Reliable entity data is critical to business strategy, but it can be difficult to manage, raising questions about how financial institutions can improve the measurement of entity data quality and manage it in a way that best suits their organisation. Answering the questions are emerging cognitive technologies that can identify and automatically fix incorrect entity records, and an entity data quality management process that assesses, remediates, enriches and maintains the data.
There are many critical use cases for entity data, including business decisions, trading, risk, settlement and reporting. From a regulatory standpoint, entity data, hierarchy data and beneficial ownership are also essential to anti-money laundering, Know Your Client (KYC) and client onboarding processes, but getting the data right can be challenging and errors can easily permeate through an organisation.
Entity data quality challenges that crop up time and time again include sourcing required data, data duplication and inconsistency, managing data across multiple legacy systems, and coping with a melange of internal and third-party entity identifiers, including Legal Entity Identifiers.
On the basis that if you can’t measure it you can’t manage it, Kingland Systems has developed advanced analytics and cognitive tools that support entity data quality measurement and management, and allow data quality weaknesses to be discovered and fixed quickly and efficiently.
The company outlines how analytics on top of your data can analyse, visualise, explore, report and make accurate predications about entity data associated with your customers and counterparties, and how cognitive data process automation can vastly improve the efficiency of searching, identifying, extracting and fixing entity data in a White Paper titled Entity Data Quality: New Approaches and the Four Categories of Data Quality Management.
You can also find out more about how to measure and manage entity data quality in an upcoming webinar featuring Tony Brownlee, a partner at Kingland; John Yelle, executive director of enterprise data management at DTCC; and a data practitioner working with entity data.
You can sign up for the webinar here and join the discussion on:
- The criticality of entity data
- Challenges to entity data quality
- Application of analytics and cognitive tools
- How to measure and manage data quality
- Beneficial outcomes of high quality data