INFORMS The Business of Big Data 2014: Day 2 Highlights

Highlights from the presentations by Big Data leaders from Accenture, Analytics Media Group, SAS and Intel on day 2 of INFORMS The Business of Big Data.

INFORMS Conference 2014, held on June 22-24, 2014 at San Jose Convention Center, focused on "The Business of Big Data" and educated the attendees on the best way to prepare themselves for the growing opportunities in the field of Big Data. The conference was a great opportunity for INFORMS The Business of Big Data 2014learning as well as networking with the leaders from industry and academia through a seamless exchange of information, ideas and perspectives. INFORMS The Business of Big Data 2014The impressive line-up of speakers, which included Big Data leaders from various industries, shared the best practices through real-world case studies and tutorials on a wide variety of topics such as getting from data discovery to return on investment and real business value, bridging the gap between decision makers, IT managers, and analytics professionals, etc. The conference program included keynote sessions, presentations across 3 tracks (Big Data Case Studies, Big Data 101 and Emerging Trends in Big Data), technology workshops, panel discussions and exhibits.

Highlights from Day 1.

Here is a succinct summary of the key takeaways from selected talks on day 2:

Michael SvilarMichael Svilar, Managing Director, Advanced Analytics at Accenture started the second day of conference giving keynote speech titled “Big Data in Action: Applying Analytics to the Internet of Everything”. In the Internet of Everything world, systems, devices and physical objects are constantly communicating with one another. He mentioned that there are more than a trillion connected and instrumented things such as cars, appliances, cameras, etc. His talk focused on how organizations can drive business outcomes in the connected world using big data analytics.

Big Data in enterprise should target driving from issues to outcomes. He also discussed steps involved in “Analytics Journey to ROI”:  Issues -> Data -> Analytics -> Insights -> Actions -> Outcomes. He also shared solution to problem of water leakage and management (Refer picture below). Digital Water
Alan PapirAlan Papir, Software Engineer, Analytics Media Group (AMG) talked about “Optimizing Media Purchasing through Big Data”.  AMG used lessons learned from the 2012 Obama presidential campaign to bring new, data-driven insights into the world of media buying. Using various modelling and data mining techniques in conjunction with large and rich datasets such as billions of set top box records. AMG discovered who is most likely to “convert” to a product or candidate at the person-level. AMG then takes these desirable targets and uses a trove of set top box data to produce a near-optimal solution to problem of purchasing the most valuable placements given a limiting budget.

He briefly discussed AMG’s techniques for identifying targets, strategies for efficiently storing and retrieving tens of billions of TV viewing records, and heuristics for finding a near-optimal media buy plan. AMG’s optimization software played a big in shaping the media planning of the Obama 2012 campaign, allowing them to stay competitive despite being outspent.

Paul KentPaul Kent, VP, Big Data, SAS, gave a talk titled “Big Data and Big Analytics – So Much More Gunpowder!” Paul’s talk focused on four themes: abundance, Hadoop, SAS on Hadoop, and Big Data ideas for organizations. We find ourselves in an “era of abundance” because the cost of storing information has become less than the cost of making the decision to throw it away. We can use this data to answer questions that have not even been formulated at the time of data collection. Paul summarized the Hadoop ecosystem which supports the collection and processing of such data. He went on to describe several SAS offerings which interact with Hadoop in various ways.

Paul provided his own definition of Big Data as: “the amount of data or complexity that puts you out of your comfort zone”.

Link C. JawLink C. Jaw, Fellow, Intel Corporation talked about “Big Data Analytics Application to Jet Engine Diagnostics”. The immense explosion of information that we have witnessed is not just contributed by a large number of data sources, but also by large amount of data originating from these sources. Together they have created the so-called “Big Data” environment. The challenge of information explosion is how to extract the right information at the right time from the data environment which we live in. The analytic process spots trends and patterns so as to derive predictive indicators. These indicators are then used to make proper recommendations or to take timely actions. Aircraft engines represent the type of machines that are most complicated and safety-critical. He discussed a case study of an aircraft engine diagnostic problem. Engine Monitoring Giving a quick background of the aviation industry, he mentioned various characteristics of machine diagnostic problem, various data elements and algorithms. The problem is posed as a classification problem in machine learning. After experimenting with different algorithms, the best algorithm was to be nonlinear SVM with a hierarchical dimension reduction technique (kernel sliced regression). He also shared results of analytics. The solution approach discussed in this use case is also applicable to various industrial sectors.