Big Data Deep Dive Techniques

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Big Data Deep Dive Techniques

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When you have limited resources and big data to crunch in real time, it makes sense to use the cloud. A less obvious scenario is to have data that isn’t very big and the need isn’t for instantaneous feedback—but the only way to get the crunching done in a timely and financially responsible way is to tap the cloud.

That’s the situation the Indiana Department of Workforce Development (DWD) was in. Their data wasn’t big, but they needed compute-intensive, sophisticated predictive modeling done each month. They asked analytics professional services firm Inquidia Consulting to help.

Join Microsoft and our partner, Inquidia, to learn how they designed, developed and deployed a predictive analytics application on Azure using R and SQL Server. All registrants will receive a link to the companion white paper detailing their process.


Josh Oberman is a consultant with Inquidia Consulting. He has three years of experience in computer programming and statistical data analysis. Josh has done extensive work in the R programming language and has a deep and up to date knowledge of the wide variety of tools available in R. Prior to joining Inquidia, Josh worked for a year and a half in a cognitive neuroscience laboratory. He holds a Bachelor’s degree from the University of Chicago.

Bryan P. Senseman is a co-founder of Inquidia. Bryan has over 25 years of experience in business intelligence, the last 20 of which have focused on the delivery of data analytics/BI technology and services. He is known for his technical versatility and deep understanding of data problems across many industries. Bryan's most recent positions were as Director of Technical Architecture for Fair Isaac Corporation, Director for Braun Consulting, Architect for Gottlieb & Wertz, Inc. and Manager for PWC Consulting. Bryan holds a Bachelor of Science degree in Systems Analysis with a minor in Operations Management from Miami University, Ohio.