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.

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.

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.

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.

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.

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. 

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.

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.

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.

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.

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.

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.

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. 

Le Big Data, à l'origine du Big Analytics chez SAS, permet d'envisager des gains de performance apportés par les nouvelles architectures. SAS vous propose des exemples concrets dans tous les secteurs d'activité notamment la grande distribution, la finance et le secteur public. Ce livre blanc décrit les bénéfices potentiels pour les utilisateurs ainsi que les apports des méthodes analytiques.

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.

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.

Hadoop is an open-source software framework for running applications on large clusters of commodity hardware. As a result, it delivers enormous processing power and the ability to handle virtually limitless concurrent tasks and jobs, making it a remarkably low-cost complement to a traditional enterprise data infrastructure.

In this white paper, Mark Troester of SAS describes key ways SAS supports Hadoop from within the familiar SAS environment – merging the power of SAS Analytics with the power of Hadoop data storage and processing.

SAS Enterprise Miner industrialise le processus de data mining pour définir des modèles prédictifs et des segmentations avec une productivité inégalée, même avec les sources de données les plus volumineuses. SAS propose la suite de méthodologies d'analyses prédictives la plus complète du marché ainsi que des fonctions interactives de visualisation permettant aux utilisateurs d'explorer et d'exploiter les données efficacement et pour créer une plus-value métier stratégique.

SAS Model Manager rationalise les phases de création, gestion, supervision et déploiement des modèles analytiques et offre de puissantes fonctionnalités de suivi de la performance et de réapprentissage garantissant précision et pertinence des modèles. Intégré à SAS Real-Time Decision Manager pour le scoring et la gestion de modèles analytiques, SAS Model Manager optimise les stratégies d'acquisition et de fidélisation de clients. L'intégration avec SAS® Scoring Accelerator (pour EMC Greenplum, Teradata, IBM/DB2 ou Netezza), permet l'enregistrement et la validation des fonctions de scoring directement à l'intérieur de ces bases de données.

Au sein d'un processus guidé, SAS Rapid Predictive Modeler prend en charge de manière automatique et transparente pour l'utilisateur final les tâches de préparation de données et de data mining en vue de générer des modèles prédictifs fiables. SAS Rapid Predictive Modeler propose aux analystes et experts métier des fonctionnalités conviviales leur permettant de générer facilement leurs propres modèles prédictifs, en fonction de leurs besoins et scénarios. Les utilisateurs peuvent ainsi exploiter et tirer parti de modèles prédictifs sans devoir systématiquement solliciter des experts analytiques.

SAS Text Miner met en évidence les informations dissimulées dans des textes, grâce à l’ajout automatisé de sources de données textuelles, au reporting interactif détaillé et aux visualisations. SAS Text Miner permet d’analyser ces données, d’anticiper les tendances et de gérer plus efficacement les nouvelles opportunités. S’appuyant sur la solution de data mining SAS® Enterprise Miner™, SAS Text Miner offre des fonctions statistiques et linguistiques avancées pour intégrer immédiatement les enseignements tirés des textes dans une analyse de data mining traditionnelle.

In a research study jointly sponsored by SAS and Accenture, only one in four respondents indicated that their organization's use of business analytics was "very effective" in helping them make decisions. How can your organization become one of the analytic leaders that reap such significant gains with analytics that its investments are repaid many times over? This white paper presents findings from the research and organizational best practices in four key dimensions – essential guidance for business managers, IT leaders and executives embarking on an analytics project.

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.

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. 

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.

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.

Ce libre blanc explore comment SAS® Analytics aide les assureurs à exploiter leurs données de manière proactive, en renforçant leur comprenhension des mécanismes du fonctionnement de leur entité, au niveau  des sinistres, de la valeur client, de l’adéquation de l’offre produit au canal de distribution, ainsi qu’au niveau des risques et de la mise en conformité réglementaire.

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. 

To improve student achievement, educators and administrators are effectively using valuable data -- through data warehousing and business analytics -- to integrate and analyze data sources in a flexible, easy-to-manage reporting environment. This white paper describes the benefits of using data-driven decision making, as well as information and case studies on SAS onsite and hosted solutions for education.

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. 

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.

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.

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. 

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.

