Getting Technical #2: Data Quality Update Across GLEIF’s Challenge LEI Data Facility
In this blog post, Zornitsa Manolova, Head of Data Quality Management and Data Science at GLEIF, explains the latest updates on the GLEIF Challenge LEI Data facility – a new graphical user interface (GUI) for the challenger role.
Author: Zornitsa Manolova
Date: 2024-10-29
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The quality of data held within the Global LEI Index is high thanks to a stringent data quality management system. GLEIF's ongoing commitment to empowering LEI data users and increasing data transparency benefits data governance and compliance at an individual entity level and provides a solid foundation for trusted open data initiatives.
A key component of LEI data quality management system is the GLEIF Challenge LEI Data facility, which extends the ability to trigger updates to LEI data to all interested parties.
To enhance the user experience of this facility, GLEIF has introduced a new graphical user interface (GUI) specifically for the challenger role. This new interface allows LEI data users to seamlessly view and track all the challenges they have raised in one central place, simplifying the process and increasing transparency.
What’s New?
Until now, LEI data users have received email notifications confirming the creation and resolution of challenges. With the new GUI, users are now supported by a comprehensive dashboard that provides a complete overview of their active and historical challenges.
It is a major step forward in giving users more control and visibility over their challenge submissions.
How to Access the GUI
The new interface is automatically available to all existing and new users with a challenger account. Usefully, no additional steps are required to access it. Users simply log in to their account via LEI Search and can access a link to the dashboard in the top right corner of their screens. Alternatively, users can access the GUI directly via: challengemanagement.gleif.org. This access link is also included in the email notifications that confirm the successful creation of a challenge.
Why This Matters
The introduction of the new GLEIF Challenge LEI Data GUI offers several important benefits:
Encourages Greater User Participation: By providing an easy-to-use dashboard, GLEIF is encouraging LEI data users to actively contribute to the improvement of LEI data accuracy and quality.
Enhanced Transparency: The new interface gives users a detailed overview of their challenges, with search and filtering options for easier navigation.
Backend Enhancements: While the front-end GUI has transformed the user experience, much work to improve the facility has also been done behind the scenes including, for example, advanced role-based permissions that allow bulk challenges to be raise by third parties.
What Are Bulk Challenges?
A bulk challenge aggregates multiple challenges under a shared title and description, making it easier for users to manage large sets of challenges simultaneously. With this new update, users can now raise, track, and resolve bulk challenges more efficiently and even automate the process through APIs. This functionality will be made available to users following a thorough evaluation of the requesting party and their specific use cases. To maintain the operational stability of the Global LEI System and avoid overwhelming LEI managing organizations, GLEIF reserves the right to determine how many and which users are granted access to the bulk challenge feature.
Regular LEI data users interested in leveraging bulk challenges are welcome to contact GLEIF for more information via info@glief.org.
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Zornitsa Manolova leads the Data Quality Management and Data Science team at the Global Legal Entity Identifier Foundation (GLEIF). Since April 2018, she is responsible for enhancing and improving the established data quality and data governance framework by introducing innovative data analytics approaches. Previously, Zornitsa managed forensic data analytics projects on international financial investigations at PwC Forensics. She holds a German Diploma in Computer Sciences with a focus on Machine Learning from the Philipps University in Marburg.
