We cheat when we measure.
pre-s: Do you have a clear purpose and strategy for your data team and how it fits in your organization?
Next week on Sept 10th, I’m hosting a 4-hour workshop to give you the confidence and direction you need to lead with data. You will walk away with a clear purpose for your data team and a vision for how to prioritize and execute.
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A common pattern data leaders follow when trying to measure their goals and success…
They cheat.
We say our goal is to be a trusted data team. But we end up measuring data quality.
We say our goal is to empower a data driven organization. But we end up measure how many times a user views a report.
We say our goal is operational efficiency. But we end up measuring pipeline runtimes.
We say our goal is self-service analytics. But we end up measuring the number of reports created by users.
The stated goal is well thought out. Purposeful. Likely came from a few hours of strategic meetings.
So why do the actual measurements fail to live up to the goal?
You assume you can’t measure the real thing. You think it’s too abstract or intangible to measure “trust”, “being data driven”, or “self-service analytics”
So you substitute a cheap imitation. But in the process, you short-change your real objective for a metric your team will cut corners to beat.
If the goal is for the organization to trust the data, then measure trust - not a substitute for trust.
If the goal is to increase self-service analytics, then measure self-service analytics - not a substitute for self-service analytics.
This will force you to ask better questions. Design better measurements. And ultimately, optimize for the right things on your team. Don’t cheat.
If you think you have a team goal that can’t be measured, hit reply and tell me about it.
I’m here,
Sawyer
