We are all shaped by our experiences. The trick is keeping up to date and discerning between knowledge, improvement, innovation and invention. I started working with data in the mid-1980s, when information often had to be assembled from paper records, punched input, postcodes, census data, county boundaries and early digital files before it could be handed to professional statisticians for analysis.

That environment shaped how I still think about data today. I see it first as a representation of the real world, then as something captured through operational processes, structured for storage, transformed for analysis and presented for decision-making.

The technology has changed considerably. Mainframes, sequential files and manual coding have given way to cloud platforms, APIs, data lakes, GIS, Python and modern analytics. The underlying questions have not changed quite so much: what does the data represent, how was it created, what happened to it along the way, and how much confidence should we place in the result?

My work sits across those layers. I bring together operational context, data architecture, storage, migration, modelling, governance and analytical use. That helps me trace information from the original real-world event through to the report, model or decision it eventually supports.

My perspective is less about any single technology and more about understanding the whole data chain: how data is created, how meaning is preserved, where quality can be lost, and how it can be made useful without losing sight of the reality it is meant to describe.