Business intelligence & dashboards
Tableau and Power BI reporting that people use, from first dashboard to a governed self-service estate. Design, build, rollout and handover.
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Data · AI · Delivery
Turning messy business problems into practical data products.
Vawnai is an independent consultancy led by Chris Vaughan. I help teams get trustworthy reporting, sensible AI adoption and a delivery model that actually holds up, whether you are building your first dashboard or untangling a decade of reporting sprawl.
The usual picture
Plenty of data. Not much confidence in it.
Most organisations do not have a data shortage. They have twelve versions of the same number, a spreadsheet nobody dares to touch, and a queue of people waiting on someone else to pull a report.
The fix is rarely another tool. It is clearer definitions, a reporting layer people trust, and a way of working that keeps it that way once the consultant has gone. That is the work I do.
Sound familiar?
- Two teams present different figures for the same measure, in the same meeting.
- Reporting depends on one person and their laptop.
- You have bought a BI tool but adoption has stalled.
- Leadership is asking about AI and nobody can say where to start.
- Work gets delivered, but no one can see what is in flight or why.
Services
Four ways I can help
Engagements range from a short diagnostic through to hands-on build and ongoing fractional support. Most clients start with one and grow into others.
AI strategy & agents
A grounded view of where AI pays off in your organisation, then the semantic layers and agents that make it work on your own data.
Explore this serviceData governance & quality
Shared definitions, documented lineage and dashboard standards, so the numbers stop being an argument and start being an answer.
Explore this serviceAgile delivery & product ownership
Scrum and Kanban that fit the team you actually have, with Jira and Confluence set up so priorities and progress are visible to everyone.
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How I work
Small, useful steps. No eighteen-month programmes.
Every engagement follows roughly the same shape, scaled to the size of the problem.
Understand the problem
Workshops and interviews with the people who use the numbers, not just the people who own the systems. The stated problem is often not the real one.
Agree what good looks like
A short, plain-English scope with the outcome, the measures and what is deliberately out of scope. You sign it off before anything gets built.
Build in the open
Short cycles with something visible at the end of each one, so you can steer while it is still cheap to change direction.
Hand it over properly
Documentation, standards and training so your team owns the result. The aim is to make myself unnecessary, not indispensable.
Who you would be working with
An operator who moved into data, not the other way round.
I spent thirteen years in Apple retail operations, most of it leading a team of around thirty technicians, before moving into analytics. Today I lead business intelligence and AI direction for a global function at Dyson.
That order matters. I have been the person waiting on a report and the person building it, so I tend to ask what decision a number is meant to support before I ask which tool it should live in.
More about my backgroundCertified in the work, not just the theory
- Tableau Desktop Certified Associate
- Certified Scrum Product Owner (CSPO)
- Google Data Analytics Certificate
- Adobe Analytics Data Analyst
Plus a Foundation Degree in Computer Science from Oxford Brookes University.
Got a reporting or AI problem you cannot get traction on?
Tell me roughly what is going on. If I am not the right fit I will say so, and point you somewhere better.