Transforming Data into Opportunities: Metric of the Month – Mapping Network
High-quality data is more than a benchmark – it is a strategic necessity for global trust, compliance, and interoperability. In this blog, Zornitsa Manolova, Head of Data Quality Management and Data Science at GLEIF, outlines the importance of GLEIF's Mapping Network in enabling interoperability across parallel ID platforms, streamlining entity verification processes, and reducing data management costs for data users.
Author: Zornitsa Manolova
Date: 2025-10-08
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In an increasingly interconnected global economy, the ability for organizations to trust and use data effectively is the foundation for innovation, growth, and competitiveness.
A high-quality data ecosystem is a driver of change and innovation that enables organizations to identify and seize new opportunities. At the same time, low data quality can lead to inefficiencies and exposure to regulatory and reputational risks.
GLEIF is committed to optimizing the quality, reliability, and usability of LEI data. Since 2017, it has published dedicated monthly reports to transparently demonstrate the overall level of data quality achieved in the Global LEI System.
To aid broader industry understanding and awareness of GLEIF’s data quality initiatives, this new blog series explores key metrics included within the reports.
This month’s blog examines GLEIF's Mapping Network
The Global LEI system (GLEIS) was designed to make legal entity data transparent, accessible, and reusable by anyone, anywhere. All LEI data and related relationship files are published as open datasets, enabling market participants, regulators, and researchers to interoperate supported by a shared and trusted foundation of high-quality data. This commitment to open data enhances financial inclusion, fosters collaboration, and ensures that high-quality entity reference data is available to all.
As described in our recent blog 'Building a transparent future with open data, AI, and the LEI’, earlier this year, GLEIF and Open Ownership launched a new strategic initiative, the Global Open Data Integration Network (GODIN) - a framework that measures and promotes the availability of trusted, machine-readable, and openly licensed entity data worldwide. GODIN embodies our belief that open data is not just a principle but a catalyst for innovation, allowing organizations, regulators, and developers to collaborate on a shared foundation of reliable information.
The importance of LEI Mapping
LEI mapping is critical in ensuring interoperability across parallel ID platforms. By linking widely used identifiers to globally recognized LEIs directly from their respective data sources, GLEIF and its partners are transforming fragmented identifiers into a connected, open network. Identifiers that the LEI is already mapped to include: SWIFT’s Business Identifier Code (BIC) and Marketer Identifier Code (MIC); the Association of National Numbering Agencies’ International Securities Identification Number (ISIN); S&P Global Market Intelligence's S&P CIQ Company ID; OpenCorporates' IDs; and Qichacha's QCC code.
These certified mappings enable seamless interoperability between data from different systems and jurisdictions, facilitating a range of applications, including cross-border compliance, risk analysis, financial market research, and due diligence. Beyond connecting data, LEI mapping also enhances transparency and trust while reducing data reconciliation costs and complexity.
To further promote the value of open mapping and encourage broader participation, GODIN is now developing an open-source space that demonstrates the transformative potential of collaboration across organizations. The first published Jupyter Notebook is a result of teamwork between GLEIF and GODIN’s new member organization: Open Data Services. The Notebook will reveal how existing mappings can be used to generate real-world insights. This will enable anyone to explore, analyze, and visualize relationships between identifiers and LEIs using live, open data. By showcasing the potential of these mappings, we aim to encourage more data providers to join the initiative, expand coverage, and accelerate innovation across the global financial ecosystem. Together, openness and mapping are transforming legal entity data into a powerful public good.
A spotlight on our Mapping partners:
SWIFT (BIC & MIC): From February 2018 onwards, GLEIF and SWIFT have been publishing an open-source relationship file mapping Business Identifier Code (BIC) records to LEI records. This enables financial institutions to link banking identifiers with globally recognized entity data, supporting payments, messaging, and regulatory reporting. Currently, a total of 39,211 BIC-to-LEI mappings are available.
Similarly, since November 2022, GLEIF has maintained the mapping of Market Identifier Code (MIC) identifiers to LEIs, enabling direct linking of market operators and trading venues to their respective legal entities. With 1,001 mappings already established, this supports regulatory reporting under regimes such as MiFID II (markets) and SFTR (security financing), facilitating counterparty aggregation, risk monitoring, and the reconciliation of trading or market venue identifiers.
Association of National Numbering Agencies (ANNA): From April 2019, GLEIF has been providing daily files mapping International Securities Identification Number (ISIN) codes to LEIs, showing which legal entity issued each security. As of October 2nd, we have 94,015 unique issuers with LEIs, and 7,441,649 mappings are in place. This initiative enhances transparency and strengthens risk analysis across capital markets.
S&P Global Market Intelligence (S&P CIQ): Launched in November 2022, the mapping between S&P Global Market Intelligence (S&P CIQ) Company IDs and LEI records connects one of the most extensive proprietary company datasets with the GLEIS. Updated weekly, this relationship of 3,056,323 records helps organizations align their internal S&P CIQ-based records with standardized LEIs, streamlining compliance, risk assessment, and regulatory reporting. By bridging these two major identifier systems, this mapping brings clarity and consistency to millions of company profiles used across the financial sector.
OpenCorporates ID (OCID): In April 2023, GLEIF and OpenCorporates launched the first bi-weekly open-source relationship file, linking OpenCorporates entity IDs with LEIs. The mapping uses data from the OpenCorporates database of over 220 million companies (across ~145 jurisdictions). 1,529,589 of the global LEI population is now linked to the OpenCorporates database, though some LEIs belong to entities not covered by corporate registries (e.g., funds), which limits full coverage.
Qichacha (QCC): Launched in April 2025, the QCC to LEI mapping extends the GLEIF open data network into China’s corporate landscape. By linking QCC’s local company identifiers with globally recognized LEIs, this dataset, published monthly, creates a vital bridge between domestic Chinese entity records and international reference data. With 1,930,040 mappings already available, the initiative is opening new possibilities for transparent cross-border analysis, compliance checks, and market research involving China-based entities.
Transforming data into opportunities
GODIN is unlocking powerful new opportunities for innovation and collaboration. As more organizations contribute identifiers to the GLEIF Mapping initiative, GODIN's open-source space showcases how to leverage these datasets. If your organization is eager to help drive greater transparency, efficiency, and trust across global data ecosystems by joining GODIN – or if you have ideas for open-source scripts that could bring value to the public – we would love to hear from you. Please reach out to us at godin@gleif.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.
