Integrating ESG data has become a major challenge for financial institutions. However, this data differs significantly from traditional financial data, both in terms of its nature and its complexity. The ability of financial players to overcome the technical challenges associated with this difference directly affects the reliability of analyses and regulatory compliance.
One of the main technical challenges lies in data mapping and reference management. ESG data is produced and distributed by a variety of providers, each using their own identifiers and scopes. For financial players, the first step is to link these identifiers to traditional financial reference systems.
This operation, which appears simple, is in fact highly complex. For example, some providers offer data at the subsidiary level when the institution manages its positions at the group level. Others apply different nomenclatures, complicating the reconciliation of data sets. At this first level, mapping must establish a reliable technical link between financial and non-financial data.
The second level, matching, adds an extra layer of difficulty. This involves creating correspondences between issuers and securities referenced by different ESG providers. This step is essential for comparing or combining indicators from multiple sources. In practice, it faces structural and methodological differences that turn a simple correspondence table into a real technical challenge. Solid expertise is required to overcome this challenge.
This preparatory work forms an invisible but critical foundation for any ESG integration strategy. A matching error can lead to inaccuracies in all downstream analyses, compromising the robustness of sustainable investment decisions. Without specialised technological tools, this work is doomed to remain manual, carried out by analysts who prioritise companies already in their portfolios. But this approach limits the expansion of ESG coverage and creates a bottleneck for large-scale adoption.
A second technical challenge lies in managing the timing of data. Financial institutions traditionally work with real-time market data, daily valuations and quarterly reports. ESG data, on the other hand, follows a different logic: it is often published annually, with a delay of several months.
Greenhouse gas emissions illustrate this lag. When integrated, this data may be more than a year old. Linking it to market capitalisation, which changes daily, poses serious methodological challenges that will need to be overcome. Otherwise, this lag may call into question the relevance of composite indicators that combine financial and non-financial criteria.
Some ESG data also follow an unpredictable timeline. This is the case for data related to controversies. These events arise irregularly and instantly change an issuer's ESG profile. Institutions must then urgently adjust their analyses, often with incomplete information.
This temporal heterogeneity complicates the integration of ESG data into financial models. It requires the implementation of version management and time-stamping mechanisms to ensure that each analysis is based on consistent and dated data. The issue of comparability over time becomes central to maintaining portfolio robustness and meeting regulatory expectations.
The third major challenge relates to the growing volume of ESG data. The granularity of this data is much higher than that of traditional financial data. For a company, a supplier can provide several hundred indicators covering a variety of dimensions: CO₂ emissions by scope and by gas, water consumption by type, workforce diversity, governance, etc.
Multiplied by several thousand issuers and over several years of history, these indicators generate considerable volumes. An asset manager may thus be faced with tens of millions of data points to manage and control. This volume inflation poses a technical challenge in terms of storage, processing and system performance.
Managing these volumes is not just an infrastructure issue. It also raises governance questions. How can duplication between sources be avoided? How can the traceability of each piece of data be ensured throughout its transformations? How can historical data be kept consistent when methodologies are constantly changing?
Traditional infrastructures quickly reach their limits when faced with this complexity. Institutions must adopt data architectures capable of absorbing this volume and providing high-performance queries for investment teams. At the same time, they must implement automated controls to verify the internal consistency of indicators and identify discrepancies between sources.
Finally, the proliferation of data implies an increased need for prioritisation. Not all available ESG data can be integrated and exploited with the same intensity. Institutions must define their priority business needs and focus their technical efforts on the indicators that provide the most strategic value.
Faced with these technical challenges, financial institutions can no longer be satisfied with fragmented or ad hoc approaches. They must strengthen their ESG project management by structuring their processes and relying on appropriate tools. In this context, using a proven technological solution such as WeeFin's not only makes data integration more reliable, but also transforms the complexity of ESG into a real lever for performance and compliance.
