Amidst all the (justified) hoopla over "big data" right now, I'm a little afraid that managers will overlook some important "small data" messages. Let me offer three in particular: a) you don't need a lot of data to be more successful; b) you don't necessarily have to do the analysis yourself; and c) you can benefit from data-based decision-making at the smallest level of the organization.
The first of these was underscored for me as, for the first time in my management research and writing, I found myself focusing on a small business case. If you've read any of my most recent posts here, you already know about the new book I've been working on with Brook Manville called Judgment Calls: Twelve Stories of Big Decisions and the Teams That Got Them Right. (Great news: it's now available.) It's about organizations that have displayed a consistent ability to make good decisions, or good "organizational judgment" as we like to call it.
Let me quickly point out that we don't think good organizational judgment is all about data wrangling, big or small. Although I have written a lot on that topic in the past, even I know that you need multiple types of interventions to make decisions better. But in our writing partnership, Brook took the lead on chapters that relate to sound decision processes and enlightened leadership, while I, in my usual pattern, focused primarily on organizations that have invested in technology, data, and analytics.
The small business I studied, WGB Homes, is a residential real estate developer in the Boston exurbs. Its challenge was what to do about a house that wasn't selling. In solving it, by surveying people who were knowledgeable about the house and the market, CEO Greg Burrill was hardly capitalizing on big data. To my knowledge he didn't even create a bar chart of the data he gathered, or attempt to determine its average. But there is little doubt that his informal listening to the market was helpful in modifying and selling the house.
If that case taught the first of my mini lessons about small data—that you don't need a lot of data to be more successful—then another chapter I researched taught the second: that you don't necessarily have to do the analysis yourself. Partners Healthcare, a Boston-based academic medical center, has done a fantastic job of embedding data-derived knowledge into medical practice through the use of technology. But the interesting thing is that, for the most part, Partners didn't need to gather and analyze all the data itself—though Partners has plenty of it and is working on harnessing it for decision-making. The institution could also draw upon a worldwide scientific community that has been analyzing data to determine the best treatments for sick patients. What Partners has created is something often called "translational medicine"—taking existing research and putting it into daily practice.
Finally, we included one case from another industry that is dramatically changing how it makes decisions: the K-12 education sector. The Charlotte-Mecklenburg school system in North Carolina is one of the most aggressive adopters of student performance data. As in all implementations of data-based decisions, senior management plays a strong role, and then-Superintendent Pete Gorman clearly led the charge. But I also visited and wrote about inspired people in a particular school in the district, and they were undertaking their own data-based transformation ahead of what the district was doing. Their analysis of reading data, for example, was helping kids to learn how to read faster, and led to more teaching of reading in kindergarten. Lesson learned: you can benefit from data-based decision-making even at the smallest level of the organization.
So you don't need big data, or even big support from senior management, to foment your own revolution in organizational decision-making. With small data to be found everywhere, there is no excuse not to improve your own judgment calls.
This blog first appeared on Harvard Business Review on 03/26/2012.
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