Many forward-looking executives are turning to predictive maintenance (PM) solutions to help prevent equipment failures and avoid the costs of unplanned downtime. This paper explores the business case for investing in predictive maintenance solutions, examines how they work to lower maintenance costs and minimize disruptions across the enterprise, and describes what is required to get started with PM today.

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.

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.

Etude mondiale sur le déploiement et la perception de la Business Analytics dans les entreprises. Selon un récent sondage réalisé par Bloomberg Businessweek, 97% des entreprises avec des revenus de plus de 100 millions de dollars utilisent une ou plusieurs solutions de Business Analytics, ce chiffre était de 90% il y a deux ans. Téléchargez l'étude complète : « The Current State of Business Analytics : Where do we Go from here »

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.

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.

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.

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.

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.

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.

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.

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.

SAS Text Miner met en évidence les informations dissimulées dans des textes, grâce à l’ajout automatisé de sources de données textuelles, au reporting interactif détaillé et aux visualisations. SAS Text Miner permet d’analyser ces données, d’anticiper les tendances et de gérer plus efficacement les nouvelles opportunités. S’appuyant sur la solution de data mining SAS® Enterprise Miner™, SAS Text Miner offre des fonctions statistiques et linguistiques avancées pour intégrer immédiatement les enseignements tirés des textes dans une analyse de data mining traditionnelle.

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.

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.

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.

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.

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.

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.

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.

SAS Enterprise Miner industrialise le processus de data mining pour définir des modèles prédictifs et des segmentations avec une productivité inégalée, même avec les sources de données les plus volumineuses. SAS propose la suite de méthodologies d'analyses prédictives la plus complète du marché ainsi que des fonctions interactives de visualisation permettant aux utilisateurs d'explorer et d'exploiter les données efficacement et pour créer une plus-value métier stratégique.

Avec SAS Desktop Data Mining for Midsize Business, solution abordable et facile à déployer, les utilisateurs disposent d’un ensemble éprouvé de fonctions de modélisation descriptive et prédictive qui les aident à prendre des décisions avisées au bon moment. Vous souhaitez savoir si votre entreprise peut bénéficier des tarifications SAS dédiées aux PME-PMI ? Contactez-nous par email : PME.PMI@sas.com 

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.

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.

The ability to make effective, fact-based decisions is not based on data quantity. In fact, most organizations are awash in data. Rather, success is based on an organization's ability to discover more meaningful and predictive insights from the data it already has. This paper provides a summary of a SAS webinar in the Applying Business Analytics series that showed how predictive analytics and data mining can reveal new insights out of existing data to improve business performance.

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. 

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.

SAS Forecast Server est une solution de prévision automatique à grande échelle qui offre une évolutivité exceptionnelle. Elle automatise les diagnostics ainsi que le traitement des prévisions statistiques en mode batch ou via l'interface graphique interactive. SAS Forecast Server intègre également des fonctions de gestion des données de séries chronologiques.

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.

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.

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.

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.

SAS Model Manager rationalise les phases de création, gestion, supervision et déploiement des modèles analytiques et offre de puissantes fonctionnalités de suivi de la performance et de réapprentissage garantissant précision et pertinence des modèles. Intégré à SAS Real-Time Decision Manager pour le scoring et la gestion de modèles analytiques, SAS Model Manager optimise les stratégies d'acquisition et de fidélisation de clients. L'intégration avec SAS® Scoring Accelerator (pour EMC Greenplum, Teradata, IBM/DB2 ou Netezza), permet l'enregistrement et la validation des fonctions de scoring directement à l'intérieur de ces bases de données.

Au sein d'un processus guidé, SAS Rapid Predictive Modeler prend en charge de manière automatique et transparente pour l'utilisateur final les tâches de préparation de données et de data mining en vue de générer des modèles prédictifs fiables. SAS Rapid Predictive Modeler propose aux analystes et experts métier des fonctionnalités conviviales leur permettant de générer facilement leurs propres modèles prédictifs, en fonction de leurs besoins et scénarios. Les utilisateurs peuvent ainsi exploiter et tirer parti de modèles prédictifs sans devoir systématiquement solliciter des experts analytiques.

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.

This paper presents a methodology for conducting a forecasting strategic value assessment for your organization.

