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Recent Analytics Insights
- IoT in healthcare: Unlocking true, value-based careGiven the potential of IoT – and the challenges of already overburdened healthcare systems around the world – we can’t afford not to integrate IoT in healthcare.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Machine learning and artificial intelligence in a brave new worldWhat is the interplay between man and machine in a brave new world with AI?
- 3 steps for AI ethicsWill artificial intelligence benefit humanity or usher in a series of unintended consequences? AI ethics may be one way to ensure artificial intelligence is used for good.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- Data for good: Protecting consumers from unfair practicesHow text mining and machine learning could help the Consumer Financial Protection Bureau do even more.
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