Archive for September, 2016

September 30th 2016

Wise Practitioner – Predictive Analytics Interview Series: Robin Thottungal at U.S. Environmental Protection Agency

By: Sean Robinson, Program Chair, Predictive Analytics World for Government

In anticipation of his upcoming conference keynote presentation, 21st Century Data-Driven Environmental Protection at Predictive Analytics World for Government, October 17-20, 2016, we asked Robin Thottungal, Chief Data Scientist/Director of robin-thottungalAnalytics at the U.S. Environmental Protection Agency (EPA), a few questions about his work in predictive analytics.

Q: How would you characterize your agency's current and/or planned use of predictive analytics?  What is one specific way in which predictive analytics actively drives decisions in your agency?

A: Every day, EPA tackles enormous challenges to protect human health and the environment. And now we realize that strategy alone is no longer adequate to address the diverse circumstances that we face in America, such as our changing climate or the risk of chemical explosions. We want to do better.

We are learning that, while technology cannot replace strategy, strategy needs to work hand in hand with a data-driven approach. So we are putting our data to work, using technology to better serve the American people.

One of our plans is to employ predictive analytics to prevent catastrophes and to reduce response times downstream. For example, could real-time monitors at underground storage tanks prevent a chemical spill?

Q: Can you describe the challenges you face or have already overcome in establishing a data-driven environment in your agency?

A: Leaders at the EPA are routinely faced with challenging situations. My question is: how do you create a culture within leadership where decisions are backed by data? One of my goals is to inspire our leadership to make data a critical part of their decision making process. I want them to instinctively ask about the data behind all proposals.

Q: Can you discuss any near term goals you have for improving your agency's use of predictive analytics?

A: We are connecting our vision with our values so that all of our activities align with our mission. And this is possible by following a simple recipe: 1) develop tech-talent within EPA staff; 2) invest in the right technology that works on large volume datasets; and 3) adjust our processes to support predictive analytics.

We took inspiration from tech start-ups. If you mix talent with technology without defining a clear path forward, the right processes evolve naturally. Over time, we will become capable of asking questions that we cannot even think to ask today.

Q: Can you describe a successful result from the employment of predictive analytics in your agency?

A: Across the board, data is driving decisions at EPA. As a result, we are seeing serious improvements in our operational efficiency. This is very exciting because we are not in the business of tackling one specific problem; among other things, we regulate air emissions, we monitor water quality, we fund innovation, and we set standards on chemicals to protect the health of all Americans.

By engraining the spirit of predictive analytics across the enterprise, it seems like everything is changing at the same time. From improved administrative processes to an informed Flint Safe Drinking Water Task Force, efficiencies abound.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World for Government.

A: You can use bureaucracy to your advantage if you take a different approach. By thinking like a start-up, my team has been successful at disrupting the typical way of doing business in EPA. We experiment, select the best minimum viable products, scale them up and iterate.

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Don't miss Robin’s conference presentation, 21st Century Data-Driven Environmental Protection  on Monday, October 17, 2016 from 9:15 to 10:00 am, at Predictive Analytics World for Government. Click here to register to attend. 

By: Sean Robinson, Program Chair, Predictive Analytics World for Government

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September 23rd 2016

Wise Practitioner – Predictive Analytics Interview Series: Miguel Castillo at U.S. Commodity Futures Trading Commission

By: Sean Robinson, Program Chair, Predictive Analytics World for Government

In anticipation of his upcoming conference co-presentation, Words that Matter:  Application of Text Analytics at the U.S. Commodity Futures Trading Commission, at Predictive Analytics World for Government, October 17-20, 2016, we asked Miguel miguel-castillo-imageCastillo, Assistant Inspector General for Audit at U.S. Commodity Futures Trading Commission, a few questions about his work in predictive analytics.

Q: How would you characterize your agency's current and/or planned use of predictive analytics?  What is one specific way in which predictive analytics actively drives decisions in your agency?

A: The agency recognizes that data and technology is critical to transform the CFTC into a 21st century regulator that can efficiently and cost-effectively surveil the derivatives markets. In its Strategic Plan, Goal 1 for IT is to deliver services aligned with core mission functions of the CFTC. Its priority is to meet business needs first by empowering users with self-service technology platforms for data analysis, then by enterprise-focused automation services.  The extensive research and analytical backgrounds of its senior economists also ensures that analyses reflect the forefront of economic knowledge and econometric techniques.  Staff expertise is grounded in a solid knowledge of market institutions and practices and experience communicating the results of quantita­tive analysis.

So while the agency is building its infrastructure, collecting market data, and hiring competent economists, there is an opportunity to introduce predictive sciences into the mix.

Q: Can you describe the challenges you face or have already overcome in establishing a data-driven environment in your agency?

A: One challenge is changing the business culture to balance data access with information security.  We are testing an MOU to meet this challenge.

Q: Can you discuss any near term goals you have for improving your agency's use of predictive analytics?

A: Collaborate, collaborate, and collaborate on projects that use analytics to determine use cases for “predictive analytics.”  I think it’s a matter of time before the agency fully learns from the experience of using data to its advantage.

Q: Can you describe a successful result from the employment of predictive analytics in your agency, i.e., cost avoidance, funds recovered, improved efficiency, etc.

A: Our audit text mining experiment used a simple mathematical approach using historical reports to understand how to better plan audits.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World for Government.

A: Not only will we discuss our audit planning text mining experiment but also another effort related the agency’s rule-making.

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Don't miss Miguel’s conference presentation, Words that Matter:  Application of Text Analytics at the U.S. Commodity Futures Trading Commission, on Tuesday, October 18, 2016, from 10:30 to 11:20 am at Predictive Analytics World for Government. Click here to register to attend. 

