Do they know all they should about their data, and do they know what to do with it? The answers to those questions are being fundamentally reshaped by the concept of big data. Big data is about high-velocity data capture, discovery and analysis to extract meaningful insights from apparent chaos.

In this white paper, Mark Troester of SAS presents three technologies that can help organizations get a handle on big data – and more importantly, enable them to extract real business value from massive data volumes with big data analytics.
Combine SAS® world-class analytic strength with Hadoop’s low-cost, distributed data storage to uncover hidden opportunities
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Want to get even more value from your Hadoop implementation? Hadoop is an open-source software framework for running applications on large clusters of commodity hardware. As a result, it delivers fast processing and the ability to handle virtually limitless concurrent tasks and jobs, making it a remarkably low-cost complement to a traditional enterprise data infrastructure. This white paper presents the SAS portfolio of solutions that enable you to bring the full power of business analytics to Hadoop. These solutions span the entire analytic life cycle – from data management to data exploration, model development and deployment.
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Master data management is an activity that goes beyond the needs of any single business function, so it is important to finesse any recognized barriers to success. In this paper by data quality and MDM thought leader David Loshin, we look at how data consolidation (the typical approach to master data management) can fail to meet data consumption needs. By transitioning from a consolidation approach to a data utilization approach, you will see how MDM can contribute to a long-term information strategy that uses best practices to take advantage of shared repurposed enterprise information.
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For many years, the first instinct of most clinical programmers has always been to write SAS® code by hand, because that was the best approach available. The next innovations from SAS were tools like SAS Enterprise Guide and SAS Clinical Data Integration, with their graphical user interfaces that made programming a great deal easier, faster and more efficient. This white paper discusses how experienced programmers can learn novel tricks and techniques with new tools, solutions and technology.
Top Trends, Ideas and Best Practices in a Maturing Data Management Practice
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Data integration involves combining data residing in different sources to provide a unified view or to make it analysis-ready. It improves the flow of accurate, enterprisewide information, enhances data quality and enables collaboration across the organization. This paper looks at the best practices involved in a maturing data management practice, as outlined in a SAS-sponsored webinar in the Applying Business Analytics series.
The benefits of an insurance data model
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Data management and data quality are no longer optional components of an insurance company's analytical environment – they are essential. And an insurance data model is fundamental to these initiatives. This white paper discusses the benefits of building an analytical data warehouse based on an insurance-specific data model that will enable insurance companies to get the most out of their investment in business analytics.
Chico's Has Customer Loyalty 'In the Bag'
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The women's specialty retailer, Chico's FAS Inc., needed to gain a more holistic view of its middle- to high-income clientele, but the company was operating with a fixed data model that didn't adapt well to a changing environment. Data was sent out for modeling, so the results were often stale by the time campaigns were developed. This paper summarizes an event in the "Applying Business Analytics Webinar Series," in which the company's director of CRM and enterprise information management describes how Chico's has benefited from a software-as-a-service (SaaS) solution for customer and campaign management. With its SaaS solution, Chico's can create timely, personalized offers, and has dramatically reduced the time needed to produce campaigns.
Flexible technology for the agile enterprise
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Today many business users are spending increasing amounts of time trying to find and integrate the relevant pieces of data needed to perform specific tasks and produce insights. One reason for this is that data has become distributed across many different data stores, making it difficult to access integrated, relevant data for cross-functional use. Data virtualization technology has emerged to simplify data access. As this paper by Mike Ferguson of Intelligent Business Strategies explains, data virtualization software gives the impression that data is integrated and stored in a database even though it is not. Using virtualization software, it is possible to create multiple virtual views of data and present data as if it is integrated. Read this paper to learn how virtualization works and how it can impact your organization, find out the requirements for using the technology and see how SAS delivers products that can help you incorporate virtualization into your technology mix.
A strategy for better decisions with better data
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Organizations require operational data quality, in real-time or near-real-time, upon which to base their analytical and decision support systems. This is driven by the need for making better business decisions faster than ever before, using better data, and more varied sources and types of data, with more transparency in data-driven decision making and its business results. Written by Jim Harris – a thought leader with 20 years of data management experience – this paper illustrates how data-driven decision making exists at the intersection of data quality and decision quality, where quality data supports quality business decisions.
Applying data mining, predictive modeling and real-time analytics in oil and gas operations
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Mining large reservoirs of data in oil and gas operations involves committing to key processes and technologies – and embracing new ways of thinking about problem solving. To extract value from vast data stores and change the way decisions are made, many operators have turned to advanced data mining techniques along with real-time analytical and data processing capabilities. This paper explores practical approaches, workflows and techniques that are used in oil and gas operations. It also examines the role of exploratory data analysis; model development and modeling techniques; and approaches to putting models into production.
