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Data Management
A monitoring system is only as good as the customer data underneath it. Most of the false positives we are asked to fix turn out to be data problems.
The problem
Expensive systems fail quietly on bad data.
Advanced applications — customer relationship management, business intelligence, decision support, executive information systems, data warehouses — are bought to support the business and win market share. What is routinely overlooked is the quality of the data feeding them.
Those systems fail when the source data is incomplete, inconsistent, inaccurate, duplicated or simply wrong. The failure is rarely dramatic. It shows up as a monitoring rule that never fires, a report that will not reconcile, or an alert queue nobody can clear.
We provide comprehensive data management so that quality is retained — protecting the investment in those systems and the return-on-investment timeframe behind them, and giving a clearer view of the underlying business.
What we provide
- Sahassa Data Quality Management — integrated data management application
- Consultation on data quality strategy and governance
- Outsourcing — clean-up, enrichment, validation and de-duplication
Why it matters here
Compliance makes data quality a regulatory question.
In an AML context, data quality stops being an efficiency concern and becomes an examination concern. Customer due diligence rests on identity data being accurate and current. Individual risk assessment for OJK rests on that data being complete. Duplicate customer records defeat aggregation, which defeats threshold monitoring.
Because we build the monitoring and reporting systems as well, we tend to see both sides of this problem — and can tell the difference between a detection rule that is wrong and a data set that is.