There’s a simple test to gauge your organization’s level of data maturity:
Can you easily cite recent examples in which data were the driving factor behind…
If so, it means that trust in the data is high enough to change someone’s mind (and it regularly does). It also means that initiatives are intentionally based on a hypothesis that everyone agrees could go one of several ways and there’s a plan to measure and iterate.
If you have piles of data but struggle to point to specific course corrections they drove, it might be worth reflecting on whether your data use is primarily operational or if it creates real insight.
Using data to support operations and keep the train on the tracks is hugely important, and I’m not discounting that value. I also want to be clear that there are plenty of good reasons not to make data-driven decisions! The top reason is simply qualitative—we do X because it’s the right thing to do and we don’t need to measure it to know that. This is absolutely valid. Sketchy data quality or high level of effort to gather valid metrics are other good reasons to be skeptical of relying too much on those ones and zeros in your computer.
On the other hand, if you’re putting in a lot of time and effort to collect data that are not used operationally, there’s no ROI on that work until you act on it. An obvious barrier to action is concerns about data quality, but framing decisions in a way that supports a data-driven answer is equally important and can be a significant challenge as well.
Questions that can help improve how your decisions are presented:
There’s a difference between looking for information to support the direction you’re already headed versus changing course because of insight from data.
The first is easy; the second indicates maturity.