1. Big Data What does it mean? How do we mine it? John Johansen October 2013 jjohansen@agiletech.com

2. Session Objective Who saw that coming! Our organizations’ data is increasingly capable of helping us anticipate and plan for the unexpected. –Powerful tools are emerging to help identify patterns and make predictions of potential risks and opportunities. –These predictive analytics will allow companies to focus on the real trouble spots and develop the right conclusions. This session will explore and demonstrate these tools while identifying potential applications and solutions in our day-to-day jobs. At the conclusion of the session participants will understand –The role that analytics can play in supporting your organizational objectives. –How these tools can identify elevated risk and help plan effective strategies to maximize opportunities. Who saw that coming? You did! 2

3. Agenda Big Data Explained Why Are These Solutions Emerging Now ? Some Common and Not-so Common Applications in our Businesses The Steps in the Mining Process A Real Live Demo of a Mining Process Questions Wrap - Up 3

4. But first… a promise… 4

5. 5

6. 6 No plunge !

7. Big Data Explained 7 Volume Velocity Variety RF Tagging Healthcare Monitoring Unstructured Data Sensor Data Real Time Data > Peta? Zetta Voice of the Customer Text Analysis

8. Four Emitters Connected to the Internet of Things Cows & Crops 8

9. Transactions, Interactions & Observations 9

10. Big Data in the Gartner Hype Cycle 10

11. There are less scientific hype indicators 11

12. Why Now? Chances are, the tools you need are the tools you have.* At long last, the data that you need is the data that you have. The processing power that you need is the processing power that you have.* 12 * And if you don’t already have them, they are readily available with very reasonable ROI

13. Four Emitters Connected to the Internet of Things Cows & Crops Christmas Trees 13

14. Data is Increasingly a Differentiator 14 Production Reporting Interactive Dashboards Data Discovery Predictive Analytics Operational Analytics Differentiation Sophistication High Low High

15. Predictive Analytics 15

16. Potential Challenges 16 “Prediction is very difficult… Niels Bohr Casey Stengel President Bill Clinton Especially if it’s about the future.”

17. Predictive Analytic Solutions We see these solutions as a class of software tools that will look through large sets of data to uncover subtle patterns in data, then use those patterns to predict the behavior of a new set of data. The impact these solutions are having at companies are generally in the areas of: –Improving the profitability of existing clients by identifying high probability cross-selling activities. –Improving the effectiveness of direct marketing programs through more focused, higher likelihood of success programs. –Identifying fraud. –Identifying likely candidates for churn either in the customer-base or in the channel. –Modeling customer reactions to price or term changes. 17

18. Organizations Embrace Analytics Differently

19. Killer Applications

20. What are the steps? As with any other solutions, we suggest starting at the end: Outlining specifically what we are hoping to achieve. Next we need to establish at least two sets of data from our existing historical data. One will help us Train the model, the other will be a Test set that will allow us to validate that in fact the model that we create is valid. Once we’re sure we’ve got a model that establishes the right relationships, we can run our source data through our model and get our predictions. Analyze, Rinse. Repeat. 20

21. Four Emitters Connected to the Internet of Things Cows & Crops Christmas Trees Pill Bottles 21

22. Demonstrating the Power We’ve arranged for a quick demo of the Microsoft tools. –Churn Prediction –Claim Amount –Cross Selling It’s interesting to note, that this demonstration is running on a simple server, using software tools that are included, free, with Microsoft SQLServer. 22

23. Demonstration – Predicting Churn 23

24. Demonstration – Claim Cost 24

25. Demonstration – Cross Selling 25

26. Four Emitters Connected to the Internet of Things Cows & Crops Christmas Trees Pill Bottles Diapers 26

27. Questions? 27

28. In conclusion… Big Data Explained Why Are These Solutions Emerging Now ? Some Common and Not-so Common Applications in our Businesses The Steps in the Mining Process A Real Live Demo of a Mining Process Questions 28

29. In conclusion… 29 “I figure lots of predictions is best. People will forget the ones I get wrong and marvel over the rest.” Alan Cox

30. Contact John Johansen Partner Agile Technologies, LLC One Easton Oval, Suite 388 Columbus, Ohio 43219 jjohansen@agiletech.com www.agiletech.com 30