SAS Forecast Server est une solution de prévision automatique à grande échelle qui offre une évolutivité exceptionnelle. Elle automatise les diagnostics ainsi que le traitement des prévisions statistiques en mode batch ou via l'interface graphique interactive. SAS Forecast Server intègre également des fonctions de gestion des données de séries chronologiques.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

Alliant une technologie d'analyse hautes performances et une interface d'exploration conviviale extrêmement graphique, SAS Visual Analytics fournit des résultats analytiques approfondis, en procédant à une analyse exploratoire de l’ensemble de vos données. Des fonctionnalités hautes performances vous permettent (PME ou grande entreprise) d’exploiter petits et grands volumes de données et d’en tirer des conclusions en un temps record. Contact SAS : Jérôme Cornillet, Responsable de l'offre SAS® Business Analytics chez SAS France.

Réunissant trois modules majeurs de SAS (Base SAS®, SAS/STAT® et SAS/GRAPH®) dans une solution PC facile à installer et à utiliser, SAS Analytics Pro for Midsize Business offre aux utilisateurs des PME de riches fonctionnalités pour accéder aux données, les manipuler, les analyser et les présenter sans aucune assistance informatique. Vous souhaitez savoir si votre entreprise peut bénéficier des tarifications SAS dédiées aux PME-PMI ? Contactez-nous par email : PME.PMI@sas.com 

SAS Business Intelligence for Midsize Business s'adresse aux petites et moyennes entreprises en quête d'une solution décisionnelle abordable, facile à déployer et à utiliser. L'intégration avec Microsoft Office permet à tous profils d'utilisateurs d'exécuter des tâches d'intégration de données, d'analyse et de reporting, et favorise ainsi l'adoption d'outils décisionnels dans toute l'entreprise. Vous souhaitez savoir si votre entreprise peut bénéficier des tarifications SAS dédiées aux PME-PMI ? Contactez-nous par email : PME.PMI@sas.com 

Exploitez tout le potentiel de SAS pour collecter, gérer et analyser des données dans votre environnement Microsoft Office. Cette solution est un facteur de productivité pour les PME-PMI dont les ressources informatiques et les compétences analytiques sont réduites. Les données étant traitées à partir du serveur SAS, le problème de limitation des données de Microsoft Office est donc résolu. Vous souhaitez savoir si votre entreprise peut bénéficier des tarifications SAS dédiées aux PME-PMI ? Contactez-nous par email : PME.PMI@sas.com 

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.

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. 

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.

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.

To improve student achievement, educators and administrators are effectively using valuable data -- through data warehousing and business analytics -- to integrate and analyze data sources in a flexible, easy-to-manage reporting environment. This white paper describes the benefits of using data-driven decision making, as well as information and case studies on SAS onsite and hosted solutions for education.

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. 

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.

Etude mondiale sur le déploiement et la perception de la Business Analytics dans les entreprises. Selon un récent sondage réalisé par Bloomberg Businessweek, 97% des entreprises avec des revenus de plus de 100 millions de dollars utilisent une ou plusieurs solutions de Business Analytics, ce chiffre était de 90% il y a deux ans. Téléchargez l'étude complète : « The Current State of Business Analytics : Where do we Go from here »

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.

Le périmètre de l'étude se concentre sur la gestion des données clients, données sur lesquelles de forts besoins d'exploitation ont été remontés dans les précédentes études de MARKESS International sur le sujet. Sur ces données clients, les décideurs interrogés indiquent, en grande majorité, avoir connu une hausse de leur volume depuis 2010 avec des perspectives d'ici à 2014 encore plus fortes. Forts de ce contexte, afin de répondre tant aux attentes des directions métiers en charge de l'exploitation des données clients que des directions informatiques, les acteurs du marché du big data devront proposer des offres modulaires, capables de démarrer sur des périmètres restreints, pour ensuite s'étendre progressivement à d'autres domaines. [ Synthèse à télécharger ]

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.

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.

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.

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.

Alliant une technologie d'analyse hautes performances et une interface d'exploration conviviale extrêmement graphique, SAS Visual Analytics fournit des résultats analytiques approfondis, en procédant à une analyse exploratoire de l’ensemble de vos données. Des fonctionnalités hautes performances vous permettent (PME ou grande entreprise) d’exploiter petits et grands volumes de données et d’en tirer des conclusions en un temps record. Contact SAS : Jérôme Cornillet, Responsable de l'offre SAS® Business Analytics chez SAS France.

