November 9-12, 2014
2014 INFORMS Annual Meeting
San Francisco, CA
Special ArticlesIBM announces $3 billion research initiative to tackle chip challenges
IBM recently announced it is investing $3 billion over the next five years in two broad research and early stage development programs to push the limits of chip technology needed to meet the emerging demands of cloud computing and big data systems. These investments will push IBM’s semiconductor innovations from today’s breakthroughs into the advanced technology leadership required for the future.Read More
Special ArticlesWSC 2014: Exploring big data through simulation
The Winter Simulation Conference (WSC) has been the premier international forum for disseminating recent advances in the field of system simulation for more than 40 years, with the principal focus being discrete-event simulation and combined discrete-continuous simulation. In addition to a technical program of unsurpassed scope and high quality, WSC provides the central meeting place for simulation researchers, practitioners and vendors working in all disciplines and in industrial, governmental, military, service and academic sectors. WSC 2014 will be held Dec. 7-10 in Savannah, Ga., at the Westin Savannah Harbor Golf Resort & Spa and the adjacent Savannah International Trade & Convention Center.Read More
Special ArticlesINFORMS Annual Meeting in S.F. to bridge data, decisions
The 2014 INFORMS Annual Meeting in San Francisco, set for Nov. 9-12 and whose theme is “Bridging Data and Decisions,” promises to be one of the largest ever, with more than 5,000 technical presentations. Whether you are interested in pressing societal needs, including healthcare, energy and climate change, new developments in supply chain management and logistics, cutting-edge methodologies for optimization, or advances in stochastic processes and risk analysis, you will find hundreds of presentations to match your interests.Read More
Big Data: Generation Next
By Vijay Mehrotra
We have all been hearing about both the “the analytics revolution” and “the rise of Big Data” forever, or so it seems. I credit the book “Competing on Analytics” by Thomas H. Davenport and Jeanne G. Harris [Harvard Business School Press, 2007] with making “analytics” part of the mainstream business lexicon. Similarly, the McKinsey Global Institute (MGI) report entitled “Big Data: The next frontier for innovation, competition and productivity,” released in May 2011, has had the same effect for the term “Big Data.”
This MGI report formally defined Big Data as “datasets whose size is beyond the ability of typical database software tools to capture, manage and analyze,” while also identifying several vertical industries and classes of applications that can be improved by intelligent use of data for better decision-making, innovation and competitive advantage. In fact, many of the broad themes presented in this report echo the ideas presented by Davenport and Harris in “Competing on Analytics.” As such, over the past year, it has become natural to think of “analytics” and “Big Data” as being virtually synonymous with one another.
I caught up with Davenport by phone a couple of weeks ago. He was in the midst of a study on the human side of Big Data sponsored by SAS Institute and EMC/Green Plum, and he was kind enough to share some of his findings with me. Over the past few months, he had interviewed a large number of data scientists who were working in Big Data roles in an effort to understand who they are, where they are working and what they are working on. I found some of his observations insightful and others more surprising.
The data scientists who Davenport had spoken with had academic backgrounds in many different disciplines including physics, mathematics, computer science, statistics and operations research, as well as less obvious ones such as meteorology, ecology and several social science fields. Almost all had Ph.D.s, and in many cases their research had been a catalyst for the development of their deep data skills (Davenport cited one recent Ph.D. cohort of seven applied ecology students, of whom six had launched careers in Big Data, rather than academia, after finishing graduate school).
More surprising, however, was Davenport’s observation that “very few large companies are going to bother with ‘first generation’ data scientists.” While pointing to General Electric as a notable exception, he noted that the vast majority of the data scientists who he had found worked at platform companies such as Facebook, Twitter, Google, Yahoo and LinkedIn and at startup companies such as Splunk  see exciting entrepreneurial opportunities  in creating tools to enable more efficient access, visualization and mining of large streams of data from multiple sources.
“Data management seems to dominate the world of Big Data right now,” Davenport explained. “There’s a huge focus on visualization and reporting among the data scientists I talked to. The statisticians are a little bit frustrated … One of the quips I heard was, ‘Big Data = Little Math.’ ”
His conclusion: today, data-driven managerial decision-making still relies almost exclusively on small-to-medium sized datasets stored in traditional data structures.
I heard some of these same themes at the recent INFORMS Analytics Conference, most notably in a panel discussion on “Innovation and Big Data.” The panelists included Diego Klabjan (Northwestern University), Thomas Olavson (Google), Blake Johnson (Stanford University), Daniel Graham (Teradata) and Michael Zeller (Zementis, Inc).
Very early in the discussion, the panelists all agreed that there’s a huge amount of confusion about what is actually happening in this space today, and that this confusion is being amped up by the massive amount of hype about Big Data (a recent Google search on “Big Data” returns a cool 1,350,000,000 entries, and a quick query on Google Insights for Search reveals that the number of people searching on this term has grown exponentially in the past year ). However, as Northwestern’s Klabjan bluntly stated, “OK, with Hadoop we know how to store Big Data. But doing analytics on top of Big Data? We have a long way to go.”
The discussion often touched on the “volume, velocity and variety”  of today’s data and the accompanying high level of complexity that leads to a variety of challenges in extracting value from it. Teradata’s Graham acknowledged these risks explicitly when he encouraged executives in the audience to (in the words of Tom Peters) “fail forward fast,” while Google’s Olavson urged the audience to not get so caught up in the complexity of the data challenges and the power of the data management solutions that the key business problems slip out of sight.
The panelists often came back around to the human side of Big Data. Zementis’ Zeller envisioned a future in which the work done by the data scientist of today is broken up into a variety of emerging roles such as data technician and data analyst, while Stanford’s Johnson suggested that the democratization of data would create a need for a quality assurance function for not only the expanding mounds of data but also for the analytic models built on top of it. And Olavson’s final comment was that with or without Big Data, analytics is ultimately about enabling smart people to use data and tools to create business value.
Which brings me back to my earlier conversation with Davenport. At several points in our discussion, he drew a clear distinction between the data scientists of today and the “second generation” of tomorrow. Based on his research, Davenport anticipates that “as more and better data management tools come to market, less software development will be needed to work with Big Data.” In this world, a combination of large, unstructured data management skills and analytic modeling capabilities will be a powerful combination.
It will, I suspect, be here before we know it.
Vijay Mehrotra (email@example.com), senior INFORMS member and chair of the ORMS Today and Analytics Committee for INFORMS, is an associate professor, Department of Finance and Quantitative Analytics, School of Business and Professional Studies, University of San Francisco. He is also an experienced analytics consultant and entrepreneur and an angel investor in several successful analytics companies.
REFERENCES, NOTES & FURTHER READING
- To read about Splunk’s recent successful IPO, see http://dealbook.nytimes.com/2012/04/19/splunk-soars-in-debut/.
- See for example http://www.gsb.stanford.edu/news/headlines/entrepreneur-conference-2012.html.
- See http://www.google.com/insights/search/#q=%22Big%20Data%22&cmpt=q.
- The three Vs are a popular foundation for Big Data – for more background on this, see http://radar.oreilly.com/2012/01/what-is-big-data.html.