I should have known a Harvard Business Review spotlight on big data was imminent. Just a few weeks ago, I noted in an HBR review column that “It’s been over a year since I’ve blogged on anything from the Harvard Business Review. For business intelligence, big data and analytics relevance, the more tech-focused MIT Sloan Management Review’s probably a better bet at this point.”

Well, lo and behold, the October, 2012 edition is on “Getting Control of Big Data.” I suppose I can take consolation from the fact that the authors of the article, entitled “Big Data: The Management Revolution,” are Andrew McAfee and Erik Brynjolfsson, from MIT’s Center for Digital Business – and often write for the SMR.

In a foundation article, “The True Measure of Success,” Michael Mauboussin warns that many companies aren’t even ready to properly exploit big data and analytics, instead cavalierly using the wrong performance metrics that have “only a flimsy connection to the objective of creating shareholder value.” He cites Michael Lewis’s “Moneyball” illustration of the superiority of team on-base percentage to batting average as a predictor of runs scored. The author goes on to compare business executives to baseball scouts who “have a gut sense of what metrics are most relevant to their businesses, but they don’t realize that their intuition may be flawed and their decision making may be skewed by cognitive biases.”

Mauboussin espouses a variant of the scientific method as the foundation of business hypotheses:

  1. Define the governing objective
  2. Develop a theory of cause and effect to evaluate the hypothesized drivers of value
  3. Identify the specific activities that lead to the governing objective, and
  4. Reevaluate the chosen metrics to ensure that they continue to link with the governing objective.

His advice on metrics that reveal cause and effect? They “have two defining characteristics: They are persistent, showing that showing that the outcome of a given action at one time will be similar to the outcome of the same action at another time; and they are predictive – that is, there is a causal relationship between the action the statistic measures and the desired outcome.” Seems an awful lot like the Science of Business thinking I’ve discussed on several occasions.

Even in HBR, MIT’s McAfee and Brynjolfsson take center stage with their article: “Big Data: The Management Revolution.” Unlike many authors, M&B distinguish big data from analytics, citing the three V’s of volume, velocity and variety that challenge business. At the same time, “there’s a huge amount of signal in the noise, simply waiting to be released. Analytics brought rigorous techniques to decision making; big data is at once simpler and more powerful … As Google’s director of research, Peter Norvig, puts it: ‘We don’t have better algorithms. We just have more data.’”

The Center for Digital Business, in collaboration with McKinsey and others, set out to systematically test whether using big data intelligently improves business performance. They correlated findings of structured interviews with executives from 330 public companies on technology and organizational management practices with independently-gathered financial performance data. Their findings? “The more companies characterized themselves as data-driven, the better they performed on objective measures of financial and operational results.”

For M&B, a “big” challenge with big data is the momentum of executive management decision-making through intuition – the so-called “HiPPO” or highest-paid person’s opinion. To evolve from HiPPO to data-driven decision making, management must adapt to instinctively ask questions like “What analyses were conducted?” and “What do the data say?” They must also acknowledge when data wins. “Few things are more powerful for changing a decision-making culture than seeing a senior executive concede when data have disproved a hunch.” In those organizations, the role of experts will evolve from those providing answers to those adept at asking questions.

The authors propose that organizations looking to “go data” take on a proof of concept that sequences as follows: 1) Choose a business unit with a quant-friendly leader; 2) Identify big data opportunities that can be prototyped within 5 weeks; 3) Adopt a “science of business” methodology that includes hypotheses, experiments, measurement and replication; and 4) “Open-Source” the challenges to benefit from expertise outside the organization.

Once the organization’s on board with the concept of data-driven management, the authors admonish to recognize that significant hurdles remain. A first critical area is Leadership that combines opportunism, vision, effective management and customer-centricity with the new science of business approach. Another key consideration is changing Company Culture from “what do we think?” to “what do we know?” A third critical consideration is Talent Management, especially as it pertains to the acquisition and development of data scientists “comfortable speaking the language of business and helping leaders reformulate their challenges in ways that big data can tackle.”

In summary, McAfee and Brynjolfsson are not timid framing the future of big data in business: “The evidence is clear: Data-driven decisions tend to be better decisions. Leaders will either embrace that fact or be replaced by others who do.”

Next week I’ll discuss two other excellent articles from the big data spotlight: “Data Scientist: The Sexiest Job of the 21st Century” and “Making Analytics Work for You.”

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