That question is not quite as simple as it sounds. At a high level, most would probably agree that the purpose of culture data is to help you see something about the culture that you couldn’t see (or articulate) before you gathered the data. If the data tell me only what I already knew quite clearly, then they didn’t really serve a purpose, other than, perhaps, a confirmation. But honestly, if you’re going through the effort of gathering culture data, I’d hope you would get more than a simple confirmation.
But the purpose of “seeing what you couldn’t see” is not quite clear enough either, because there are many different directions you can go with that. Do you want to see what people LIKE about your culture? Do you want to see what frustrates them? Or, as is the case in our work, do you want to see the patterns? Do you want to understand how your people experience the culture—what is valued, and where might there be contradictions or reinforcing loops that are having an impact on performance?
We think there is much more value in going after the patterns, as opposed to seeking the sentiment-based analysis (like/dislike), but to get to the real purpose of the culture data, I would still go one layer deeper. It’s important to understand that the data themselves will never give you the complete picture. They will help you see things, and they will provide powerful insights, but that is all in service of you—the humans inside your organization—figuring out what to do about it. The data start the conversation; they don’t end it.
Frequently, however, people really want the data to end it. They want a conclusion. They want proof. We’ve had clients (particularly those in the research field) that were frustrated because the results of our culture assessment didn’t conclusively prove as much as they were hoping they would. But that was not the purpose of the data. The purpose of culture data is to shine a light that leads to those conversations you’ve been missing that leads to concrete changes that improve performance.
The data play a critical role in that chain of events, but if you misunderstand what that role is, you can easily get off track.