There is probably no shortage of important work to do (speaking from experience here!! 🙋♀️)
The challenge is deciding what gets attention when everything feels urgent.
Where is the ROI on that AI work? Can we get a dashboard for this before the exec review? Why doesn’t this number match what finance is reporting? Can we pull in this new data source by next week?
The requests don’t slow down.
Without a clear way to manage that tension, teams fall into a pattern of reacting instead of progressing.
That is when the work starts to lose its connection to the outcomes it was meant to drive.
When the work gets done, but the outcomes don't follow, you have an ownership problem
Your team is not idle. Dashboards get updated, pipelines run, and tickets are addressed.
But the work that moves the business forward keeps slipping.
A new use case sits in the backlog because no one has time to scope it. A reporting issue gets patched more than once instead of fixed properly. Priorities shift depending on who asks or what breaks.
Everyone is working. Still, nothing feels finished.
This is what it looks like when execution has no clear owner across the data lifecycle. Intake, prioritization, and delivery operate in silos, so work may start easily but it will often fail to result in measurable outcome.
What to do next:
Create one path for intake and prioritization. When requests enter from more than one place, you’ll lose control. A clear entry point is the first step to deciding what gets prioritized.
Align every request to a business outcome. If the impact is unclear, pause. Clarity at the start prevents rework later.
Make ownership extend beyond delivery. The work is not “done” when it is built. Someone needs to carry it through to adoption and results.
Once you put structure around how work gets defined, prioritized, and carried through, progress becomes easier to see and harder to lose.
So... who's the owner?
As us consultants like to say, ‘It depends!’ 😉
In some cases, ownership gaps point to a structural issue. In those cases, we help companies rethink how their data teams are organized: identifying missing roles, clarifying responsibilities, and aligning teams to drive meaningful progress against their data strategy.
"When work is siloed between BI and data engineering, you see conflict occur. Whose responsibility is it to handle this?
The business doesn't know nor care about the distinctions between data engineering versus BI development. They need to be able to go to one team and say, 'Hey, this is the issue', and be assured that the team is going to resolve the issue."
-Travis LaMont, Program Director
Factors to think about as you define ownership of data outcomes at your org:
Your operating model, team structure, capacity, and data maturity. There’s no one-size-fits-all approach, but there is a right answer for your organization.
