© Hitachi Solutions, Ltd. 2014. All rights reserved. June 6, 2014 Rowan Sciban and Tyler Becker...

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© Hitachi Solutions, Ltd All rights reserved. Why Advanced Analytics Now Are We Ready For the Next Step In Data  Data Everywhere  Data Collection has be Prevalent  Reporting and Monitoring Mature  Specialized Skills in Analysis  Tools Are Accessible

Transcript of © Hitachi Solutions, Ltd. 2014. All rights reserved. June 6, 2014 Rowan Sciban and Tyler Becker...

Hitachi Solutions, Ltd All rights reserved. June 6, 2014 Rowan Sciban and Tyler Becker Starting Advanced Analytics Hitachi Solutions, Ltd All rights reserved. Rowan Sciban & Tyler Becker Who Are We Hitachi Solutions, Ltd All rights reserved. Why Advanced Analytics Now Are We Ready For the Next Step In Data Data Everywhere Data Collection has be Prevalent Reporting and Monitoring Mature Specialized Skills in Analysis Tools Are Accessible Hitachi Solutions, Ltd All rights reserved. Target Predicts Pregnancy Hitachi Solutions, Ltd All rights reserved. Part 1 Business Understanding Part 2 Data Understanding & Preparation Part 3 Modeling Part 4 Evaluation & Deployment Approach to Predictive Analytics Cross Industry Standard Process for Data Mining Hitachi Solutions, Ltd All rights reserved. Used Automobile Reseller Hitachi Solutions, Ltd All rights reserved. Part 1 Business Understanding Identifying a Worthwhile Business Problem Hitachi Solutions, Ltd All rights reserved. What Specific Problem are we Trying to Solve Relevant to the Business What Will the Analysis Determine Asking A Worthwhile Business Problem Hitachi Solutions, Ltd All rights reserved. How do we predict the number of days an automobile will remain in our inventory before it will be sold? AutoZen Business Question Hitachi Solutions, Ltd All rights reserved. Part 2 Data Understanding & Preparation Selecting and Preparing the Right Data for Analysis Hitachi Solutions, Ltd All rights reserved. 1.Collection of the Data i.Reality: Data is the Biggest Challenge ii.Classifications vs Forecasts 2.Data Cleansing i.Garbage In = Garbage Out ii.Outliers Keep or Remove 3.Selection of your Data Sets i.Parsimony Less is More ii.Science vs. Instinctive data selection Selecting & Staging Your Data Hitachi Solutions, Ltd All rights reserved. Selecting the Right Data Science vs Instinct Hitachi Solutions, Ltd All rights reserved. AutoZen We did not immediately recognize the exact data sets that we wanted to create our analysis with. It took some digging in unsuccessful directions before something bore fruit. Hitachi Solutions, Ltd All rights reserved. AutoZen Hitachi Solutions, Ltd All rights reserved. AutoZen Most Important Data Hitachi Solutions, Ltd All rights reserved. Part 3 Modeling Leveraging Tools and Technology Hitachi Solutions, Ltd All rights reserved. What is Machine Learning Why is it Valuable Using Machine Learning Leveraging Machine Learning Hitachi Solutions, Ltd All rights reserved. AutoZen Demo of Microsoft Azure Machine Learning Hitachi Solutions, Ltd All rights reserved. Part 4 Evaluation & Deployment Hitachi Solutions, Ltd All rights reserved. AutoZen Results of the Analysis Hitachi Solutions, Ltd All rights reserved. AutoZen Utilization of the Results Hitachi Solutions, Ltd All rights reserved. Next Steps Rollout and Adoption Hitachi Solutions, Ltd All rights reserved. AutoZen Step 1: Pilot Randomly select a few stores Deploy the app selectively track sales performance Improvement Step 2: Incentivize Incentivize sales persons on profit margin Make the forecasting app available Step 3: Communicate Publish news on sales improvement from the app Hitachi Solutions, Ltd All rights reserved. Other Applications of Machine Learning Microsofts How Old Hitachi Solutions, Ltd All rights reserved. Other Applications of Machine Learning Microsofts Q&A Hitachi Solutions, Ltd All rights reserved. Sponsors Hitachi Solutions, Ltd All rights reserved. Thank You.