Data Readiness: The Foundation for AI Automation & Effective Customer Engagement
The full recording and slide deck from our live session, everything you need to take action on your data strategy today.
22%
of customer records in UK databases are inaccurate
12%
of revenue lost to inaccurate data on average
£900bn
annual cost of dirty data to UK businesses
The full session, on demand
Session agenda
Why automation investments underdeliver
The tools are ready. The ambition is there. The gap is almost always the same thing.
Marie Roberts
A live diagnostic: how data-ready are you?
Five questions. Answer honestly. Understand where your organisation actually sits.
Dan O'Reilly
What data readiness actually means
Clean, structured, accessible, usable, defined and why each layer matters.
Marie RobertsThe Automation Maturity Model
Four stages, where most organisations sit, and how to progress through them.
Marie RobertsA practical framework: Audit · Centralise · Enrich · Govern
Four actions you can start today. The sequence matters.
Dan O'ReillyHubSpot in action
How HubSpot enables data readiness end-to-end, with live examples.
Jeremy WilliamsonGuest spotlight: Compare the Market
DMA Gold 2025: "Making AutoSergei Real with Meer-tech." What it actually took behind the scenes.
Ji Hye ChangLive Q&A
Your questions, answered by the full panel.
All SpeakersThe full slide deck from the session, including the Automation Maturity Model, the Audit · Centralise · Enrich · Govern framework, case studies, and all key stats.

Three Things to Act On
1
The data problem is solvable
It is not glamorous, but it determines whether every other investment you have made actually delivers. The good news: you can start with a single contacts report this week.
2
Most organisations are closer to the start than they think
Run the five diagnostic questions honestly. The gap between perceived maturity and real maturity is usually significant and knowing where you are is the first step.
3
The sequence matters, you cannot skip steps
Clean data → connected systems → reliable automation → AI as the natural next chapter. In that order. AI does not fix a data problem. It amplifies one.