As financial services face growing regulatory pressures around Environmental, social, and governance (ESG), they need to ensure they can capture all the relevant data.
Over the past few years, governments have implemented several regulations that aim to bolster the regulatory reporting requirements related to ESG activities. For instance, one of the most significant in Europe is the CSRD, which came into effect in 2023. It was later followed by an Omnibus Package in 2025, which delayed application dates and changed thresholds. However, it is still an important regulation that has changed the level of ESG reporting relevant firms must complete.
But the importance of data and analytics tools for ESG extends beyond reporting, effective tools are vital for areas such as wealth management. Over recent years, more investors have shown an interest in matching their investments with their personal ethics. This means many have started to invest only in portfolios that are ESG friendly. To accommodate this, firms will need to ensure the companies in certain portfolios meet ESG standards and be able to relay this information to their clients through reports when needed. A recent study from Capital Group found that 53% of companies cite the reliability of ESG data as the major challenge when looking to adopt ESG practices.
Why data and analytics is vital
Speaking to FinTech Global, Amrita Bhakta, Product Manager – ESG at IntellectAI, outlined why modern data and analytics tools are vital for ESG compliance. Bhakta said, “In the fast-paced, volatile business environment, technology is your aid and best friend when used rightly.”
This is particularly the case for ESG data. She noted that regulations are evolving quickly and vary between regions. Firms trying to manage ESG compliance through manual tracking or static spreadsheets will not be able to keep pace with change. Secondly, there is increased scrutiny from investors, boards and regulators. Companies will need to provide defensible ESG data, and inaccuracies or evidence of greenwashing can lead to significant problems, such as fines, reputational damage or delisting.
Bhakta also noted that ESG data is growing in complexity and volume, making it tough for teams to stay up to date. “ESG data comes from diverse, often unstructured sources (e.g., supply chain reports, sentiment data, carbon accounting). Technology solutions can help clean, standardise, and analyse this data effectively. Manual ESG reporting is error-prone and incomplete without integrating into data systems (ERP, SCM, etc.) and automation to capture full impact. Take the example of BP, which faced scrutiny for incomplete Scope 3 emissions disclosures. It also had to bear the brunt of ESG scores being downgraded.”
What to look for
It can be easy to be overwhelmed by the number of solutions available in the market. Unfortunately, there is no easy answer to picking the right tools. Every firm’s needs differ based on their legacy technology, objectives, and budget. Firms will need to assess tools to find the perfect fit, but there are some capabilities they can keep an eye out for that indicate strong data and analytics tools.
For Bhakta, one of the first things she recommends looking for is the completeness of the data. When looking for the ESG data solution, the data is where everything starts and the foundation of the analytics. As such, it is vital firms evaluate it on data completeness factors, such as quality, coverage and auditability.
She added, “Quality would include accuracy on both structured and unstructured data absorption and the degree of lag in the data.
“Coverage: Factors such as – Is there data for yet to mature topics, such as anticorruption, oceans, and tax, as well as relatively mature topics, such as climate change, human rights. Is there data availability for listed companies and private companies? This would indicate coverage of the data.
“Traceability and auditability – establishing lineage of the data is essential in today’s world, especially for compliance purposes.”
On the analytics side, Bhakta urged firms to look for regulatory and ESG framework mapping. “If the analytical tool can map the degree of alignment of the company’s disclosure against a regulatory compliance or framework, that would be gold.”
A couple of other factors to assess are the analytics prowess, as well as its AI integration for risk detection, NLP capabilities and its ability for large volume and complexity processing of data.
She added, “Top things I would look for in an ESG tool is multi-source handling capability with complete data lineage, regulatory norms and framework support, Predictive AI capability for risk analysis & trend prediction and last but not the least an ecosystem that ensures data privacy and security of my data. Governance guardrails are a must for any ESG data and analytics solution.”
Potential red flags
On the other side, there are also various aspects that can be early warning signs that a solution might not be the perfect tool. Bhakta also highlighted several red flags firms should monitor.
The first of these is the lack of transparency in data sources. There should be clear traceability of data, especially for compliance, and audit logs for improved auditability are also important. In the same vein, Bhakta warned against black box scoring methodologies. ESG scores or risk indicators that lack an explanation or access to underlying data logic can be problematic, she explained.
Next, Bhakta warned against using tools that are not flexible. She added, “Tools that only support high-level frameworks or are slow to adapt to new regulations (like CSDDD), will not be able to support the organisation as it matures along its sustainability journey. This can also lead to regulatory fines and reputational damage.”
Finally, she noted solutions with proprietary formats or limited interoperability with other tools or systems can be difficult to use as they can lead to redundancy and repetition of effort on the ESG team.
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