About Session
Every finance team wants to put AI to work — forecasting, close automation, anomaly detection — but AI doesn’t fix messy data, it amplifies it. Duplicate vendors, stale customer records, inconsistent GL mappings, and undocumented ownership quietly undermine reporting today and will sabotage every AI initiative tomorrow. This 30-minute session reframes data hygiene and governance not as back-office housekeeping but as the prerequisite for trustworthy automation. Drawing on real ERP cleanup and AI-enablement work, we’ll show how the same AI tools finance leaders are excited about can be turned inward to accelerate the unglamorous work: profiling data quality, deduplicating and validating records at scale, flagging anomalies, and drafting the policies that keep data clean once it is. You’ll leave with a clear-eyed view of what “AI-ready” data actually requires, a sense of what can be automated versus what still needs human judgment, and a lightweight governance model you can stand up without adding headcount or grinding the business to a halt.
Learning Objective
After this session, attendees will be able to:
- Identify the most common data hygiene failures in finance and ERP systems : duplicates, stale records, inconsistent mappings, and unclear ownership : and articulate the reporting and AI risks each one creates.
- Apply AI tools to accelerate data profiling, cleansing, deduplication, and validation, distinguishing the tasks that can be safely automated from those that still require human review.
- Design a lightweight, sustainable data governance framework : covering ownership, standards, and controls, that keeps data AI-ready without slowing down the business or requiring new headcount.
