Doubting Data’s Magic

Loraine Lawson

I’m having a hard time believing some of the claims I see about data — particularly Big Data. And the sad thing is, I’m a huge proponent of its potential.

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Four Steps to a Big Data Strategy

But it’s hard not to feel a bit cynical when you see stories like this, claiming Big Data “has the potential right now to produce a smarter, more efficient government that could cut the $3.54 trillion federal budget by 14 percent, freeing up an extra $500 billion per year.”

Really? Five hundred billion dollars per year?

I’m skeptical. Frankly, of the predicted areas of savings — managing transportation infrastructure; fighting fraud, waste and abuse; and executing military, intelligence, surveillance and reconnaissance missions — only that last one even sounds convincing, since government waste tends to be defined largely from a political perspective. Heck, some say even the data can be biased.

And it’s not as if the government has been totally clueless about identifying potential savings in these areas — without Big Data.

Yet, overspending persists. So it’s not as if knowing where the waste is results in actual cost savings.

But this is not a political blog and that’s not my point.

My point is we’re at a dangerous intersection with data: On one hand, we’re still pretty clueless and inept, and on the other, we’re hopelessly optimistic about its potential. This is always a dangerous crossroads for IT and CIOs because, frankly, it’s where everyone engages in magical thinking that gets us in trouble with hard facts like ROI and TCO on down the line.

On one hand, you read about $500 billion in federal savings per year and learn how data science dictates what we eat and that’s just amazing. Who doesn’t want some of that pixie dust? If only we had the right tools, the right data sets, Hadoop, a few integrated social media feeds, and a horde of data scientists — what couldn’t we do?

Well, here’s one thing you won’t be able to do: Create change. And you really don’t have to look farther than the U.S. federal government to know that. Is there any organization in all of history that has collected more data on itself, engaged in as much self-reporting, or been more studied and analyzed than our government?

Yet despite all the data, all the analysis, it remains incredibly, annoyingly and, yes, sometimes most fortunately, resistance to change.

There are some dire warnings about data-driven enterprises, but for most people and organizations, I think the opposite will be true: Data analysis alone will not be enough.

“Never make the mistake of assuming that the results will ‘speak for themselves,’” writes Tom Davenport, an IT and management professor, research fellow at the MIT Center for Digital Business, and co-author of “Keeping Up with the Quants” and “Competing on Analytics.”

In a recent Harvard Business Review blog post, Davenport points to a recent McKinsey Global Institute report on Big Data that found Big Data will create a need for over 1.5 million more data-savvy managers.

But managers don’t need to be able to tinker with data — they just need to be able to “become better consumers of data, with a better appreciation of quantitative analysis and — just as important — an ability to communicate what the numbers mean,” he writes.

Taking a strategic step back, Davenport looks at what needs to happen after we’ve created this data-rich world. His examples speak to marketing more than management, IMHO, but I really liked the framework created by George Roumeliotis, who heads a data science group at Intuit.

Roumeliotis insists each analysis include answers to six questions:

  1. My understanding of the business problem
  2. How I will measure the business impact
  3. What data is available
  4. The initial solution hypothesis
  5. The solution
  6. The business impact of the solution

Data isn’t magic, and neither is data analysis. But if there is magic — if we’re going to point to some undefinable power that creates something we can’t quite quantify or scientifically explain — then this is where you find it: By understanding and explaining what the data means in such a way that something even more mythical is created: change.

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