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Power BISelf-Service BITeams

Self-Service BI Reset

You wanted self-service analytics. Did you accidentally create self-service report chaos instead?

A focused reset of how business users, BI specialists and platform teams work together around Power BI.

Start with what is actually happening.

  • Hundreds of reports exist but trusted answers are still difficult to find.
  • Different teams calculate the same KPI differently.
  • Business users have Power BI access but still depend heavily on BI developers.
  • Nobody agrees where self-service ends and governed BI begins.
  • Training exists, but support and ownership after training are unclear.

Good work starts with better questions.

01

Who should build what?

02

Which semantic models should be centralised?

03

Where does business ownership sit?

04

What support does self-service actually require?

05

How much freedom is useful before it becomes fragmentation?

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.

Current self-service adoption

User personas and capability levels

Semantic-model ownership

Workspace model and publishing patterns

Training and enablement

Support and office-hours model

Governance and endorsement

Community and BI-team responsibilities

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.

01

Current-State Diagnosis

Where self-service is helping and where it is creating avoidable friction.

02

Self-Service Operating Model

A practical definition of governed, managed and user-owned BI.

03

Role Boundaries

Clear responsibilities for business users, creators, BI teams and administrators.

04

Enablement Plan

Training, support and community mechanisms matched to user needs.

05

90-Day Priorities

A realistic starting plan rather than a multi-year transformation deck.

Enough structure to make the work clear. Not so much process that the process becomes the work.

01

Context

We start with a short conversation about what is happening, what matters and what has already been tried.

02

Review

I analyse the relevant parts of the solution or operating model at the depth agreed for the engagement.

03

Prioritise

I organise observations around impact, risk and usefulness — not around how many issues I can find.

04

Walkthrough

We go through the findings together, challenge assumptions and discuss trade-offs.

05

Next step

You decide what happens next: implement internally, continue together or stop because the focused engagement was enough.

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.

This is likely to help if...

  • Power BI has scaled beyond one central team.
  • Self-service exists but trust, reuse or ownership are inconsistent.
  • You want to reduce the “report factory” pattern without blocking the business.
  • You only need introductory Power BI training for a small group.
  • Your main issue is technical performance rather than operating model.

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.

You wanted self-service analytics. Did you accidentally create self-service report chaos instead?

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 happening

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.

SIGNALMESSAGECONVERSATION

AI & TRANSPARENCY

AI supports selected parts of my work — it does not replace my judgement, responsibility or relationship with the client.

I use AI tools for tasks such as research, organising information, structuring content and preparing materials. I treat AI as support for my work — not as an autonomous author or decision-maker. Anything I publish or use professionally remains my responsibility.

In coaching, I do not use AI to automatically assess, diagnose or make decisions about a client. AI does not replace attention, confidentiality or human responsibility for the coaching relationship.

AI may support the process. Human responsibility remains.