Hello, I'm Chris Vaughan.
I have spent the last twenty years somewhere between operations and data: first running teams that had to hit numbers, then building the reporting that told them whether they had. Vawnai is where I do that work independently.
The route in
I did not start in analytics. I joined Apple retail in 2008 and spent thirteen years there, most of it as a technical lead running a team of around thirty technicians in one of the busiest stores in the estate. That job was workforce planning, service quality, escalations and coaching, and it was where I first started using reporting seriously, because it was the only way to see what was actually happening across a team that size.
That pulled me toward the data itself. I moved into a learning and development analyst role at Apple, building Tableau reporting for leadership, supporting the rollout of a new learning management system, and improving how training effectiveness and supplier performance were measured.
Since 2022 I have been a senior business intelligence analyst at Dyson, leading the analytics and reporting capability for a global function. Alongside the reporting estate I own the function's AI direction, which in practice means finding the places where AI genuinely improves productivity and knowledge access, then building the semantic models and agents that let people interact with business data without needing to know where it lives.
What I am actually good at
The consistent thread is bringing structure to problems that arrive vague. A stakeholder rarely says "our metric definitions have diverged across three source systems". They say the numbers look wrong. Most of the value I add is in the translation between those two sentences, and then in building something practical on the other side of it.
- Turning ambiguous business questions into defined, buildable requirements
- Designing reporting that survives contact with real users
- Setting up ways of working where none existed, without drowning people in process
- Working across business, engineering and data teams without losing anyone
Why "Vawnai"
It is a made-up word, which is deliberate. The work spans data, AI and delivery, and I did not want a name that boxed it into one of those or dated the moment the acronyms change.
Away from work
My first degree was in music performance at Birmingham Conservatoire, which is a less strange background for this work than it sounds. Ensemble playing is largely about listening carefully, knowing when to lead and when to support, and practising the unglamorous parts until they are reliable. That describes most good delivery work too.
A few things worth knowing up front
Plain language, always
If a recommendation cannot be explained to the person who has to live with it, it is not finished. Jargon usually hides an unresolved decision.
Small before big
I would rather deliver one dashboard that changes a decision this month than scope a platform that lands next year.
Handover is the deliverable
Documentation, standards and training are part of the work, not an optional extra at the end. You should not need me on retainer to keep the lights on.
Honest about fit
If your problem needs a data engineer, a platform migration or a different kind of specialist, I will tell you rather than stretch to fill it.
Realistic about AI
AI is genuinely useful in specific places and oversold nearly everywhere else. I will help you find the former and skip the latter.
Discreet by default
Client work stays confidential. Nothing appears as a case study without written agreement, and often it does not appear at all.
Want to talk it through?
A first conversation costs nothing and usually clarifies more than a written brief does.