The scoreboard shapes the work
What a team measures eventually becomes what it optimizes for.
Many Analytics teams measure their work through activity:
Dashboards shipped
Tickets closed
Questions answered
Analyses completed
Those measures may be useful when a team is diagnosing a specific operational problem. They are not measures of value.
A team can complete more requests, build more dashboards, and answer questions faster without improving the business.
I have always believed that this is the wrong scoreboard.
If Analytics exists to help the business make better decisions and improve measurable outcomes, its scorecard should reflect that purpose.
At DoorDash, we did not define the value of Analytics by the volume of work the team produced. We focused on whether the work improved how the business understood a problem, made a decision, took action, or achieved an outcome.
I would rather see one piece of work change an important decision than twenty pieces of work be delivered and forgotten.
The question is not: “What did the team produce?”
It is: “What changed because of the work?”
The Analytics impact ladder
Not every project will have a clean, causally attributable business impact.
But every important project should be explicit about the type of impact it is intended to create and, where possible, estimate the size of the impact.
I evaluate Analytics impact across four levels.
Acknowledgements: The ideas and writing are mine, but they’ve been shaped by current and former teammates who challenged my thinking and helped evolve how we built and ran the Analytics team. Special thanks to my Chief of Staff, Anita Chan, for brainstorming, editing, and AI wizardry on the images, and to ChatGPT and Claude for serving as editorial critics.
I love the idea of estimating incremental impact based on the team's size year over year. How would you measure impact for an analytics team that becomes part of a new org (through a reorg) which doesn't have a data function 🤔? This is a more of a greenfield type of scenario