Blog entryData inconsistency, duplication, hierarchies and industry classification are just some of the challenges of entity data quality management, but there are solutions that can ease these problems and deliver efficient and effective entity data quality measurement and management.
The challenges and opportunities of entity data quality were discussed during a recent A-Team Group webinar entitled ‘Four categories of entity data quality management’. The webinar was sponsored by Kingland Systems, moderated by A-Team editor Sarah Underwood, and joined by Sean Taylor, executive director at Canaccord Genuity Group; Tony Brownlee, a partner at Kingland; and John Yelle, executive director of enterprise data management at DTCC.
Webinar Recording: Four categories of entity data quality management
The webinar kicked off with an audience poll questioning how well organisations understand the scale of their entity data quality challenge. The results of the poll showed 21% of respondents understanding very well and having a systematic approach to measuring entity data quality, 19% having no means to measure entity data quality, and a large percentage hovering in the middle having completed a one-off assessment.
Considering these responses, the panel went on to discuss the importance of entity data quality to firms’ strategies and revenues, but also to wider financial stability. Looking at the challenges of entity data quality, it noted data inconsistency, duplication, coverage and classification, as well as problems caused by legacy systems and difficulties around managing and maintaining entity hierarchy data.
Moving on, the conversation turned to issues around managing multiple entity identifiers, including Legal Entity Identifiers (LEIs), and cross-referencing them with other identifiers and securities. The panel saw a solution to these issues if the LEI is adopted across the industry and also suggested much can be achieved through good governance and control.
Addressing new approaches to entity data quality measurement and management, the panel suggested firms that have executed Know Your Customer (KYC) and client onboarding processes well have a good foundation to build on. An ongoing process of assessing, remediating, enriching and maintaining entity data was also noted as a means of improving quality, along with emerging technologies such as cognitive processing, robotics and artificial intelligence.
A final audience poll considered the benefits of entity data quality measurement and management. The results showed the majority of respondents achieving some business and operational benefits, and a minority achieving significant benefits.
Listen to the webinar to find out more about:
- Requirements for entity data quality
- Challenges of achieving quality
- Approaches to improvement
- Technology support
- Beneficial outcomes
You can also find out more by reading the Kingland White Paper Entity Data Quality: New Approaches and the Four Categories of Data Quality Management here.
