🗺️ Why This Site Exists

I’m in the final chapter of a career that kicked off in 1986. Not done yet—there’s still fuel in the tank and the occasional hill to climb—but I’ve hit that stage where writing things down feels more like duty than nostalgia. Think of it as stroking the metaphorical white beard—or tipping the hat to a bit of Renaissance thinking: reflect, distil, and pass it on before it all vanishes into the cloud—be that cumulus or Kubernetes."

Over the years, I’ve seen projects fly, stall, and occasionally fall over sideways—not for lack of talent, but usually because they got tangled in jargon or seduced by frameworks that looked fabulous on a slide deck but collapsed under the weight of reality. Clarity still matters. Always has. Good delivery doesn’t start with a licence key, a TED Talk, or whatever the latest three-letter acronym happens to be—it starts with understanding. The kind grounded in principles that have survived since the Renaissance: observation, curiosity, logic... and the occasional raised eyebrow when something smells off.

But I’m not just here to wax lyrical about lessons hard-won. I’m still learning—because today’s slick platform is tomorrow’s legacy system with a support contract no one wants to touch. So part of this exercise is me kicking the tyres on whatever’s coming next, figuring out what actually works, and showcasing how the state-of-the-art might earn a long-term place in the kit. Because I’m not just handing over the baton – I’m checking it's not made of glitter and glued together with duct tape.

And if some of this site sounds like the ramblings of a grizzled old data wrangler muttering into his beard—well, fair enough. But I like to think of it as leaving a trail of breadcrumbs for the next poor soul who has to explain why the star schema matters, why the data model isn’t “just a diagram” and why nobody should let marketing pick the system architecture.

So if you’ve made it this far — cheers. Either you’re in the business, curious enough to dig deeper, or just remarkably patient with metaphor-laced ramblings. Whatever brought you here, welcome aboard. Together, let’s keep things sharp, sound, and only mildly scribbly.

👋 About Me

I’ve worked across telecoms, finance, insurance, utilities, and the public sector — usually in that elusive space where strategy makes promises and delivery keeps them. Three degrees, a few decades in the game, and still partial to a clean star schema.

I enjoy a good metaphor — think of me as the violist or bass player: not chasing the spotlight, but keeping the whole thing in tune.

🛠 What I Do and How I think (and Why you Might Actually Care)

Whether you’ve arrived out of curiosity, confusion, or because someone said, “This one knows what he’s talking about,” (thanks, Mum!), you’re in the right place.

Data migrations, data architecture, project rescue missions, cloud integrations — and plenty of time spent bridging the gap between tech and business. If it involves data, delivery, or crossed wires, I can probably help with it.

📈 Data in Context : Dig Site ≠ Museum

Data is the artefact; insight is the archaeologist going, “Aha!” OLTP is the dig site—real-time, chaotic, with mud on your boots. OLAP is the curator’s lounge—quietly piecing together the past over a cup of tea. Best practice? Don’t curate in the trench. Let Change Data Capture (CDC) ferry the finds from OLTP to lakehouse—without tripping over the wheelbarrow.

🧠 Gantt and Jira Type Musings

Waterfall (e.g., MS Project) keeps the CxOs calm at the programme level, Kanban chunks (e.g., Jira) keep progress visible and controlled, and Scrum sprints give devs enough freedom to fly—just not a passport to vanish into techie nirvana.

🤖 🧠 🛸 Hey Ho AI/ML 

AI doesn’t naturally understand star schemas or metadata — that’s our architectural baggage, not its native tongue. It wouldn’t know a conformed dimension if it tripped over one. But give it a clean, flattened dataset extracted from your beautifully modelled star schema, and it’ll crunch numbers faster than a finance team at quarter-end. It doesn’t care what a ‘dimension table’ is — just that the columns line up and the maths add up. The real trick? We hide all that clever pattern-spotting behind slick APIs, so other systems can call it like it’s magic — no whiteboards or data lineage diagrams required.

🔗 [AI/ML Example Scenarios →]

🔗 [AI/ML Cloud Blueprints →]

 

☁️💻 📊 Every Day is a Cloudy One!

'The Cloud' — basically a data centre, just without the weird humming noise. Or as we now say (because we’re professionals): DCaaS — Data Centre as a Service. We must be experts — after all, we’ve invented a whole new set of acronyms, hand signals, and architectural diagrams.

Frankly, with this level of vocabulary inflation, it’s probably time we set up our own professional movement. Robes optional, but a Latin motto wouldn’t hurt. How does ”Templum Nubium: In Cloud We Trust” grab you?

📬 Contact

Need help untangling a project? Want to chat SAP, ERP, data, integration or delivery over coffee (real or virtual)? Happy to hear from you.

🛠️ Thanks for visiting

If something here resonates, bookmark it.

If it makes you roll your eyes — well, at least it made you think.

🤔 Let's get back to fixing what needs to be fixed.