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As data volumes and complexities continue to grow, organizations are turning to master data management (MDM) solutions to bring a more coherent view of their various data domains. MDM systems focus on accessing data in disparate systems, bringing it together using matching and cleansing techniques, and presenting the data in a consolidated hub. Data federation, or data virtualization, technologies provide the ability to view data from multiple sources through an integrated, virtual data view. While the data remains stored in original sources, multiple systems can "see" integrated data that appears as a single view. Organizations often implement MDM and data federation technologies in isolation, without regard to the potential power of using them in tandem. This paper explores two scenarios where using SAS® MDM technology and SAS® Federation Server together can enhance the overall value of information while addressing typical master data management requirements.
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This paper examines data management issues within the context of Acme, Inc., a fictitious manufacturer of widgets. Rather than looking at data quality and management through a theoretical lens, this paper uses a persona-oriented approach to tackle the issues endemic to many organizations regarding enterprise data governance. 
Tactics for loading SAS® High-Performance Analytics Server and SAS® Visual Analytics
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Traditional data management strategies will not scale to effectively govern big data for high-performance analytics. As a result, many organizations are evolving their enterprise architectures to address specific business analytics needs. To quickly maximize return on investment from SAS® In-Memory Analytics products, it's important to devise and deploy appropriate information management strategies. This paper discusses tactics, best practices and architecture options associated with loading analytics-ready data required for SAS High-Performance Analytics Server or SAS Visual Analytics. By using a flexible data management approach to get the most out of your analytic data warehouse, you will be well prepared to address a multitude of evolving business, operational and technical requirements.
Insights from a webinar in the 2012 Applying Business Analytics Webinar Series
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Organizations are struggling to manage the growing volume, velocity and variety of enterprise data – and the growing expectation to deliver analytic insights from all that data right to the point of decision, right now. In this conclusions paper from the 2012 Applying Business Analytics Webinar Series, Mark Troester and Malene Haxholdt of SAS make the case for adopting an information management approach that brings data management, analytics management and decision management into a unified process under one governance umbrella. Two SAS customers describe how a more holistic approach has delivered greater business value and competitive advantage from their information assets.
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The fundamental challenge for health care providers is that too much information distracts from the goal of providing patient care. The common denominator – the unit of measurement in health care – is patient outcome and the ability to manage all the factors that influence well-being. Solid data governance practices enable the organization to get control of all of its moving parts, identify an individual patient, and track that patient from the doctor’s office to the ER, hospital and home again. When data governance works, it can not only lead to improved patient outcomes, it can also change the very culture of the organization that institutes it.  This paper illustrates how a successful data governance program will harness the energy of disparate IT initiatives and focus on the ultimate goal of patient-centric care.
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There is no question that Solvency II will bring sweeping change to the insurance industry. Companies must not only prove that they have adequate risk management practices and risk models, but must also provide evidence that the data used in those models is accurate, complete and appropriate. This paper explains why.
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From CEOs to CFOs to marketing execs, management understands why customer loyalty is important for increasing profit, why automation creates cost savings through efficiencies, why a better understanding of customers means differentiating the behaviors around them – and why that's a competitive advantage. What they need help with is the "hows." As many executives embrace the idea of a true "single version of the truth" about their customers, they face the fact that their data warehouse and CRM systems, however successful, haven't reached this goal. This paper examines the techniques and processes behind the creation of a more relevant, coherent view of customer data. To put that into practice, organizations can turn to technology designed to manage their customer data – and create a uniform, repeatable set of processes that guide the creation and maintenance of a single customer view.
Insights from a webinar hosted by Electric Light & Power
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New volumes and types of smart grid data can significantly enhance utilities' planning capabilities, inform better market segmentation and improve engagements with customers. But the task is challenging, considering all the new data inputs combined with the complexity of GIS, SCADA, smart meter and CIS systems. To be successful, IT leaders must decide precisely what they need to accomplish with smart grid data. Then they can create an architecture and data governance model that enables easy, wide-ranging data access and relies on analytics to provide reliable energy and outstanding customer service.
Inspection, Monitoring and Tracking
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Before starting any data quality or data governance initiative, an important first step is to set expectations. Specifying expectations provides a means for measuring and monitoring the conformance of data (and the associated processes) within an operational data governance framework. These expectations can be formalized under a data quality service level agreement (DQ SLA), which specifies the roles and responsibilities associated with managing data quality expectations and assuring that they will be met. This paper describes the techniques that can help support operational data governance through creation of DQ SLAs.
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After you have decided your organization needs a data quality scorecard, your first step is determining the types of metrics to use. This paper explores ways to qualify data control and measures to support a data governance program. It examines how data management practitioners can define metrics that are relevant to how specific data-quality issues affect their business. The paper then describes a framework for defining measurement processes to quantify the business value of high-quality data.
