Big learning from the INSITE big data symposium 2013

October 10, 2013

Big Data symposium an event organized in University of Arizona, MIS Department, and INSITE center. It had a great list of speakers from diverse backgrounds representing different industrial backgrounds. The symposium covered a huge group of big data use cases from health care to sports.

Started with a great speech from Brian Gentile, Chairman and CEO, jaspersoft, he gave a simple and a clear overview of how big data would influence the industry and discussed the various topics around big data. His speech was about the “New factors of production and rise of big data”. Explained the abundance of data and the value big data brings in by analyzing the data. It was interesting to learn that how big data had been a transitional technical revolution. The transition of the economy from the traditional pattern of land, labor and capital to time, information and capital was an interesting topic. The impact of the four V’s Volume, Velocity, Varity and the fourth new V that I learnt was Varsity. It is essential to understand the importance of the varsity of the analytics achieved from the big data. If the truthfulness of the data is lost, there is no use of the other V’s.
“Applications, Analytics and Aspirations” Brenda Dietrich, VP Strategy and CTO for Business Analytics, IBM. Brenda gave us insights extracting values from the big data analysis. IBM added another V to the stack Value. It is important for industry to understand the value derived before implementing a big data implementation. Big data is not just about social media analytics it is more about extracting value from enormous data available in which we are interested. It is important to understand the internal data before exploring external data sources like the web to conduct your business well. It is safe to start within a controlled boundary where results varied by real experience. With the knowledge gained from that, efforts taken to expand the data sources. It is important that big data analytics provides business value to the organization.
“Driving your business through Big Data”, Darren Stool and Kerem Tomak, marketing analytics, macys.com. It is interesting to see how customer analytics have grown. Importance have changed from understanding the customers implicit feedbacks and shopping preference to the explicit values like how people tend to correlate two different products and similar analysis. Retail industry has been using customer information effectively and the interest appears been growing since the last five years. Macys has been improving the information infrastructure to capture various customer and inventory data to focus and serve its customers better. The retail industry has been focusing on insights to understand its customers better. The industry has also been focusing on using data science and automation in analytics, which appears to be a great indication of big data and analytics growth.

“Unlocking business value with big data and predictive analytics”, Tim Hood, Global VP Chief Solutions Architect, Retail industry, SAP AG. This session explained the importance of big data to carry out predictive analytics. The business value big data could bring through predictive analytics was enormous. Retail industry is one segment where it could be safely and effectively be tested. SAP had taken clear advantage of this using its product HANA. It was interesting to see how predictive analytics and big data was able to function successfully in the gaming world. The ability to use the gaming logs to create retail opportunity is next level of using predictive analytics. It was interesting to see how HANA functions and how it is able to provide real-time analytics from the big data which helps in realizing the business value of data in short interval of time. 

Below you can find my real-time opinion as I was part of the symposium through Twitter. Looking forward for the next years symposium. Will the focus move from Big data to a new concept before 2014? Looking forward for that advancement in analytics.


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