Analytics
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Article
A guide to machine learning algorithms and their applicationsDo you know the difference between supervised and unsupervised learning? How about the difference between decision trees and forests? Or when to use a support vector algorithm? Get all the answers here.
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Article
Machine learning, Michael J. Fox and finding a cure for Parkinson’sUsing machine learning, data scientists developed a model that can help doctors accurately predict Parkinson's disease progression and start treatment earlier, when it will have greater impact.
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Article
Preventing domestic violence with wearablesAfter her sister became a victim of domestic violence, Kimberly Calhoun developed a wearable that collects data and reports on the offender's location in real-time, alerting police and protecting victims.
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Article
Big data in educationA recent MIT Sloan Management report highlights how businesses are using analytics as a source of innovation -- so are universities, says SAS' Georgia Mariani.
Big Data
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Article
Modern manufacturing's triple play: Digital twins, analytics, IoTUsing exact duplicates to manage complex systems dates to NASA’s early moon missions. Today, IoT is key to implementing digital twin technology in manufacturing.
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Article
Game-changing technologies turn IIoT data into goldThe real value of IoT lies in its data. Maciej Kranz says technologies like edge and fog computing, machine learning and AI can unlock the hidden value in data from the IIoT.
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Big data in government: How data and analytics power public programsBig data generated by government and private sources coupled with analytics has become a crucial component for a lot of public-sector work. Why? Because using analytics can improve outcomes of public programs.
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Article
Machine learning, data science and AI meet IoTIn this video, Kirk Borne and Michele Null discuss the intersection of machine learning, AI and data science with IoT data and analytics.
Business Intelligence
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Article
Seven tips for creating a self-service BI governance strategySelf-service BI and IT governance – sometimes the two seem at odds. Can they coexist peacefully? Live happily ever after? TDWI thinks so. They offer seven tips for creating a strategy that works for both.
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Article
Location intelligence: Adding geospatial context to BI Get tips for implementing a location intelligence data strategy from industry thought leader and data explorer, Jen Underwood.
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Article
IIA research identifies barriers to business intelligence and analytics adoptionFind out what the biggest barriers are to business intelligence and analytics adoption and how to overcome them.
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Interview
Location analytics: Why adding where makes BI betterLearn how location analytics can enrich your BI endeavors by blending geographic data, spatial analysis and business data for deeper insights.
Data for Good
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Article
Analytics can help prevent substance use disorder and over-prescribingStates and MFCUs now have the analytics tools they need to change the trajectory of the opioid crisis by analyzing data and predicting trouble spots – whether in patients, prescribers, distributors or manufacturers. The OIG Toolkit with free SAS® programming code makes that possible.
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Article
Machine learning, Michael J. Fox and finding a cure for Parkinson’sUsing machine learning, data scientists developed a model that can help doctors accurately predict Parkinson's disease progression and start treatment earlier, when it will have greater impact.
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Article
Preventing domestic violence with wearablesAfter her sister became a victim of domestic violence, Kimberly Calhoun developed a wearable that collects data and reports on the offender's location in real-time, alerting police and protecting victims.
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Article
Big data in government: How data and analytics power public programsBig data generated by government and private sources coupled with analytics has become a crucial component for a lot of public-sector work. Why? Because using analytics can improve outcomes of public programs.
Data Management
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Article
Key questions to kick off your data analytics projectsThere’s no single blueprint for starting a data analytics project. Technology expert Phil Simon suggests considering these ten questions as a preliminary guide.
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Article
5 ways to become data-drivenSuccessful data-driven businesses foster collaborative, goal-oriented cultures, have leaders who believe in data and are governance-oriented. Read more in this summary of TDWI research that uncovers best practices for becoming data-driven.
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Article
GDPR and AI: Friends, foes or something in between?The GDPR may not be best buddies with artificial intelligence – but GDPR and AI aren't enemies, either. Kalliopi Spyridaki explains the tricky relationship between the two.
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Article
Personal data: Getting it right with GDPRTo learn more about the definition of personal data, why it’s in the news and why it’s being tightly regulated by laws like the General Data Protection Regulation (GDPR), we interviewed Jay Exum, Privacy Counsel at SAS.
Fraud & Security
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Article
Analytics can help prevent substance use disorder and over-prescribingStates and MFCUs now have the analytics tools they need to change the trajectory of the opioid crisis by analyzing data and predicting trouble spots – whether in patients, prescribers, distributors or manufacturers. The OIG Toolkit with free SAS® programming code makes that possible.
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Article
Shut the front door on insurance application fraud!Fraudsters love the ease of plying their trade over digital channels. Smart insurance companies are using data from those channels (device fingerprint, IP address, geolocation, etc.) coupled with analytics and machine learning to detect insurance application fraud perpetrated by agents, customers and fraud rings.
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Article
Detect and prevent banking application fraudSince credit fraud often starts with a falsified application, it makes sense to have analytics-driven tools in place to detect fraud from the earliest point and across the life of the account.
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Article
Medicaid and benefit fraud in 2018 and beyondTo curb the growing amount of Medicaid and benefit fraud and improper payments, agencies and their commercial counterparts need fraud and abuse detection systems with data management and analysis that can keep up and even stay one step ahead.
Marketing
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Article
Effective real-time marketingEffective real-time marketing is about engaging with customers whenever and wherever they are in their buying journey. Organizations should also be focused on knowing what the customer wants (product, pricing, experience, etc.) based on real-time analytics and predictive modeling.
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Contextual engagementCreating contextual customer engagements means understanding enough about each customer to ensure that all communications over time are progressively more anticipated, personal and relevant.
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Article
Data and the customer experienceCustomer experiences are better when they are backed by a deep customer profiles based on a wide range of data and driven by the latest analytical technologies that enable real-time engagement.
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Article
How to improve your AI marketing skillsMarketing teams can use current AI capabilities to enhance their efforts around campaign automation, dynamic pricing based on forecasting models, and by providing more relevant, real-time customer offers.
Risk Management
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Article
The analytical CRO and the risk aware CFOTo create a more risk-aware organization, the most important collaborative relationship for the CRO is with the CFO and the finance team. The CFO and CRO – as the executives responsible for budgeting and supervision – tend to get caught in the middle of competing objectives.
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Article
frtb: a wait and see strategy could be riskyFRTB, fundamental review of the trading book, is a regulation that changes how banks analyze market risk in the trading book to address systemic challenges.
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Article
CECL: Are US banks ready?CECL, current expected credit loss, is an accounting standard that requires US banking institutions to estimate life-of-loan losses at origination or purchase.
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Article
IFRS 17 and Solvency II: Insurance regulation meets insurance accounting standardsIFRS and Solvency II encourage comparability and transparency from a regulatory and accounting perspective for insurers, but there are important differences.