If you need someone to blame this on, it might as well be Rene Descartes. The 23-year-old Descartes was serving in the army when was visited in a dream by the Spirit of Truth who told him that “Conquest of nature is to come through number and measure.”
Numbers were power. That effect was amplified during the Industrial Revolution. That’s when the engineers took charge, measuring and calculating. Soon, Frederick Taylor and the efficiency experts showed up with their stopwatches and clipboards. Now we’re in the Digital Age, where computers spit out numbers by the mountain load.
Today, companies trumpet the claim that they’re “data-driven.” The Economist proclaims that “data is the new oil.” If there is a golden calf to worship today, it’s probably digitized.
We love numbers so much that we don’t think about where they come from or how we’re using them. We can summon them from our vast databases, manipulate them, and turn them into equations that give us “answers. It makes us feel like we’re in control. We’re not, really. We’re only in control of the data.
The map is not the territory and data is not reality
Data is not reality. At best, data can only represent reality. Reality is complex and messy and we can use data to simplify parts of it so we can understand it better. To do that we must leave out part of reality, assign numbers to things that aren’t inherently quantifiable, and approximate relationships with equations.
If, after all that, we treat data like reality we commit what Alfred North Whitehead called “the fallacy of misplaced concreteness.” We get lost in the wonder of our calculations and think we’re describing the elephant, when we’ve only got hold of one leg.
It’s a good idea to apply George Box’s observation about models to our data. All are flawed, but some are useful.
Quantitative data is not objective
No matter what you or your boss thinks, quantitative data is no more objective than qualitative data. Someone, somewhere, sometime decided what would be counted and tracked and what would not. Someone, somewhere, sometime decided how and how often data would be gathered and how it would be presented.
That’s obvious when you talk about qualitative data. We usually get qualitative data in the form of a story. This happened when we observed it this way. With quantitative data, the questions, assumptions, and decisions that lie behind the data are usually behind the curtain and invisible to the people who receive and use the data.
Dig into the history of things to find out why you use certain measures and not others, how the raw data is gathered and manipulated, and why it is presented in the way that it is.
Quantitative data is not enough
Quantitative data is important, it’s just not enough for a successful business or a satisfying life. The most important things in life and business can’t be counted or calculated. Relationships drive much that happens in business. More than half a century ago, Mason Haire demonstrated that emotions influence buying decisions of all kinds. Knowledge workers trade in conversations and tacit knowledge.
There’s one more thing about quantitative data. It’s easy for us to manipulate and “understand” quickly, so we’re likely to pay attention to what we can count and ignore what we can’t. That’s part of the reason why the long term is often sacrificed to the short term and why numerical accounting data gets more attention than “soft” human stuff. As one friend of mine said years ago, “When the pressure’s on to make the numbers, people almost always take a hit.”
Quantitative data is important. You can’t run a successful operation today without paying attention to it. Remember that quantitative data is always a flawed representation of reality. Look behind the curtain to discover the whys and hows behind the data. Remember that human choices drive quantitative data as much as qualitative data. And, please, remember that the most important things in life and business cannot be force-fit into a dataset.