We’re a regional water utility that just finished a major ERP migration, and the biggest lesson we learned was fixing master data quality before we could even think about AI. When we started planning the migration from our legacy billing and asset management systems, we discovered over $500,000 in unreconciled payments sitting in the old databases. Payments existed but weren’t properly matched to customer accounts because of duplicate records, variant naming, and inconsistent account numbers across systems.
We brought in a consulting team to do a full data audit and remediation before go-live. They profiled all our legacy data, built matching logic to consolidate duplicate customer and asset records, and implemented human review for high-risk cases. For the lost payments, they used pattern matching on amount, date, and customer name to reconcile transactions. We recovered most of that missing cash and got our customer accounts accurate.
The real win was what came after. With clean master data in the new ERP, we could deploy demand forecasting and predictive maintenance models that actually worked. Before cleanup, we couldn’t trust the data enough to let AI make recommendations. Now our asset maintenance scheduling is more accurate, billing disputes dropped significantly, and we’re running conservation programs based on solid customer segmentation. If we’d skipped the data work and gone straight to AI, we’d have been making decisions on garbage.
We set up automated quality checks in the new ERP that flag anomalies and route them to data stewards for resolution. Validation rules prevent new records from being created without required fields, which catches most issues at the source. For assets, internal cleanup was sufficient—we standardized asset IDs, linked maintenance history, and filled in missing installation dates where possible. The consulting team used some external reference data for customer address validation but most of the enrichment was reconciling our own fragmented records.
Half a million in lost payments is wild but honestly not surprising. We found similar issues when we consolidated three regional finance systems. Payments recorded under slight name variations or wrong cost centers just sat there unmatched. Did you use any specific tooling for the matching logic, or was it mostly custom scripts and manual review?
The downstream benefit for AI is exactly what we’re hoping for. We’re sitting on years of sales and inventory data but half of it has missing product codes or inconsistent categorization. Any forecasting model we try just produces noise. Did you set up ongoing data quality monitoring post-migration, or was it a one-time cleanup?
Curious about the predictive maintenance piece. We manage a lot of infrastructure assets and our maintenance records are fragmented across paper logs, spreadsheets, and an old CMMS. If we can’t trust asset IDs or service history, I don’t see how we’d get reliable failure predictions. Did you have to enrich your asset master with external data or was internal cleanup enough?
Remediation took about four months. First month was profiling and scoping the issues, next two were building matching rules and running consolidation logic, last month was human review and final validation. We staged the migration so critical operational data went live first with strict monitoring, then historical data later. That let us confirm quality before full cutover.