Competitive Advantage with Data? Maybe … Maybe Not

As companies the likes of IBM move boldly forward toward a new future based on big data, there is another side of the data story to consider.

IBM chairman, president and CEO Virginia "Ginni" Rometty has been appearing in public a lot lately. In February, she spoke at the annual IBM investor briefing; in March, it was the Council on Foreign Relations — in both cases espousing a bold new future built upon big data.

At the investors' meeting, Rometty said that big data — defined as data streaming into the world's organizations from cloud, mobile and social networks (and presumably mingled with enterprise data) — will be the basis of competitive advantage for every company, and indeed every industry, for the next decade. This naturally includes IBM, which will be in a state of "continuous transformation" to pursue this opportunity around data.

At the Council on Foreign Relations, Rometty likened big data to our collective next natural resource. Used effectively, she said, it will result in the 21st-century version of the industrial revolution. Data, in other words, will do for our modern era what steam, oil and electricity did for society in the 1800s: Enable business innovation, create prosperity and ensure growth.

A new era notwithstanding, there is another side to the data story that is worth exploring.

During the Q&A portion of Rometty's address, Richard Haass, an American diplomat and president of the Council on Foreign Relations, asked Rometty a frank question that addresses the proverbial elephant in the room: "You don't worry at all that there's a danger in data?" he said. "I was sitting there listening, and I was thinking of what Wayne Gretzky says about, 'you don't skate to where the puck is; you skate to where the puck is going to be.' Can't reams of data get in the way? Doesn't data at some point almost force you inside the box and towards averages?"

Others are addressing this question as well. At the recent O'Reilly Strata Conference, Kate Crawford, principal researcher at Microsoft and a visiting professor at the MIT Media Lab, gave a talk suggesting that big data might not be providing the whole picture. "In fact," she said, "we may be getting drawn into particular kinds of algorithmic illusions."

Crawford provided several reasons for these algorithmic illusions: Biases in data collection, both in how it's prepared and cognitively; exclusions, or gaps, in data signals whereby some people are not represented by data; and the constant need for context in conclusions, whereby small data — asking people how and why, and not just how many — tells a better story than big data.

Using just data to get insights represents a "scary future where we're putting on blinkers and only thinking of the big data answers to our questions," said Crawford. "It represents a set of problems for you, particularly if you're making business decisions based on those decisions, or public spending decisions."

According to Crawford, there is an answer to this big data quandary:

One of the ways forward that I think could address some of these weaknesses in big data is thinking about how we might bring data together with small data — computational, social science along with traditional qualitative methods. Because we know that data insights can be found at all levels, and by bringing together a range of tools we get a much more three dimensional image of what we're looking at and the questions we're trying to ask … For me, this represents the next big challenge: How we move from big data, to data with depth.

In order for organizations to win in this new competitive environment, Rometty said that leaders must embrace three principles that IBM has learned through its own work with big data, and in its work with thousands of companies, governments and cities: First, that data will change how you make decisions; second, that it will change how you create value; and third, that it will change how you deliver value. According to Rometty:

When it comes to decisions, they'll be made on predictive analytics and data. When it comes to creating value, the social network will be a production line. And when it comes to delivering value, it will be the individual; it will not be a segment.

Rometty's perspective on the potential danger of relying too much on data? "My view is it's not an either/or," she said, in answer to Haass's question. "You've got to take data as an input. There will be times when you take it for the answer. But often, data will be to make you think of something different … if anything it's giving you advice … and then you add creativity and innovation to that."

1 Comment On: Competitive Advantage with Data? Maybe … Maybe Not

  • Chris | April 14, 2013

    Great and timely piece. Not only can data give a false picture, it can consume an inordinate amount of resources to gather, analyze and regurgitate into a actionable form.

    In short, I teach my clients to ask the question: Do you know what the conclusion will be in advance of the data analysis project? If so, spend the resources on corrections.

    Chris Reich, TeachU

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