ESG data management: understanding the major challenges to better overcome them

Despite political and regulatory ups and downs, sustainable investment continues to grow in volume in 2025, driven by strong market demand. For financial institutions, integrating sustainability is therefore a performance issue, as much as it is a risk management or compliance topic. However, this requires the implementation of efficient ESG data management and therefore involves overcoming numerous difficulties: multiple data sources and large volumes to process, variable data quality, methodologies to be defined, technical problems to be solved, etc. There is no shortage of challenges!

To take things further, WeeFin's team of data and ESG experts has written a comprehensive guide to help you set up an ESG data management system that is tailored to your organisation and delivers results. Download it for free here to discover our roadmap.

Challenge #1: Dispersed and heterogeneous data

Quickly collecting the data needed for an ESG approach is a daunting task for financial institutions. The data is particularly scattered and heterogeneous. Why? Because it covers a wide range of topics, from climate change to anti-corruption, biodiversity preservation, water management, human rights, transparency of executive compensation, and more. The landscape is so vast! And therefore complex to grasp as a whole. 

The other difficulty is the variety of sources. Data can come from company reports, public sources or private suppliers. Matching all this data is not something that can be improvised. 

Finally, even within the same theme and source, the data format can also vary. Reported data, modelled data, data specific to a sector of activity, structured or unstructured data... 

WeeFin's advice:In this article, we review these challenges to help you better understand the best practices for implementing data management tailored to the challenges of sustainable finance.In this highly fragmented landscape, financial players need to rely on several providers. This approach allows them to cover their needs, cross-check data and avoid dependence on a single source.

Challenge #2: Raw data that is difficult to use

The multiplicity of sources and formats is compounded by another problem that financial players would have preferred to avoid: the lack of standardisation and the variable quality of the data collected. Depending on the data providers, the formats, benchmarks or methods used to calculate the indicators may change and provide completely different results for the same theme. Not to mention that ESG data coverage varies significantly depending on the indicators and providers. As a result, there are real ‘blind spots’ that complicate the task of analysts. 

WeeFin's advice: Aggregating data to build a single, reliable source – a ‘Golden Source’ – is the cornerstone of an effective data management strategy. Support from ESG experts is essential to achieve this.

Challenge #3: A technical process to master

Given the complexity of the tasks to be performed, financial institutions cannot do without a technological solution capable of automating the collection, processing and analysis of large volumes of dispersed and heterogeneous data – up to tens of millions of data points!

But here again, there are many obstacles to overcome. First and foremost, a significant amount of work is required to reconcile ESG data with traditional financial indicators. This requires, for example, the use of complex version management and time-stamping mechanisms to ensure the consistency of analyses over time, as ESG data and traditional financial data often have very different time frames. Next, the quality and consistency of the data must be ensured through a series of checks. These are essential steps before moving on to the ESG rating stage.

Finally, the technical solution used must be flexible enough to adapt to all changes: evolving needs, integration of new data sources, new calculation methods, or regulatory changes. 

WeeFin's advice: To remain responsive and adapt to all scenarios, choose a modular architecture rather than a monolithic solution. And check the guarantees it provides in terms of ESG expertise, compliance and scalability.

Conclusion:

Faced with increasing pressure from the market and regulators, financial institutions can no longer limit themselves to a DIY approach to ESG data management. The implementation of a technological solution to industrialise the collection, processing and reporting of this data is essential. Specialised platforms such as WeeFin – which has a particularly powerful Data Management module – enable them to meet this challenge without developing complex and costly internal infrastructures, while benefiting from expert support on all topics related to sustainable finance.

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