Esoof Piperdi
Principal Consultant
Last month, I had the pleasure of attending the IRM Data Governance Conference in London. The event gave me the opportunity to step out of the day to day for some thought-provoking conversations, candid sessions, and some brilliant minds all wrestling with the same fundamental challenges. I was there representing Agile along with my two colleagues Olly and Danny, talking to organisations about Modern Data Management. I came away having learnt just as much as I shared.
Here's what stood out for me across the two days. I managed to attend five sessions across the two days, and each one added a different layer to what became a consistent overall narrative.
Keynote: Monday morning
The tone was set early. The keynote grounded everything in the reality that data governance is no longer a back-office IT concern. It is a strategic business priority. Organisations that treat it as such are the ones pulling ahead.
From Chaos to Clarity: Darren Russell
This was one of my personal highlights. Darren's session was a refreshingly honest look at what it takes to bring order to a data landscape that has grown organically (and often chaotically) over years. The message was clear: clarity doesn't come from a single big project. It comes from deliberate, incremental progress.
AI Governance at Royal London
A brilliant real-world case study. Not just how things should be done but how it is done in practice at Royal London. Mary was frank and honest about achievements as well pain points and hurdles. Hearing how Royal London are approaching AI governance in a regulated environment was reassuring. The key takeaway? You can't bolt governance onto AI after the fact. It must be designed in from the start.
Accelerating Digital Transformation: Agile Governance & Responsible AI
This session resonated deeply with how we work at Agile. The argument was compelling that you can move fast and govern responsibly. They're not opposites. A governance model, when done right, accelerates transformation rather than slowing it down.
Cutting Through the Hype: Practical Insights from a Real Data Transformation Journey
Possibly the most interesting session of the two days. No vendor talk, no theoretical frameworks. but what a real transformation looked like, including the setbacks. It reinforced something I believe strongly: ROI from data initiatives has to be demonstrated quickly and tangibly, or stakeholder confidence evaporates.
Get Data & AI governance right before AI gets widely used in your organisation, not after.
That sentiment came up again and again across sessions and through many conversations. It was a practical, grounded concern from people who are already living the consequences of AI being deployed without the right guardrails in place.
The second big thread running through the conference was the relationship between Master Data Management and AI. Two sides of the same coin, really. On one hand, good, solid MDM creates the clean, trusted, well-governed data that AI models actually need to perform well. On the other, AI is increasingly being used to enhance MDM automating data matching, identifying anomalies, surfacing insights that would otherwise take weeks of manual effort.
For those of us in the data management space, this is both an exciting opportunity and a responsibility. If the data foundations aren't right, AI doesn't just underperform but it compounds existing problems at scale.
Many of you who stopped by asked a version of the same question: "When you say Modern Data Management platform, what does it actually mean in practice?" It's a fair challenge. The term gets used broadly, and organisations have often been burned by large, expensive, slow-moving data projects that promised transformation but delivered very little, very late.
At Agile, our answer to that is our MVP-first approach. Rather than proposing a sweeping, multi-year programme, we start with a focused Minimum Viable Product scoped, delivered, and showing measurable return on investment within three months. If you see the value, we move to the next phase. If not, you haven't bet the house on it. It's an approach that aligns very naturally with the conversations at this conference, incremental progress, demonstrable outcomes, governance built in from the start.
What does this mean for data and transformation teams? I've been thinking about this since I got back. The conversations at IRM weren't happening in a vacuum, they reflect what's going on in boardrooms and leadership teams right now. For data and transformation teams, the implications are significant.
Data quality directly affects how well we understand our customers, how accurately we can report financials and targets, and how reliably our services and offerings perform. Poor data governance doesn't just create compliance risk. It creates noise, wasted spend, and missed opportunities. And as AI-driven tools become more embedded in how we do business, the quality of the data feeding those tools becomes even more critical.
The good news is that the path forward doesn't require a massive upfront commitment. Start with a clear, scoped problem. Prove the value. Build from there.
It was a valuable two days, and it showed why what we do at Agile matters.
If we spoke at the conference and you'd like to continue the conversation, We'd love to hear more about what your organisation is working through. Feel free to reach out.