ANALYTICS INSIGHTS
Transform data into the best decisions
Recent Analytics Insights
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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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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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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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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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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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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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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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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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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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Machine learning and artificial intelligence in a brave new worldWhat is the interplay between man and machine in a brave new world with AI?
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What do drones, AI and proactive policing have in common?Law enforcement and public safety agencies must wrangle diverse data sets to be effective in their operations. Intelligence analysts are using that data to apply machine learning and AI for more proactive policing.
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Meet the data scientist: Kristin CarneyWhen Kristin Carney graduated with a BS in mathematics, she wasn't sure what she wanted to do with her degree. That’s when she began researching data science.
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Meet the data scientist: Daymond LingDaymond Ling believes the right personal traits are more important than technical skills when it comes to being a successful data scientist.
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Meet the data scientist: Colin NugterenData scientist and CAO Colin Nugteren says while every day is different, one thing remains the same. He ends each day with SAS® Visual Analytics.
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Using data to change the worldApplying data science for social good has led to new and creative ways to address issues related to education, poverty, health, human rights, the environment and more.
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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.
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3 data analytics lessons SMEs can learn from top casinosHow can SMEs in Australia and NZ use big data analytics? First, by learning from major companies already successful in the space, such as casinos.
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5 reasons why everybody should learn data analyticsCould data analytics be the new coding? We certainly think so, and in this article we'll look at five reasons why data analytics is a great skill to learn.
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Data analytics for utilities in Australia: What, why and howHow can big data analytics help Australia's energy utilities? In this article, we discuss why you should care about data, and how it can improve your service.
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Fraud detection and machine learning: What you need to knowFrom integrating throughout operations to maintaining customer service, machine learning – supervised and unsupervised – is a critical part of the fraud detection toolkit. Here’s what you’ll need to consider to get started.
Analytics: The Basics
- Overview
- Big Data Analytics
- Data Mining
- Predictive Analytics
- Statistical Analysis
- Blockchain
- Neural Networks
Analytics
If you're reading this, you probably know at least a little – maybe even a lot – about analytics. According to Wikipedia, analytics is "the discovery and communication of meaningful patterns in data." Seems obvious, right? Where the real story with analytics lies, however, is in what you can do with it. Think in terms of possibilities. Opportunities. Discoveries. Made possible by technological advancements in analytics that were never even dreamed of not so very long ago, within your lifetime. So let's explore the topic of analytics together. We'll share insights from the brightest minds in analytics – insights that you can turn into ... well, it's up to you.
Big Data Analytics
As data floods your organization on a daily basis, the question is no longer "What is big data?" Instead, it's "What can we do with the big data we have?" The answer, of course, is integral to the future of your business. And big data analytics opens up the world of the possible. With big data analytics, you can implement data mining, predictive analytics, text mining, forecasting and optimization to explore your options and make the best possible business decisions.
Data Mining
Have you heard the one about the retailer that discovered diapers and beer are often purchased together? What about the telco that can predict your likelihood to switch carriers based on how many of your friends have already switched? These businesses used data mining to uncover completely unexpected insights in their data. What secrets are hiding in your ever-growing data stores? Find out with data mining.
Predictive Analytics
So you’ve embraced the power of collecting and analyzing data. That’s a great first step, but it just scratches the surface. To get the most out of your data, you need to anticipate the future, not just analyze the past.
Whether you want to attract new customers, assess payment risk, identify fraud or forecast energy demand, predictive analytics can provide answers to your most complex questions.
Statistical Analysis
Statistical analysis isn’t just for sports geeks and political pollsters. Every day, statistics plays a critical role in informing public policy and improving human welfare. The field of statistics has grown from sampling populations to analyzing even the biggest of big data sources.
Today’s statisticians are tracking endangered species, improving agricultural processes and predicting the best treatments for complex diseases. There's a lot going on with this influential field and the statistical programming techniques used by today’s statisticians.
Blockchain
What can you store in blockchain besides Bitcoin transactions? Insurance claims, medical records and even voting records can all be stored in blockchain. Simply put, blockchain is a distributed data architecture that stores data and transactions – and validates the accuracy of those transactions by consensus. Learn more about blockchain and how you can analyze blockchain data.
Neural Networks
Neural networks are computer programs designed to imitate how the human brain learns and searches for patterns. Often referred to as artificial neural networks (ANNs), they are ideally suited to solving real-world problems in different fields. Much like a human brain, they can be trained over time to improve their interpretations of data.
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