To underwrite client insurance requirements quickly, the organization needed to standardize the quality of their worldwide brokerage data.
Business objective & project requirements
- Implement real time data quality management processes.
- Improve data accessibility and useability through a company-wide, self-service model.
- Build a data analytics capability to better leverage business data insights that came with improved data accuracy.
- Eliminate the need to frequently correct and resubmit mandatory reporting required by a major insurance regulator.
- Enable accurate and timely mapping across hundreds of disparate, non-standardized spreadsheets from various brokers around the world for input into RMS RiskLink, the risk assessment solution.
Initial data challenges
- With little to no data quality automation, this insurance provider had become overly dependent on manual go-arounds and fixes.
- A recent data maturity assessment identified stakeholders lacking trust in the quality of the company’s data, potentially slowing adoption of new data quality automation.
- Accurately enforcing GDPR standards required good data quality support to maintain compliance with the “right to be forgotten” and “right to rectification” rules on behalf of clients and employees.
Solution | Phases
After concluding a successful PoC, the customer implemented their DQ solution across the following phases:
- Integrate Ataccama’s ONE DQ Engine and profiling solution with multiple systems in order to 1) improve process management on behalf of employees (e.g. Employer’s Liability Tracing Office (ETLO), and 2) synchronize monthly spreadsheet data-feeds from worldwide, syndicate underwriting systems.
- Automate data quality to validate BI (Business Intelligence) transformed by SQL procedures stored in the company's data warehouse.
- Enable superior quality and access to a homegrown glossary solution through scheduled jobs or on-demand.
- Standardize all report data to conform to regulatory reporting standards.
- Create a DQ automation standard to enable accurate mapping for all non-standardized underwriting submissions.
Results
- Through validation and reconciliation, the organization achieved a minimum 99% success rate for Employer’s Liability Tracing Office (ELTO) submissions.
- Improved data accuracy substantially reduced rejection of regulatory reporting, and reduced the need to expend additional work hours submitting corrections.
- Quickly and efficiently underwriting client insurance requirements by standardizing data quality across worldwide brokerage data.
- Fast/accurate delivery to RMS RiskLink to increase underwriting productivity, in addition to saving hundreds of work hours every month.