En offrant aux PME des fonctions optimisées d’analyse, de visualisation et d’exploration interactive des données, SAS® Visual Data Discovery for Midsize Business améliore les analyses, accélère la prise de décision et présente clairement les résultats. Vous souhaitez savoir si votre entreprise peut bénéficier des tarifications SAS dédiées aux PME-PMI ? Contactez-nous par email : PME.PMI@sas.com 

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.

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.

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.

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.

Hadoop is an open-source software framework for running applications on large clusters of commodity hardware. As a result, it delivers enormous processing power and the ability to handle virtually limitless concurrent tasks and jobs, making it a remarkably low-cost complement to a traditional enterprise data infrastructure.

In this white paper, Mark Troester of SAS describes key ways SAS supports Hadoop from within the familiar SAS environment – merging the power of SAS Analytics with the power of Hadoop data storage and processing.

SAS® Enterprise GRC est une solution d'anticipation et de gestion systématique des expositions et de la communication des risques. Elle renforce la gouvernance et construit la confiance au sein de l'entreprise. Cette solution permet d'identifier et de prévenir les infractions aux lois et réglementations en vigueur.  En vous permettant d'évaluer la performance de vos processus stratégiques par rapport à un référentiel de risques, SAS Enterprise GRC offre une vision plus pointue des risques à l'échelle de votre entreprise.

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.

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.

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.

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.

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.

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.

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.

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.

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 DataFlux® Federation Server together can enhance the overall value of information while addressing typical master data management requirements.

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. 

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.

Opinion des décideurs informatiques et opérationnels sur la gestion du risque (Solvabilité II) et les contraintes en matière d'alignement de l'informatique. Enjeu clé des sociétés d'assurances dans les mois à venir, la mise en conformité avec la directive Solvabilité II concerne en premier lieu les directions financières mais aussi les responsables informatiques. Alors qu'il ne reste que quelques mois aux entreprises, 45 % des responsables interrogés dans cette étude déclarent être encore en cours de décision sur le sujet.

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.

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.

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.

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.

The banking sector routinely manages massive amounts of data, ranging from financial transactions to customer, operational and regulatory data. All this data means big challenges – but also big opportunities – for the industry. Using high-performance analytics, banks can turn their big data into pertinent new business insights that guide faster, better decisions. As a result, banks can successfully manage risk, retain profitable customers, improve operational efficiency and differentiate themselves in the marketplace for competitive advantage.

This collection of articles, which originally appeared in Bank Systems & Technology's special digital publication Big Data = Big Gains, provides insight into the promise that big data holds for an industry still recovering from the turmoil of the financial crisis, and how banks can turn that promise into better and more profitable insight into customers, channels and risks.

It appears that the next step in the big data journey is for companies to discover how they can extract value from the data they gather. But how far along are companies on their big data journeys? And how can they best exploit the massive amounts of data they are collecting? These were among the questions explored by the Economist Intelligence Unit in a recent survey sponsored by SAS. As a result of the survey of senior executives from a broad range of sectors and countries, the EIU found that one thing was clear: if a company can work out how to harness them, the data torrents that are shaping the business landscape can be powerful drivers of innovation and revenue. Read this report to learn more.

Le Big Data, à l'origine du Big Analytics chez SAS, permet d'envisager des gains de performance apportés par les nouvelles architectures. SAS vous propose des exemples concrets dans tous les secteurs d'activité notamment la grande distribution, la finance et le secteur public. Ce livre blanc décrit les bénéfices potentiels pour les utilisateurs ainsi que les apports des méthodes analytiques.

When it comes to big data, CIOs have a choice to make: Lead their IT organizations in a functional role, reacting to big data needs as they are presented; or, elevate IT’s profile by proactively and strategically addressing upcoming business needs. If CIOs choose the latter path, they get in front of the demands now. This white paper will help you get there, describing how to prepare for big data analytics, the key elements of a strategic big data analytics architecture and the success criteria for big data analytics.

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.

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.

Hadoop is an open-source software framework for running applications on large clusters of commodity hardware. As a result, it delivers enormous processing power and the ability to handle virtually limitless concurrent tasks and jobs, making it a remarkably low-cost complement to a traditional enterprise data infrastructure.

In this white paper, Mark Troester of SAS describes key ways SAS supports Hadoop from within the familiar SAS environment – merging the power of SAS Analytics with the power of Hadoop data storage and processing.