By: Sean Robinson, Program Chair, Predictive Analytics World for Government

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September 16th 2016

Wise Practitioner – Predictive Analytics Interview Series: Michael Berry of TripAdvisor Hotel Solutions

In anticipation of his upcoming keynote co-presentation, Picking the Right Modeling Technique for the Problem, at Predictive Analytics World London, October 12-13, 2016, we asked Michael Berry, Analytics Director at TripAdvisor Hotel Solutions, a few michael_berry-imagequestions about his work in predictive analytics.

Q: In your work with predictive analytics, what behavior do your models predict?

A: At TripAdvisor for Business, one of our most important products is subscription-based. We price our subscriptions based on the value our product will deliver to hoteliers in the form of increased direct bookings on their web sites. This means predicting their future traffic, click-through rates, conversion rates, room rates, average length of stay, and so on. Beyond that, I worry about all the usual things subscription-based businesses worry about: What is the probability that a subscriber will renew? What actions of ours can increase that probability? Which non-subscribers are the best prospects? What actions on our part will lead to increased owner engagement?

Q: How does predictive analytics deliver value at your organization? What is one specific way in which it actively drives decisions?

A: I’ve already mentioned pricing.  Another area is sales efficiency.  There are over 900,000 hotels listed on TripAdvisor and our salespeople can’t reach all of them. We use predictive models to pick which properties to call.

Q: Can you describe a successful result, such as the predictive lift of your model or the ROI of an analytics initiative?

A: No. In a public forum like this, I generally show graphs with no numbers on the axes. Of course internally we measure things like the increase in expected value of sales leads so we know how valuable our work is.

Q: What surprising discovery have you unearthed in your data?

A: Here’s one that surprised me a bit when I first started looking at hotel ratings data: The average bubble rating of all reviews is higher than the average bubble rating of all hotels.  Both are pretty high since people tend to like the places they picked, but the difference is noticeable.  How can that be?  Well, some properties have enormous numbers of reviews. Think The Bellagio in Las Vegas.  These properties tend to be traveler favorites so their thousands of reviews bring up the average review score. But the Bellagio is still just one hotel, so it doesn’t affect the average hotel score any more than a Motel 6 on a truck route somewhere.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.

A: There is no one best type of predictive model; you need to pick your tools to match the problem you are trying to solve.

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Don't miss Michael’s keynote co-presentation, Picking the Right Modeling Technique for the Problem on Wednesday, October 12, 2016 at 11:45 am at Predictive Analytics World London. Click here to register to attend.  

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September 6th 2016

Wise Practitioner – Predictive Analytics Interview Series: Ken Yale at ActiveHealth Management

By: Jeff Deal, Program Chair, Predictive Analytics World Healthcare

In anticipation of his upcoming keynote co-presentation at Predictive Analytics World for Healthcare New York, October 23-27, 2016, we asked Ken Yale, JD, DDS, Vice President of Clinical Solutions at ActiveHealth Management, a few questions about Ken Yale 2incorporating predictive analytics into healthcare. Catch a glimpse of his presentation, Predictive Analytics, Genomics, and Precision Medicine – Separating the Hype from the Reality, and see what’s in store at the PAW Healthcare conference in New York City.

Q: In your work with predictive analytics, what area of healthcare are you focused on (i.e., clinical outcomes, insurance, quality improvement, etc.)?

A:  We focus on both clinical and financial outcomes for health insurance plans, and in fact are one of the few organizations that have the capability to derive clinical variables from data. As a care management company, we believe putting clinical knowledge and insight in the hands of doctors and patients can transform the healthcare system and improve lives. We do this by finding the latest developments in the clinical literature, translating these research findings into computer algorithms that mine consumer, member, and patient data, and presenting our findings to patients, providers, and payers so they can understand the situation and take action to improve care. Our job, what we do every day, is find people and give them actionable steps to improve their health.

Q: What outcomes do your models predict?

A:  Clinical actions, quality metrics, financial costs, and other outcomes of interest to our clients – such as personal interests so we can assist an individual with appropriate care management services and behavior change opportunities.

Q: How does predictive analytics deliver value at your organization? What is one specific way in which it actively drives decisions or impacts operations?

A:  One way that predictive analytics delivers value is enabling us to micro-segment a population, identify a “population of one” and deliver targeted services to improve care.

Q: Can you describe a successful result, such as the predictive lift of your model or the ROI of an analytics initiative?

A:  In one micro-segmentation program we were able to obtain a 74% lift in response rate when using different methods of communication designed specifically to the individual, and a 99% lift in response rates when using different kinds of messages targeted to personal interests.

Q: What surprising discovery have you unearthed in your data?

A:  The actual variables that are predictive of chronic conditions, such as obesity, and how easily these can be measured and used to improve health and care.

Q: What areas of healthcare do you think have seen the greatest advances or ROI from the use of predictive analytics?

A: Care management and outcomes have seen the greatest advances and ROI from the use of predictive analytics. In the future, using precision medicine and genomic sequencing, we believe the ROI shall be even greater as we target care and services to individuals. At that point, “population health” will have evolved to be “personal and prescriptive health,” and we shall no longer need to use “one-size-fits-all” population norms to deliver care.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Predictive Analytics World.

A:  Personalized and precision medicine cannot wait for the perfect program to be developed; you have to start somewhere, so any improvement in health quality, outcome, or cost is beneficial and will move the field forward. We shall review and discuss specific improvements in health quality, outcomes, and cost reduction.

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Don't miss Ken’s keynote co-presentation, Predictive Analytics, Genomics, and Precision Medicine – Separating the Hype from the Reality, at PAW Healthcare on Wednesday, October 26, 2016 from 9:10 to 10:05 am.  Click here to register for attendance.

By: Jeff Deal, Conference Chair, Predictive Analytics World Healthcare

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