Where does work actually get stuck?
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Data Team Ways of Working
Your people are capable. Why does delivering data products still feel harder than it should?
A practical review of how work moves through your BI or data team — from request to delivery, feedback and operation.
DOES THIS SOUND FAMILIAR?
Start with what is actually happening.
- Too much work is in progress at the same time.
- Requests arrive through multiple channels.
- Priorities change without a clear decision process.
- Testing happens late or inconsistently.
- Developers spend more time coordinating than building.
- Recurring manual tasks keep consuming attention.
WHAT WE NEED TO ANSWER
Good work starts with better questions.
Who decides what gets built?
What should happen before development starts?
Which recurring manual tasks should disappear?
Where would clearer working agreements reduce friction?
WHAT I REVIEW
The scope follows the problem.
I do not force every engagement through the same checklist. These are the areas that may matter; the agreed scope determines how deep we go.
Intake and prioritisation
Requirements and definition of ready
Development workflow
Testing and acceptance
Deployment and release flow
Documentation expectations
Monitoring and support
Feedback loops and ownership
WHAT YOU GET
Something you can use after the conversation ends.
The output should help you make a decision, change a practice or move the work forward. It should not exist simply to prove that work happened.
Workflow Map
How work actually moves today, not how the process document says it moves.
Friction Points
Where handoffs, ambiguity or recurring manual work slow the team down.
Decision & Ownership Model
Clearer points for prioritisation, acceptance and technical decisions.
Improvement Experiments
Small changes worth trying before redesigning the whole operating model.
Working Agreements
Practical rules the team can adopt and inspect over time.
HOW IT WORKS
Enough structure to make the work clear. Not so much process that the process becomes the work.
Context
We start with a short conversation about what is happening, what matters and what has already been tried.
Review
I analyse the relevant parts of the solution or operating model at the depth agreed for the engagement.
Prioritise
I organise observations around impact, risk and usefulness — not around how many issues I can find.
Walkthrough
We go through the findings together, challenge assumptions and discuss trade-offs.
Next step
You decide what happens next: implement internally, continue together or stop because the focused engagement was enough.
YOUR TIME COMMITMENT
I keep the client-side time demand deliberately light. A focused assessment usually needs a kickoff conversation, access or materials, and a final review session. We agree the exact involvement before we start.
A GOOD FIT WHEN
This is likely to help if...
- The technical capability is stronger than the delivery flow around it.
- Your team has recurring coordination or handoff problems.
- You want pragmatic DataOps thinking without turning it into a tool implementation exercise.
IT MAY NOT BE THE RIGHT FIT IF
- You need a company-wide agile transformation.
- The main issue is simply lack of delivery capacity.
If the problem is real but the format is wrong, that is useful to know early. I would rather redirect the conversation than force the challenge into the wrong service.
DATA TEAM WAYS OF WORKING
Your people are capable. Why does delivering data products still feel harder than it should?
Bring me the problem, not a polished brief. A few sentences about what is happening and what you would like to change are enough to start.
Tell me what's happeningPREFER TO WRITE?
Send me a message.
You do not need a polished brief. Whether the topic is Power BI, Fabric, AI, team training, mentoring, coaching or simply a challenge that needs untangling, leave a few details and we can start from there.
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