Data Minus “Data”

via Garfield Minus Garfield

Data can be beautiful. Data can help us understand each other better. However, the more I’m impressed by actual data, the more I find myself wary of the word itself. The word “data” is used to refer to information of widely varying scope and reliability. It is also used to describe conclusions drawn from that information, and visual representations of that information. In its common and colloquial use, the word “data” does little to clarify what it’s actually describing, while handily imparting an air of certainty and rigor. The word “data” is dangerous for the very reason it’s useful: it wields authority without specificity.

To that end, I’ve found myself instituting a new rule when discussing data strategy and projects: don’t use the word “data.” If you’re discussing a particular set of information, describe that specific set of information. If you’re discussing conclusions you’ve made based on that information, describe those specific conclusions and how you reached them.

Take, for example, this somewhat-hypothetical sentence: “Our data shows that millennials are highly receptive to our value proposition.” Now imagine rephrasing this as “the email survey we conducted shows that millennials are highly receptive to our value proposition.” There are still many points here that need clarification. (What is the value proposition? How does the email survey show this?) But at the very least, this rephrasing opens up a more meaningful conversation about what information was gathered, how it was gathered, and how it is being interpreted.

To use a more general example, imagine replacing the overused and often misapplied phrase “social data” with something more specific and descriptive like “sentiment analysis conducted on our customers’ Tweets.” The latter phrasing seems to invite more questions, but these are the very questions that make information both accessible and actionable. The absence of the d-word makes it easier to distinguish information from assumption, to have an informed conversation about methodology, and to set clear and reasonable expectations.

In other words, data can be even better without “data.”