Analyze This!: Are analytics and big data overhyped?
By Vijay Mehrotra
As described in the previous edition of Analyze This!, I am currently working on a research study with Jeanne Harris at Accenture’s Institute for High Performance. Specifically, we are seeking to develop a quantitative and qualitative understanding of the current state of analytics practice. If you have already completed the on-line survey, please accept our gratitude (extra kudos to those who have sent us emails with additional thoughts and ideas).
For the rest of you, it is most definitely NOT too late: just click here to provide your valuable input into our research process. Full disclosure: Based on the first wave of respondents, the mean time to complete the survey is just under 15 minutes.
In our study, we are trying to practice what we preach by gathering and analyzing data to empirically test some of our hypotheses about what we think is going on. Meanwhile, the “big data” and “analytics” hype is almost deafening. In February 2013, Gartner changed the name of their celebrated Magic Quadrant from “Business Intelligence” to “Business Intelligence and Analytics Platforms” , while formally including predictive modeling, data mining and interactive visualization into its evaluation criteria. Over at LinkedIn.com, there are more than 2,500 analytics-related groups, including eight with more than 20,000 members and 30 with more than 5,000 members. While less than five graduate programs in analytics existed in 2011 in the United States, there will be at least 17 such programs as of fall 2013 . Meanwhile, analytics-oriented headlines seem to crop up every day in publications as diverse as InfoWorld , the New York Times , Atlantic Monthly  and Forbes .
What Really Matters
All of this was on my mind recently when I happened across a May 2003 Harvard Business Review article entitled “IT Doesn’t Matter,” written by Nicholas Carr (who later expanded it into a book ). Carr’s basic thesis was that despite all of the hype, information technology actually no longer provided its adopters with competitive advantage. Placing IT along a continuum of innovations such as steam shovels, trains and electricity, Carr argued that the economics of such technological breakthroughs – and in particular, the need for those that developed them to sell them widely in order to recoup their development costs – meant lower prices and rapid commodification.
Given the visibility of the article and its author (Carr was an editor-at-large at HBR at the time) and its provocative title, the article was met with a firestorm of criticism. Several letters to the editor were subsequently published. Steven Alter, who is currently a colleague of mine, thoughtfully pointed out that information systems are merely a component of what he calls “work systems” ; because IT is an essential component of most modern corporate work systems, it did indeed matter a great deal.
Others responses were less sanguine. Gartner executives Marianne Broadbent, Mark McDonald and Richard Hunter warned that, “the danger is that by scanting the fantastic potential for innovation that lies ahead in IT, Carr will lead executives to focus only on controlling IT costs.” Paul Strassman, former CIO of General Foods and Kraft, wrote of Carr that, “he bases his conclusions entirely on his reasoning, by analogy…any proof that rests entirely on analogies is flawed. This technique was used to uphold medieval dogma, and it delayed the advancement of science by centuries.”
New Album, Same Band
After reading this article and the associated letters, a number of thoughts struck me:
- One reason that the hype surrounding big data and analytics sounds so familiar may be that many of the same players from the last revolution are also a part of this one. For example, see Accenture, Gartner and InfoWorld above, as well as companies like IBM and Oracle that have been aggressively acquiring analytics companies over the last few years. Yeah, the band’s definitely got a new album out, but there’s no mistaking the sound.
- Software vendors, new ones as well as old, are working furiously to bring analytics software products to market. But these vendors should beware of making extravagant promises that they are likely to fail to deliver on. As John Seeley Brown (former chief scientist at Xerox PARC) and John Hagel III (former McKinsey principal) pointed out in their response to Carr’s article:
“Rather than help companies understand that IT is only a tool, technology vendors have tended to present it as a panacea. ‘Buy this technology and all your problems will be solved.’…When the anticipated results did not materialize, the backlash began to gather in executive suites … ‘let’s buy as little as we can and squeeze the vendors as much as we can.’ ”
- Along these same lines, the new offered wisdom is that the real value of previous IT expenditures (many of which have heretofore been viewed as bad investments) is the data they are providing to support analytics. Part of the sales pitch for analytics and big data today is that managers and executives need to jump on the big data train in order to capture those long-ago promised benefits. In Vegas terms, they are being asked to “double down.”
- In the end, we are probably best off to think of IT, big data and analytics as part of a classical supply chain whose mission is to capture, store and analyze data and then interpret, communicate and utilize the results to deliver business insights, make better decisions, and ultimately achieve increased profitability with decreased risk. Regardless of where the media might be focused at any given time, the results largely depend on the capability of the weakest link.
- Indeed, another key (and rarely mentioned) factor in the increased acceptance and utilization of analytics in the business world is the ascendance of a new generation of managers and executives – a generation that has grown up with computers and is more comfortable with what they can and cannot do.
Whoops! That last paragraph sounds like more big data hype! Please note: This claim about more enlightened managers and executives is just a hypothesis, one of several that we seek to examine in the research project that I talked about at the top of this column. To test these hypotheses, we need data! Please fill out our survey and help us to better understand what’s really going on out there in the world of analytics. We promise to share our results with all of you, both here and elsewhere, to help you cut through all the noise, which seems to be getting louder every day.
Vijay Mehrotra (firstname.lastname@example.org) is an associate professor in the Department of Analytics and Technology at the University of San Francisco’s School of Management. He is also an experienced analytics consultant and entrepreneur, an angel investor in several successful analytics companies and a longtime member of INFORMS.
- Click here for more details on the Magic Quadrant.
- For a listing and additional details about graduate programs in analytics, click here.
- “Why You Should Jump Into Big Data” is accessible online. To view it, click here.
- “The Origins of ‘Big Data’: An Etymological Detective Story” is accessible online. To view it, click here.
- “Can Big Data Save American Schools?” is accessible online. To view it, click here.
- “Revolution in the Big Data and Business Landscape” is accessible online. To view it, click here.
- “Does It Matter” is available through Amazon. For details, click here.
- For more, see Alter’s book, “The Work System Method. For details, click here.
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