For marketers, CRM managers, RevOps teams, demand generation leaders, and data-driven decision makers who want to improve campaign performance, trust their reporting, and make AI actually useful instead of confidently chaotic.

Many organisations invest heavily in marketing technology, automation, and AI while overlooking the quality of the data powering them. Duplicate leads, inconsistent fields, poor categorisation, and conflicting records undermine reporting, personalisation, attribution, and AI outcomes. This session explores how to identify and prioritise the most damaging data quality issues using the COAT framework (Consistent, Organised, Accurate, Trustworthy), helping teams build trusted, decision-ready data that supports better decisions, stronger customer experiences, and more effective marketing.

What you can expect to takeaway:

How dirty data impacts attribution, reporting, customer insights, automation, and AI outputs
Practical techniques for identifying and prioritising the most important data quality issues
A simple framework for creating trusted, decision-ready marketing data that supports long-term success

Level it's for: Executive / Director / Head of / Manager