OKR Check-In Templates · 7 min read

The Data Team OKR Check-In Template I Use (Trust and Decision Signals, Not Dashboard Counts)

I've run OKRs for data teams for about a decade, and the check-in is the part everyone gets wrong. Most data check-ins turn into a status report: "we shipped four dashboards, two pipelines, one model." Nobody can tell from that whether the data is trusted or whether anyone changed a decision because of it. So I built a check-in that asks about trust and impact first, and counts artifacts last. This is the template, the scoring, and how I actually run it.

By Max Bondarenko · Last updated June 2026

The template, up front

Here's the whole thing before any explanation. Five questions, asked the same way every week, answered async by the person who owns each key result. The point isn't to list what the data team built. It's to surface two things a status update hides: do people trust the numbers, and did the numbers change a decision. If your check-in can't answer those, you're running a delivery log, not an OKR.

Why each question is there

Question two is the one I fight for hardest. A data team can ship a flawless model and move nothing. I track a metric I call decision coverage: the share of recurring business decisions that are backed by data the team produced. On one team I ran, that started at a 40% baseline and we set a target of 75%. Forcing every check-in to name a real decision that changed killed the habit of reporting dashboard counts inside a month. If nobody acted on the work, the honest answer is "nobody," and that's the most useful sentence in the whole update.

Question three exists because trust is the data team's actual product, and it erodes quietly. One wrong number in a board deck and three months of credibility is gone. So I ask where trust broke every single week, even when the answer is "nowhere." Pipeline uptime lives here too: a key result like uptime from a 97% baseline to a 99.9% target isn't an infrastructure vanity stat, it's the floor under whether anyone trusts the morning dashboard. When uptime slips, trust slips a week later, and the check-in is where you catch it before the stakeholder does.

Question five is the cheap insurance. Data targets are guesses dressed as numbers, and you usually set them before you've seen the data quality you're actually working with. The whole reason I make people stare at the target weekly is so we revise a wrong one early, in week 4, not week 12. I'd rather move a target from 75% down to 60% in February with a clear reason than limp toward a number nobody believes and write a sad retro in April. Revising early isn't failure. Pretending in week 11 is.

Scoring: three states, one action each

I don't use 0.0 to 1.0 grades on a data check-in. They invite false precision and endless debate about whether something is a 0.6 or a 0.7. Three states is enough, and every state comes with exactly one action so the check-in produces a decision instead of a feeling. The state is about the trend and the trust, not just the raw number.

How I actually run it

Async by default. Every key result owner posts written answers to the five questions by Tuesday noon, in a thread the whole team can read. Writing forces clarity that a standup never does, and "nobody acted on this" is much harder to mumble past in text. No status meeting for the green ones. The only thing that earns a live session is an "at risk" item, and that's a focused 20 minutes with the owner and the affected stakeholder, not the full team staring at a screen.

Who attends the async thread: the data team plus the one or two business owners who consume the work, because their answer to "did this change a decision" is the answer that counts, not ours. When to skip entirely: the week a quarter opens and the week it closes. Opening week the numbers haven't moved, closing week you're writing the retro. Every other week, run it. The check-in is where data OKRs live or die, and the teams that skip it always find out in the retro that they spent a quarter shipping things nobody used.