SAS High-Performance Analytics Server permet aux entreprises d'analyser de gigantesques volumes de données (« Big Data ») avec une extrême précision pour en retirer des enseignements fiables en quelques minutes seulement. La solution SAS High-Performance Analytics Server est configurée pour être couplée à une appliance de base de données proposée par des partenaires SAS (EMC Greenplum ou Teradata), et permet de résoudre des problèmes complexes à l'aide de ressources de traitement en mémoire. Il s'agit de la seule offre à associer le traitement du « big data » et l'analytique de haut niveau pour fournir des informations cruciales en un temps record.

Alliant une technologie d'analyse hautes performances et une interface d'exploration conviviale extrêmement graphique, SAS Visual Analytics fournit des résultats analytiques approfondis, en procédant à une analyse exploratoire de l’ensemble de vos données. Des fonctionnalités hautes performances vous permettent (PME ou grande entreprise) d’exploiter petits et grands volumes de données et d’en tirer des conclusions en un temps record. Contact SAS : Jérôme Cornillet, Responsable de l'offre SAS® Business Analytics chez SAS France.

Exploiter le « Big Data » et ses enseignements rapides pour transformer votre activité avec SAS® High-Performance Analytics Server. Présentation, enjeux et composants (SAS® Grid Computing, SAS® In-Database, SAS® In-Memory Analytics). SAS® High-Performance Analytics Server dote les entreprises d'une stratégie de croissance administrée pour la gestion analytique d'entreprise, leur permettant de tirer le meilleur parti de l'infrastructure actuelle et des tout derniers investissements en calcul et en matériel.

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.

SAS, Teradata and Platform Computing collaborated with ComputerWorld to produce this tech dossier/white paper on using high-performance computing and business analytics to transform data assets into meaningful insights, gain faster time to results and maximize the productivity of resources to drive sustainable growth. The Tech Dossier will help prospects and customers learn how grid computing and in-database technology can be combined with SAS Business Analytics to drive proactive, evidence-based business decisions.

The banking sector routinely manages massive amounts of data, ranging from financial transactions to customer, operational and regulatory data. All this data means big challenges – but also big opportunities – for the industry. Using high-performance analytics, banks can turn their big data into pertinent new business insights that guide faster, better decisions. As a result, banks can successfully manage risk, retain profitable customers, improve operational efficiency and differentiate themselves in the marketplace for competitive advantage.

It appears that the next step in the big data journey is for companies to discover how they can extract value from the data they gather. But how far along are companies on their big data journeys? And how can they best exploit the massive amounts of data they are collecting? These were among the questions explored by the Economist Intelligence Unit in a recent survey sponsored by SAS. As a result of the survey of senior executives from a broad range of sectors and countries, the EIU found that one thing was clear: if a company can work out how to harness them, the data torrents that are shaping the business landscape can be powerful drivers of innovation and revenue. Read this report to learn more.

SAS High-Performance Analytics Server permet aux entreprises d'analyser de gigantesques volumes de données (« Big Data ») avec une extrême précision pour en retirer des enseignements fiables en quelques minutes seulement. La solution SAS High-Performance Analytics Server est configurée pour être couplée à une appliance de base de données proposée par des partenaires SAS (EMC Greenplum ou Teradata), et permet de résoudre des problèmes complexes à l'aide de ressources de traitement en mémoire. Il s'agit de la seule offre à associer le traitement du « big data » et l'analytique de haut niveau pour fournir des informations cruciales en un temps record.

SAS, Teradata and Platform Computing collaborated with ComputerWorld to produce this tech dossier/white paper on using high-performance computing and business analytics to transform data assets into meaningful insights, gain faster time to results and maximize the productivity of resources to drive sustainable growth. The Tech Dossier will help prospects and customers learn how grid computing and in-database technology can be combined with SAS Business Analytics to drive proactive, evidence-based business decisions.

The banking sector routinely manages massive amounts of data, ranging from financial transactions to customer, operational and regulatory data. All this data means big challenges – but also big opportunities – for the industry. Using high-performance analytics, banks can turn their big data into pertinent new business insights that guide faster, better decisions. As a result, banks can successfully manage risk, retain profitable customers, improve operational efficiency and differentiate themselves in the marketplace for competitive advantage.

