About The Modern Analytics

Thirteen years leading data, analytics and AI at enterprise scale, most recently running the function for a multi billion dollar commercial and operations business.

I now run The Modern Analytics, consulting across data modernisation, analytics and BI, AI and agentic transformation, legacy modernisation, and the governance that decides whether any of it is allowed to go live.

Two engagements are running as I write this. A healthcare marketing and media agency, where I lead the whole chain from the data foundation through to agentic workflows in a regulated content environment. And a digital trust and AI platform company, where I have taken two platforms from definition to release with a third in build. Both are described here.

I do not sell development, and I am not going to. Having built things is useful to a client. Selling the build alongside the advice on whether to build is not, and the reasoning is here.

Why this exists

Thirteen years in, the pattern stopped being interesting. Somebody buys a platform, the platform works, and eighteen months later nobody can say what changed. Not because the technology failed. Because the chain from a goal to a number in the accounts had four places to break and nobody was watching three of them.

Every assessment I was ever shown ended with the assessor recommending themselves. That is not dishonesty, it is structure: if you sell delivery, delivery is what you find. So this practice has no delivery arm, no licences to resell and no referral fees. When the answer is build, I scope it, help you choose who does it and hold them to it, and the invoice goes to somebody else. The full argument is here.

Where the experience comes from

Most of it in regulated environments, which is where governance stops being a slide and starts being the thing that decides whether you go live at all. Life sciences and healthcare, industrial manufacturing, banking and financial services, commercial and operations.

The healthcare thread runs longer than the rest. A global BI platform and a drug development analytics portfolio, and now agency work where the constraint is not whether an automated step is right, but whether the reasoning behind it can be reconstructed for a regulator months later. The seven case studies carry the situation, what I did and what it produced, with the numbers attached to each.

What I actually do well

• Turning investment into evidence. Every product I have delivered carries a measured baseline and a named owner, which is why the impact figure survives scrutiny.
• Building the function, not just the thing. I have taken data and AI organisations from zero to enterprise maturity twice, including the operating model, the hiring and the succession pipeline.
• Governing AI that acts. Model governance across eight production models with versioning, lineage, drift detection and audit, plus authority boundaries for agentic workflows.
• Killing what should not run. Demand governance that eliminated 30%+ of low value initiatives from a $17M+ portfolio. Saying no is most of the value.

What I will tell you that others will not

Sometimes the answer is a licence you already own. Sometimes the process you want to automate should not exist. Sometimes your data team is right to say no. I have said all three and lost work for it, and I would rather that than the alternative.

Qualifications, briefly

Chief Digital and AI Officer programme at the Indian School of Business. Postgraduate programme in AI for Leaders at Texas McCombs. BTech in Computer Science. Databricks certified across data engineering, generative AI and machine learning, plus AWS, Alteryx, SAFe and Scrum. None of that is why you would hire me, and it is here because somebody always asks.