End-to-end continuity, cohesion and governance for the entire information path - from raw data to analytic insight delivered at the point of decision.
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The terms "data management" and "information management" are often used interchangeably – but SAS has a much broader interpretation for the latter. Data management is concerned with data integration, data quality and master data management; information management is about using that data to create business value. In this white paper, Mark Troester of SAS describes a holistic information management approach that brings much-needed governance and efficiency to the entire continuum from data to analytics delivered at the point of decision.
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Many of the challenges to master data management (MDM) are organizational and collaborative issues—not technical ones. Luckily, many of MDM's challenges can be remedied by a well-designed and mature program for data governance (DG). In fact, MDM can suffer without DG's processes for collaboration, stewardship, and change management. DG programs are usually founded on a strong mandate, which it can share with MDM to provide much-needed executive sponsorship and a business case. Furthermore, an MDM program won't get far without ample collaboration, and a mature DG program is inherently collaborative, providing processes for cross-functional cooperation and coordination with other data disciplines, especially data integration and quality. DG's collaborative process can help get MDM past many roadblocks. There are good technology and business reasons why master data management needs data governance. This TDWI Checklist Report drills into seven of these reasons as well as use cases and organizational situations where DG and MDM work well together.
How analytics can transform masses of data into competitive differentiation
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The benefits of subscriber data management (SDM) techniques are relatively well known, but providers could significantly extend the value of SDM by adding a layer of analytics. Analytics can bridge the gaps between the telco and IT domains in a service provider's data architecture to create new insights based on a more comprehensive view. In this white paper, Ken King of SAS discusses six key ways service providers can use analytics to develop more enduring and profitable customer relationships.
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Master data management (MDM) is a transformative effort, often requiring organizations to rethink their human resources, business policies and internal processes. In this paper by data quality and MDM thought leader David Loshin, we examine how an incremental approach to MDM can reframe the implementation to focus on near-term business user expectations as well as the long-term needs of the organization. By assessing your existing data governance, metadata, data quality, identity management and change management capabilities, you can prioritize these components within your MDM implementation and establish a more achievable path to MDM.
How data management and analytics can help reinsurers
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Insurance is a tough marketplace, but in many respects reinsurance is much tougher in its unprecedented number of challenges – be they natural disasters, regulatory issues, a continuing global soft market, and legacy issues such as exposure to mold and asbestos claims. To combat these challenges, reinsurers are turning to technology for catastrophe modeling, data analytics and geospatial information systems (GIS) to better understand the data and their risk exposure. This white paper explains how reinsurers can gain a competitive advantage by using data management and analytics. 
What is the future of SAS® Business Analytics from a technology perspective?
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This paper describes the future of SAS® Business Analytics from a technology perspective. During the next few years, SAS will continue driving innovation and delivering new capabilities, technologies and solutions. At the same time, SAS will adapt to the ever-changing landscape and organizational requirements of customers and the marketplace. This paper outlines some, but not all, areas where SAS will focus in the next few years to build on today's solid base.
A Brave New World for the CIO
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Organizations continue to recognize that data holds significant value, especially as new data types and sources become part of the IT infrastructure. As data sets are collected and combined for reporting or analysis, their use and reuse introduces new demands – and uncovers new constraints – in the information infrastructure. Written by thought leader and author David Loshin, this paper provides an overview of the key drivers facing information management teams. By examining the roles of information management in different industries and the types of data management technologies that underpin these efforts, the paper outlines a strategy to help CIOs and other IT leaders prepare for the next wave of information.
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Before starting a data-profiling project, it's vital to understand the tie between the information gleaned from the reports and the proposed business outcomes. By clarifying specific analysis process and techniques (such as those presented in this paper) that use data profiling technology, the analyst team can establish a well-defined scope with specific goals (such as documenting metadata or identifying potential data errors) that can be achieved in a timely and predictable manner.
Industry Point of View
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Retailers have a never-before opportunity to get to know their customers better, but this great opportunity has created a great data privacy challenge. While customers are sharing a good deal of personal information, and retailers are readily acquiring it, it's imperative that businesses create a sound privacy plan to instill trust in customers and protect their sensitive, private data. EKN's latest report, The Rising Importance of Customer Data Privacy in a SoLoMo Retailing Environment, details the steps you can take to successfully manage customer data security and privacy, and still provide value.
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This paper discusses how business intelligence complements analytic processes and applications—and how both of these are complemented by data management technologies. For years, organizations have viewed these as separate systems, often with different philosophy. In this paper, Bloor Research discusses the logical connection between the analytics world and data governance (including data quality and master data management) and data integration (including data virtualization, data replication as well as extract, transform and load).
