2015 INFORMS Conference on Business Analytics and Operations Research
Huntington Beach, Calif.
CORS - INFORMS Joint International Meeting
Le Centre Sheraton, Montreal
INFORMS Healthcare 2015
2015 Analytics Applications Summit
Harrisburg University of Science and Technology, Harrisburg, Pa. (Free event)
Analytics Section of INFORMS NewsConference Updates
Healthcare Conference: Section Past President Don Kleinmuntz and Treasurer Tarun Mohan Lal are organizing an analytics cluster of sessions/presentations at the upcoming INFORMS Healthcare Conference in Nashville, Tenn., July 29-31. The cluster will have speakers from several healthcare provider organizations and vendor groups.Read More
Analytics Section of INFORMS NewsSave the Date for CAP Information Session
A Certified Analytics Professional (CAP) event will be held April 13 during the 2015 INFORMS Conference on Business Analytics & Operations Research in Huntington Beach, Calif. Section members, along with other conference attendees, are encouraged to attend this informative session to better understand the value a certification can bring to a career.Read More
Analytics Section of INFORMS NewsINFORMS Professional Colloquium
The INFORMS Professional Colloquium (IPC), a one-day program set for April 12 in conjunction with the 2015 INFORMS Conference on Business Analytics & Operations Research, enhances the experience for a select group of 20 students (mostly Ph.D.s, a few MS and MBA students) who are near graduation and who want to enter industry rather than academia. The colloquium will cover real-world case studies, job searching, a “day in the life of an O.R. professional,” and other useful topics, capped by a reception with the students and sponsors prior to the job fair receptionRead 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 (firstname.lastname@example.org), 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.