From defect logs to the data layer

I go after problems where better use of data changes a real outcome, often a public one. The common thread across my career has never been a sector or a job title. It has been taking fragmented information and turning it into something people can trust and act on. In academia, the smartphone industry, local government and community projects, the job has been the same: use data to solve a problem, improve a decision, and build a system others can pick up and run.

Four years of full-time experience across the private and public sectors, from UX Research Supervisor to Data & Analytics Engineer. The work stayed consistent while going deeper. I started by analysing data, then designed the methodologies behind it, and now engineer the standards and pipelines that whole organisations rely on.

At Transsion, I analysed after-sales records, social channels and surveys of 700+ users, led the KT4 programme, and built testing methodologies for pre-launch devices. That work fed product improvements for millions of customers in Pakistan, Indonesia, Africa and China, and earned a three-grade promotion to Assistant Regional Manager. It also taught me the lesson that still shapes how I work: finding an insight is the easy part. The value comes from building the framework that lets an organisation use that insight reliably, at scale, after you have moved on.

At WASA I helped a water agency serving millions move off legacy platforms toward the cloud. At the University of Lincoln I built Fabric-based Power BI reporting while completing my MSc. At Leicester City Council I support Youth Justice, Housing, Highways & Street Management, Education Welfare and Sports. I presented my reporting framework to the Leicester Insight Group, and the council adopted it as its standard. It is the same pattern as Transsion. Find a real problem, build a practical fix, and leave behind something used well beyond one team.

Outside the day job there is more: SellScene AI (university-funded), Datavrex (free data education, covered by the Lahore press), three practitioner books, a peer-reviewed chapter, and regular articles. Filling a role is not enough on its own. I also help prepare the people who will fill the next ones, a gap the UK has said it cannot close alone.

My goal is to build the AI data layer the public sector will run on: the pipelines, the self-service platforms that cut out gatekeepers, the governance, and the vector storage that keeps AI grounded on trusted data. The UK's National Data Library and its first "AI-ready" data standards depend on this foundation being built well. That is the work I want to do here.