It appears that the next step in the big data journey is for companies to discover how they can extract value from the data they gather. But how far along are companies on their big data journeys? And how can they best exploit the massive amounts of data they are collecting? These were among the questions explored by the Economist Intelligence Unit in a recent survey sponsored by SAS. As a result of the survey of senior executives from a broad range of sectors and countries, the EIU found that one thing was clear: if a company can work out how to harness them, the data torrents that are shaping the business landscape can be powerful drivers of innovation and revenue. Read this report to learn more.

SAS High-Performance Analytics Server permet aux entreprises d'analyser de gigantesques volumes de données (« Big Data ») avec une extrême précision pour en retirer des enseignements fiables en quelques minutes seulement. La solution SAS High-Performance Analytics Server est configurée pour être couplée à une appliance de base de données proposée par des partenaires SAS (EMC Greenplum ou Teradata), et permet de résoudre des problèmes complexes à l'aide de ressources de traitement en mémoire. Il s'agit de la seule offre à associer le traitement du « big data » et l'analytique de haut niveau pour fournir des informations cruciales en un temps record.

The banking sector routinely manages massive amounts of data, ranging from financial transactions to customer, operational and regulatory data. All this data means big challenges – but also big opportunities – for the industry. Using high-performance analytics, banks can turn their big data into pertinent new business insights that guide faster, better decisions. As a result, banks can successfully manage risk, retain profitable customers, improve operational efficiency and differentiate themselves in the marketplace for competitive advantage.

It appears that the next step in the big data journey is for companies to discover how they can extract value from the data they gather. But how far along are companies on their big data journeys? And how can they best exploit the massive amounts of data they are collecting? These were among the questions explored by the Economist Intelligence Unit in a recent survey sponsored by SAS. As a result of the survey of senior executives from a broad range of sectors and countries, the EIU found that one thing was clear: if a company can work out how to harness them, the data torrents that are shaping the business landscape can be powerful drivers of innovation and revenue. Read this report to learn more.

Le Big Data, à l'origine du Big Analytics chez SAS, permet d'envisager des gains de performance apportés par les nouvelles architectures. SAS vous propose des exemples concrets dans tous les secteurs d'activité notamment la grande distribution, la finance et le secteur public. Ce livre blanc décrit les bénéfices potentiels pour les utilisateurs ainsi que les apports des méthodes analytiques.

SAS High-Performance Analytics Server permet aux entreprises d'analyser de gigantesques volumes de données (« Big Data ») avec une extrême précision pour en retirer des enseignements fiables en quelques minutes seulement. La solution SAS High-Performance Analytics Server est configurée pour être couplée à une appliance de base de données proposée par des partenaires SAS (EMC Greenplum ou Teradata), et permet de résoudre des problèmes complexes à l'aide de ressources de traitement en mémoire. Il s'agit de la seule offre à associer le traitement du « big data » et l'analytique de haut niveau pour fournir des informations cruciales en un temps record.

Alliant une technologie d'analyse hautes performances et une interface d'exploration conviviale extrêmement graphique, SAS Visual Analytics fournit des résultats analytiques approfondis, en procédant à une analyse exploratoire de l’ensemble de vos données. Des fonctionnalités hautes performances vous permettent (PME ou grande entreprise) d’exploiter petits et grands volumes de données et d’en tirer des conclusions en un temps record. Contact SAS : Jérôme Cornillet, Responsable de l'offre SAS® Business Analytics chez SAS France.

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.

Regulatory agencies in several jurisdictions have sought to augment regulatory requirements put forth by the Basel Committee on Banking Supervision (BCBS) following the financial crisis by mandating that banks define a forward-looking capital plan that incorporates stress scenarios. The new regulations may force banks to redesign their risk modeling, data infrastructure and technology components, as well as more closely integrate their risk and finance departments – which historically have been managed separately. This white paper discusses how banks can successfully cope with the growing regulatory burden by adopting solutions that not only meet current regulatory requirements, but are also flexible enough to address future requirements. The paper also explains how banks that demonstrate a better ability to measure and manage risk can derive business benefits from these regulations and emerge as winners.

