Archive for April, 2016

April 28th 2016

Wise Practitioner – Text Analytics Interview Series: Frederick Guillot at Co-operators General Insurance Company

By: Steven Ramirez, Conference Co-Chair of Text Analytics World Chicago

In anticipation of his upcoming conference presentation, Leveraging Hands on Approaches to Identify Actionable Topics in Property Insurance at Text Analytics World Chicago, June 21-22, 2016, we asked Frédérick Guillot, Manager, Research and Innovation at Co-operators Frederick Guillot ImageGeneral Insurance Company, a few questions about his work in text analytics.

Q: In your work with text analytics, what behavior or outcome do your models predict?

A: First of all, I’m working in the insurance world. As of now, our main focus had been to identify actionable topics in the free format text fields where our exclusive agents can write notes on everything they want about our clients. For instance, the most useful information we try to identify is the “life events” (newborn, wedding, new car, new home, new job, etc.) of our clients.

Q: How does text analytics deliver value at your organization – what is one specific way in which it actively drives decisions or operations?

A: We are at a very early stage of using text analytics in the organization. For this moment, only research prototypes had been delivered. By the end of 2016, we will use text analytics to prioritize which clients to contact first, mostly for cross sell opportunities.

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

A: It is really difficult to articulate for this moment, but early tests in a recent marketing campaign showed an improvement by 15% on our cross sell acceptance ratio compared to a control group.

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

A: Hey, this was something we were really not looking for, but we discovered accidentally that a lot of our clients are annoyed by the fact that we have really strict underwriting rules for oil tanks older than 5 years. We are losing a lot of clients because of this rule, while 5 years of age is not necessarily too old for an oil tank.  

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

A: I will try to demonstrate that hands on efforts can have a great value while doing text analytics, especially in an industry heavily relaying in specialized terminology.  

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Don't miss Frédérick’s conference presentation, Leveraging Hands on Approaches to Identify Actionable Topics in Property Insurance on Wednesday June 22, 2016 at 9:20 to 9:40 am at Text Analytics World Chicago. Click here to register to attend.

By: Steven Ramirez, CEO at Beyond the Arc, and Co-Chair of Text Analytics World

 

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April 25th 2016

Wise Practitioner – Predictive Analytics Interview Series: Alice Chung at Genentech

By: Eric Siegel, Founder, Predictive Analytics World

In anticipation of her upcoming conference co-presentation, Utilizing Advanced Analytics to Generate Insights at Predictive Analytics World Chicago, June 20-23, 2016, we asked Alice Alice Chung imageChung, Senior Manager at Genentech, a few questions about her work in predictive analytics.

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

A: We are trying to understand correlations between certain attributes that drive behavior changes of our study population. Understanding these relationships will help us address business questions and develop appropriate action plans to work effectively with our customers.

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

A: Predictive analytics allows us to perform scenario planning to answer specific questions for our business. In both decision making and operational activities, we will be able to answer questions precisely and to anticipate outcomes based on market dynamics and business needs.

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

A: We are still working on collecting results and characterizing our predictive output. This is a work in progress for us at the moment.

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

A: It’s all about using and synthesizing data to answer specific questions for decision making. I have heard people hypothetically discuss various scenarios to utilize various predictive models yet there’s no data to use. I have also heard people spending time reviewing/analyzing data without business context or knowing what questions to address. Both approaches, in my mind, don’t yield meaningful insights.

For my team, we understand what and how our data can be used and the level of insights it can bring to support predictive models. The most critical component of our analysis and recommendations is to understand its limitations to answer business questions and identify methods to overcome them.

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

A: We will be discussing high level approach of our experiments and have dialogues with the audience to gather input and feedback based on their experiences!

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Don't miss Alice’s conference co-presentation, Utilizing Advanced Analytics to Generate Insights on Tuesday, June 21, 2016 from 11:45 am to 12:05 pm at Predictive Analytics World Chicago. Click here to register to attend.

By: Eric Siegel, Founder, Predictive Analytics World

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April 22nd 2016

Wise Practitioner – Manufacturing Predictive Analytics Interview Series: Carlos Cunha at Robert Bosch, LLC

By: Bala Deshpande, Conference Co-Chair, Predictive Analytics World for Manufacturing 2016

In anticipation of his upcoming Predictive Analytics World for Manufacturing conference presentation, Manufacturing Analytics at Scale:  Data Mining and Machine Learning inside Bosch, we interviewed Carlos Cunha, Senior Data Scientist at Robert Bosch, LLC. View the Carlos Cunha imageQ-and-A below to see how Carlos Cunha has incorporated predictive analytics into manufacturing at Robert Bosch, LLC. Also, glimpse what’s in store for the PAW Manufacturing conference.

Q: What are the challenges in translating the lessons of predictive analytics from other verticals into manufacturing?

A: One of the biggest challenges is in level of accuracy needed. In e-commerce and social networking applications, 80% accuracy might be very good and the consequences of errors are limited compared to that in manufacturing. Particularly, in manufacturing of safety critical applications, the accuracy requirements are stringent. Consequently, lot more complexity and sophistication goes into the provision of analytic solutions.

In root-cause analysis tasks, the challenge is to go from the correlations identified by the models to actual causation. The final proof can only be obtained by direct testing at the line the top potential factors.  But those tests can be time consuming and expensive for the plants, particularly if the plant has not yet fully embraced data mining methods.

Finally, verification and validation in manufacturing is an open challenge, under active research.

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

A: The target applications of our predictive models include the entire variety of business functions: manufacturing, supply chain and logistics, engineering, and Internet of Things and Services.    

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

A: Predictive analytics delivers value at all verticals of our organization; logistics, engineering, production, demand forecasting, etc. It guides our company, helping to decide what we build, how we build it, how to distribute our products and how and who to sell them to.

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

A: In some projects we have obtained up to a 65% reduction in scrap and up to 45% reduction in the time required for testing parts.

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

A: The best discoveries are the ones that are completely unexpected. It is very satisfying when our team discovers issues in the production line that are initially dismissed by the plant engineers as implausible based on their knowledge of engineering principles, only to be later confirmed to have been correct due to secondary and non-linear effects that their physical models did not take into consideration.

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

A:  It is much easier to cook a good meal if you have good ingredients. Knowing what data to collect and how to collect it makes all the difference in the world. However, even with incomplete and noisy data, it is possible to extract useful insights, as long as you account for the limitations of the inferences that can be deduced from such a data. With a tough piece of meat you can’t grill a nice steak, but you can still make a great stew.

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Don't miss Carlos’ conference presentation, Manufacturing Analytics at Scale:  Data Mining and Machine Learning inside Bosch, at PAW Manufacturing, on Wednesday, June 22, 2016, from 4:20 to 5:05 pm. Click here to register for attendance. 

By: Bala Deshpande, Founder, Simafore and Conference Co-Chair of Predictive Analytics World for Manufacturing.

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