Feeling the heat? Here’s how your Data Science team can prove ROI faster

It’s the beginning of Q3. You lead a data science team at a health insurance company.

You’re about to kick start a project and you need to support a variety of deployment scenarios in multi-cloud environments.If this isn’t making your life complicated enough, your company works in an environment that’s highly sensitive and subject to regulations — ranging from GDPR to HIPAA.

Given the hefty investment in both the team and the tools, you’re under intense pressure to deliver value. Your team has a mixed bag of skills because data scientists are still a rare and expensive commodity. With all these factors at play, how do you deliver on the promise of Data Science and Machine Learning?

IBM has two demos that will show how you can quickly kick start and rapidly scale data science projects in a highly regulated industry.

Watch this video to see a use case involving a complex industry, namely insurance, where fraudulent claims and faulty agent deployments can hurt the bottom line. IBM Analytics solutions architect Irina Saburova shows how data makes all the difference in planning for an impending weather event. Besides predicting financial reserves needed, she shows how data science flushes out the fraudulent insurance claims and intelligently deploys traveling claim adjusters.

Want to follow along? You can read the Q&A on IBM Big Data and Analytics Hub.

Next, in this demo, Tim Bohn from IBM’s Data Science Elite team calls out three areas where IBM Data Science Experience platform overcomes challenges Data Science leaders have in proving value to the business.

· Providing the tools to empower different types of users with a range of skill sets

· Machine Learning at scale — deployment and model upkeep

· Model training where you need and model deployment where you want.

Across these two demos, you’ll learn step by step how IBM’s Data Science Experience supports open source libraries, visual model building, and decision optimization models. You’ll see the process for model development, model deployment and model health evaluation — across secure environments.