Exploiter le « Big Data » et ses enseignements rapides pour transformer votre activité avec SAS® High-Performance Analytics Server. Présentation, enjeux et composants (SAS® Grid Computing, SAS® In-Database, SAS® In-Memory Analytics). SAS® High-Performance Analytics Server dote les entreprises d'une stratégie de croissance administrée pour la gestion analytique d'entreprise, leur permettant de tirer le meilleur parti de l'infrastructure actuelle et des tout derniers investissements en calcul et en matériel.

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.

SAS® Enterprise GRC est une solution d'anticipation et de gestion systématique des expositions et de la communication des risques. Elle renforce la gouvernance et construit la confiance au sein de l'entreprise. Cette solution permet d'identifier et de prévenir les infractions aux lois et réglementations en vigueur.  En vous permettant d'évaluer la performance de vos processus stratégiques par rapport à un référentiel de risques, SAS Enterprise GRC offre une vision plus pointue des risques à l'échelle de votre entreprise.

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 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.

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.

To improve student achievement, educators and administrators are effectively using valuable data -- through data warehousing and business analytics -- to integrate and analyze data sources in a flexible, easy-to-manage reporting environment. This white paper describes the benefits of using data-driven decision making, as well as information and case studies on SAS onsite and hosted solutions for education.

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.

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.

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.

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. 

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.

Opinion des décideurs informatiques et opérationnels sur la gestion du risque (Solvabilité II) et les contraintes en matière d'alignement de l'informatique. Enjeu clé des sociétés d'assurances dans les mois à venir, la mise en conformité avec la directive Solvabilité II concerne en premier lieu les directions financières mais aussi les responsables informatiques. Alors qu'il ne reste que quelques mois aux entreprises, 45 % des responsables interrogés dans cette étude déclarent être encore en cours de décision sur le sujet.

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.

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.

Ce document propose une nouvelle méthodologie pour l'intégration des principes de gestion des données dans l'entreprise. Le cycle de gestion des données qui est ici proposé a largement fait ses preuves, et permet aux entreprises d'instaurer un référentiel de données plus précis, parfaitement intégré et mieux maîtrisé, sur lequel pourront s'appuyer toutes les facettes de leur métier.

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.

This report from Bloor Research evaluates SAS ETL^Q, 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 ETL^Q.

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.

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 DataFlux® Federation Server together can enhance the overall value of information while addressing typical master data management requirements.

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.

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.

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

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 DataFlux® Federation Server together can enhance the overall value of information while addressing typical master data management requirements.

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.

Opinion des décideurs informatiques et opérationnels sur la gestion du risque (Solvabilité II) et les contraintes en matière d'alignement de l'informatique. Enjeu clé des sociétés d'assurances dans les mois à venir, la mise en conformité avec la directive Solvabilité II concerne en premier lieu les directions financières mais aussi les responsables informatiques. Alors qu'il ne reste que quelques mois aux entreprises, 45 % des responsables interrogés dans cette étude déclarent être encore en cours de décision sur le sujet.

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.

Cet article se propose d'examiner l'impact des données non fiables sur les compagnies d'assurance. Il proposera ensuite une définition des besoins permettant de garantir la fiabilité des données en assurance, avant d'exposer une approche pratique pour la création et la gouvernance des données. Enfin, il proposera quelques pistes pour bien engager un programme garantissant la fiabilité des données, qui à leur tour contribueront à optimiser les activités marketing, les activités de souscription et de traitement des sinistres, la gestion du risque, la gestion des réserves techniques, le service client, la conformité et la rentabilité.

Ce document propose une nouvelle méthodologie pour l'intégration des principes de gestion des données dans l'entreprise. Le cycle de gestion des données qui est ici proposé a largement fait ses preuves, et permet aux entreprises d'instaurer un référentiel de données plus précis, parfaitement intégré et mieux maîtrisé, sur lequel pourront s'appuyer toutes les facettes de leur métier.

Cet article se propose d'examiner l'impact des données non fiables sur les compagnies d'assurance. Il proposera ensuite une définition des besoins permettant de garantir la fiabilité des données en assurance, avant d'exposer une approche pratique pour la création et la gouvernance des données. Enfin, il proposera quelques pistes pour bien engager un programme garantissant la fiabilité des données, qui à leur tour contribueront à optimiser les activités marketing, les activités de souscription et de traitement des sinistres, la gestion du risque, la gestion des réserves techniques, le service client, la conformité et la rentabilité.

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.

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.

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.

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.

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.

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. 

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.

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.

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.

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.