Four Categories of Data Quality Management

Tony Brownlee
10/11/16 6:30 AM

In our time spent with executives throughout the financial services industry, we've uncovered four categories of activity that consume leaders' time and budget for data quality.  In particular, with such a focus on legal entity data, we are seeing a significant focus on improving the spend and data governance efforts around these categories of data quality management. Firms that identify data quality issues early and move quickly to fix problems typically achieve higher success with more Four categories of data quality managementadvanced data management requests, and fewer questions from regulators. Take a look at the four categories and download the whitepaper for more information on entity data quality.


Poor data quality and data quality management impact the business through inefficiencies, errors, additional costs or even fines. You need to understand exactly where you stand today with your legal entity data, clients and counterparties, security master, or other critical data assets. The right data quality assessment can uncover hot spots and ensure better compliance with industry standards and regulations without expensive and weighty analytics tooling, along with associated staff costs.

Important steps include a data quality assessment, segmentation and analysis, and creating a baseline and governance for data quality. Assessment must be done thoroughly to identify priorities and then must be completed regularly to monitor progress.


This approach involves tuning your efforts to cleansing and improving the often outdated, error-filled, and inconsistent data that’s under the spotlight from risk and regulatory reporting needs. Expand your records with critical data attributes and maintain these records on a daily basis. Depending on your organization, you may have 5,000 to 2 million clients – along with U.S. and global regulators – who rely on the accuracy of your data.

Important steps include data set up and validation, data remediation projects, and cognitive training and corpus building. Remediation will show shorter term results to leadership, but is a time and expense waster over time.


Using the newer big data, analytics, and AI approaches to manage data, improve quality and implement components of data governance focused on expanding the data. Identify business units or enterprise wide programs that have data related goals. Determine the data governance priorities and verify the data quality problems that exist. Establish a strategy for securities, clients and counterparties, and hierarchies and identify the additional data that can expand usefulness on top of a sound legal entity foundation. Think business and IT impact.

Important steps include integrating third-party data, integrating legacy system data, and aggregating and distributing mastered data. Enrichment efforts will fail without proper governance and reliable assessment.


Address your reference data management issues around corporate actions with automation. Continuously scan sources for events that could have an impact on entity data, including familiar event types, unique client-specific events, future events, and effective data management. This can provide a proactive approach to reviewing corporate actions and provide data in a feed format that can be used to meet risk, regulatory and operational requirements.

Important steps include automating corporate actions, managing new legal entity actions, and extracting data from agreements/contracts. The larger your dataset grows or the more attributes added through enrichment, the more attention required in maintenance.

These four approaches can help your organization provide more accurate client and regulatory reporting and other realized benefits across the enterprise, such as improved cross-selling rates. As client-facing staff gain confidence in the quality of the entity data, the data becomes more valuable for idea generation and identifying new business opportunities.

It is no longer necessary to hire a team of people or consultants, or spend months implementing generic data quality tools, to improve entity data management. By using new technology specifically designed for assessing, remediating, enriching and maintaining entity data quality, you can achieve the results needed to satisfy regulators and clients alike and potentially unlock new revenue streams.

This is an excerpt from our latest whitepaper "Entity Data Quality: New Approaches and the Four Categories of Data Quality Management." 

Download now and discover more. 

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