Regulatory issues, operational problems, and bad business decisions are just a few of the results of poor data quality. Problems like these can quickly escalate out of control, bringing regulatory penalties and reputational damage. Improved data quality however can reduce these risks and problems, and lead to significant cost savings and better business decisions.
Many financial services organizations today are introducing enterprise-wide data quality management initiatives that include a data quality strategy and capability assessment. But a critical first step is to establish accurate data quality metrics. Without a baseline for regular measurement of quality levels it is impossible to know whether or not the data quality is improving.
The best way for a financial services organization to measure the metrics it needs is by implementing a Data Quality Assurance Platform, a vital tool in improving the reliability and accuracy of data across the firm. The Data Quality Assurance Platform monitors data quality exceptions on a regular basis by applying user-defined data quality rules and rule exception thresholds. Its enterprise-level approach means that multiple business units can leverage one common platform and share costs and the resources.
Capgemini’s Data Quality Assurance Platform includes data quality metrics reporting together with issue detection and resolution management. It allows multiple business user types to monitor and improve data quality in business, operations, support, technology and senior management. Capgemini collaborates with each individual client to implement an appropriate Data Quality Assurance Platform that leverages third-party tools that suit that client’s requirements and standards.
Capgemini’s Data Quality Assurance Platform includes:
- A data quality rules engine to manage and execute the rules, and generate exceptions when records fail any rule.
- A data quality dashboard to report all metrics and trends, and manage quality issues.
- Data quality issue/resolution management to provide a workflow process to make it possible to manage issues through to closure via automated workflow steps.
- Metadata management to facilitate the administration of definitions of critical data elements, identify physical columns in tables together with usage information, data quality rules, names, descriptions and data lineage from data sources to report for simplifying data quality issue resolution.
- Metrics management to capture exceptions and identify red/amber/green (RAG) status based on multiple exception thresholds.
When required, Capgemini can also help clients implement the platform in a big data context. Although a full implementation of the platform is a significant project, a pilot can be completed in around three months. Capgemini collaborates closely with each bank, insurer or capital markets firm to ensure that the metrics and reports produced are tailored to the needs of the business.
Take the first step today towards enterprise-wide data quality assurance by contacting us at email@example.com or reaching out to one of our experts.