SAS provides solutions to streamline deployment
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Organizations spend a significant amount of time operationalizing models. But, the less time you have to spend on model deployment issues, the more time you will have to address critical organizational challenges. SAS has accelerated data mining model deployment throughout the analytical life cycle by providing key integration points across SAS solutions, enabling customers to reap immediate benefits, including reduced data movement, increased productivity of analytic and IT teams, and faster time to results. This white paper will help readers understand the analytical modeling life cycle and who in their organization is involved in each step. It also shows how SAS can simplify modeling operations with interfaces for data preparation; model building, registration, validation and promotion; automated model deployment and scoring; and monitoring of results.
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It's easy to get caught up in the idea of big data. You've got massive amounts of data streaming into your business – and it has lots of potential. How can you apply analytics to reveal key business insights? What can you learn about your customers and products? How soon can you get started?

But wait. There are things you need to do first – reliable data management practices you need to put in place before fully taking advantage of big data. This paper explains why data quality and data governance are so important to large-scale analytics. It will help you learn how to balance governance with usability so you can come up with a strategic plan for managing big data, and it includes a checklist of things to look for when evaluating data management tools for big data.
A white paper by David Loshin
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This paper examines some standard examples for product data to highlight some reasons for differentiating product data from other master data domains and then discusses the interplay between product data and other data activities in a multidomain environment. The paper looks at the semantic aspects of product data that enable tagging, description, classification and segmentation. Good product MDM frameworks will address some of the important issues, and we will explore how product MDM helps improve the types of applications described. Lastly, this paper presents some practical steps to take prior to purchasing a product MDM system that will help in assessing business needs and reviewing where product MDM can have the most effect on an organization.
A white paper by David Loshin
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In this paper, we review some barriers to success for MDM programs, especially in terms of accumulating data from multiple source systems with the objective of creating a single source of truth. When an organization can narrow its scope to look at specific value creators, there are opportunities for incremental acceptance and adoption of a master data resource. The adjusted approach to MDM is that a consolidated and unified view of specific master data need not be the only “source of truth.” Instead, consider creating master data repositories that satisfy the needs of specific sets of business or operational processes that need a unified view. Those repositories can be for applications and processes relying on master customer data sets supporting increasing revenues, reducing attrition or increasing customer satisfaction.

Enterprise Intelligence Platform

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Better information for winning decisions
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This paper explores the concepts of Information Management, including the challenges and importance of effectively managing ALL data whether it is structured, unstructured or semi-structured. It discusses the evolution of information management and outlines the SAS approach for a coherent IM strategy, which includes implementing a business intelligence competency center and the use of a comprehensive, integrated software platform.
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This IDC paper, sponsored by Platform Computing and SAS, focuses on the value of deploying business analytics solutions on grid computing platforms. It discusses high-performance computing environments (evolution is moving from clusters to grids to cloud computing), the reasons for choosing business analytics software on grid computing platforms and the benefits achieved by three organizations. These case studies illustrate how SAS Business Analytics and grid computing technologies can enable competitive differentiation, even with increasing data volumes, challenging and ever-changing decision-support requirements, and pressure on IT departments to do more with less.
A comprehensive approach to profitable post-sales customer and service support
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For the past several decades, organizations have focused heavily on improving their supply chains. Best-in-class companies are now turning their attention to the extended supply chain, the service chain, to further improve customer service, reduce costs and boost net profits. This white paper discusses the emerging concept of "service intelligence" and shows how the SAS Enterprise Intelligence Platform provides an integrated foundation for the SAS Service Intelligence software suite.
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This paper investigates how innovative IT can help companies transform themselves into high-performance organizations (HPOs). Having surveyed organizational performance literature and conducted interviews of leaders at 16 major HPOs, the author identifies 12 HPO characteristics that can be positively influenced by IT. He applies them to the IT Practices Capability Framework to produce a new framework for guiding decisions about IT investment and implementation. 
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Intelligence Storage

SAS provides a unified, agile and more effective information infrastructure to support evidence-based decision making across the enterprise
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This white paper discusses some of the key infrastructure challenges that IT faces in meeting the ever-increasing demands for intelligence across their organizations. It provides an overview of how the platform for SAS Business Analytics can help overcome those challenges. It also describes SAS strengths within each of the platform components -- data integration, analytics, and reporting. Most importantly, it outlines how SAS is here to help organizations achieve success through analytic solutions built upon an integrated framework. 
A solution for true scalability tested in the Sun Solution Center
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This technical white paper discusses the test results of SAS Scalable Performance Data Server (one component of SAS Intelligence Storage) on a Sun Solaris 10 x64 Sun Fire X4600 server with two attached Sun StorageTek 6540 arrays. The findings of the test can be applied easily to any engine or server being used as a component of SAS Intelligence Storage.
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