Early Data Records – Handwritten Era: Building Foundations on Paper

Double glazing telesales in 1987 taught me that every conversation mattered, every rejection held insight, and patterns emerged from the chaos, if you knew how to read. It was simple. I tracked call volumes, conversion rates, and seasonal trends across lined paper, building my first understanding that data without action is just wrist-aching paperwork.

Back in 1989, I was analysing chemical data as a surface coatings laboratory tech with nothing but daisy-printer extra-wide green lined sheets. You know the ones, you’d have to babysit the printer just in case one of the teeth would send a spiral catastrophe to create chaos, panic and a white coat frenzy of wasting ink ribbon. #cleandatasets

The Rolodex Revolution: Relationships Become Data

Moving into newspaper advertising sales, I graduated to index cards and a trusty Rolodex. Each client became a data point with purchase history, seasonal preferences, and negotiation patterns meticulously documented. I learned that successful sales forecasting wasn’t about gut instinct—it was about recognising behavioral patterns and timing market cycles with mathematical precision.

Data Digital Liberation: When Large Datasets Finally Arrived

The relief when digital cataloguing systems emerged was profound. Suddenly, months of manual data entry transformed into searchable databases, pivot tables, and trend analysis. I could finally validate those early hypotheses formed across handwritten ledgers. The patterns I’d suspected were real, measurable, and scalable.

I’ve always loved eCommerce, the clear cataloguing, library-like system made Optimisation and website navigation so much easier. Homepage > Categories > Products > Product Descriptions > YouTube video Embeds.

Today: AI-Enhanced Predictive Intelligence

Four decades later, I’m still that curious analyst, but now I’m training LLMs and engineering prompts that surface insights in seconds rather than weeks. I’ve evolved from counting tally marks to deploying NLP topic analysis and semantic pattern recognition. The foundation remains identical: understanding human behavior through data interpretation.

The difference? Today’s tools amplify human intuition rather than replace it. I’m still asking the same fundamental questions I asked in the 1980s, but now AI helps me find answers that transform businesses overnight rather than over quarters.

I’m bloody crazy passionate about turning historical marketing data into tomorrow’s competitive advantage—because patterns don’t lie, people do.