Why Spreadsheets Make Bad StrategiesRoger L. Martin
Posted on Harvard Business Review: January 11, 2010 4:07 PM
We live in a world obsessed with science, preoccupied with predictability and control, and enraptured with quantitative analysis. Economic forecasters crank out precision predictions of economic growth with their massive econometric models. CEOs give to-the-penny guidance to capital markets on next quarter's predicted earnings. We live by adages like: "Show me the numbers" and truisms such as "If you can't measure it, it doesn't count."
What has this obsession gotten us? The economists have gotten it consistently wrong. As late as the first half of 2008, no prominent macroeconomist or important economic forecasting organization predicted that the economy would not grow in 2008 (or 2009), let alone that it would crater as disastrously as it did. But, undaunted, the same economists who totally missed the recession turned back to the same quantitative, scientific models to predict how the economy would recover, only to be mainly wrong again. CEOs keep on giving quarterly guidance based on their sophisticated financial planning systems and keep on being wrong—and then get slammed not for bad performance but for their failure to predict performance exactly as they promised mere months earlier.
In this oh-so-modern life, we have deep-seated desire to quantify the world around us so that we can understand it and control it. But the world isn't behaving. Instead, it is showing its modern, scientific inhabitants that quantity doesn't tell us as much as we would wish. While the macroeconomists would dearly love to add up all the loans to provide a total for "credit outstanding" and then plug this quantity into their economic models to be able to predict next year's Gross Domestic Product, they found out in 2008 that all of those loans weren't the same—some, especially the sub-prime mortgages, weren't worth the proverbial paper on which they were written.
And CEOs and their CFOs would love to be able to extrapolate last month's sales quantity and predict next quarter's sales, but sometimes they find out that those sales weren't as solid a base for growth as they might have thought—especially if some of the customer relationships underpinning them weren't as strong as they might have imagined.
The fundamental shortcoming is that all of these scientific methods depended entirely on quantities to produce the answers they were meant to generate. They were all blissfully ignorant of qualities. My colleague Hilary Austen, who is writing a fantastic book on the importance of artistry, describes the difference between qualities and quantities in the latest draft:
Qualities cannot be objectively measured, as a quantity like temperature can be measured with a thermometer. We can count the number of people in a room, but that tells us little about the mood—upbeat, flat, intense, contentious—of the group's interaction.
Why are qualities so important? We need to understand the role of qualities in dealing with the complex, ambiguous and uncertain world in which we live because understanding, measuring, modeling and manipulating the quantities just won't cut it. Adding up the quantity of credit outstanding won't tell us nearly enough about what role it will play in our economy. Adding up sales won't tell us what kind of a company we really have. We need to have a much deeper understanding of their qualities—the ambiguous, hard-to-measure aspects of all of these features.
To obtain that understanding, we need to supplement the quantitative techniques brought to us through the march of science with the artistic understanding of and facility with qualities that our obsession with science has brushed aside. We must stop obsessing about measurement so much that we exclude essential but un-measurable qualities from our understanding of any given situation. We must also consider the possibility that if we can't measure something, it might be the very most important aspect of the problem on which we're working.
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