Banks today face growing pressure from regulators, investors, and customers to back up ESG commitments with transparent and reliable data. Many have strong sustainability strategies, yet still lack the governance needed to manage their information effectively.
This challenge is now more urgent. A 2023 KPMG survey found that ESG data challenges are widespread. Only 25% of organizations have the systems and policies in place to support ESG assurance (required for many companies), and only 27% have robust policies and procedures in place to support the development of their ESG disclosures. This is the case even as regulatory scrutiny increases rapidly across global markets.
Though they may have sophisticated data systems and protocols for financial reporting, banks are one of the many industries unprepared for tightening disclosure expectations. In fact, banks feel the pressure more than most companies because their disclosures influence lending decisions, risk assessments, and market perception.
The rising stakes for ESG data
New regulations are transforming ESG reporting. The Corporate Sustainability Reporting Directive (CSRD) requires detailed information about climate risks, emissions, and transition plans. At the same time, the International Sustainability Standards Board (ISSB) has created global baseline standards that emphasize consistency and comparability across markets. Banks and financial institutions are also being held to finance-sector-specific regulations such as SFDR and OSFI B15.
These frameworks increase expectations for accuracy and audit readiness. Banks must track more granular metrics, maintain clear documentation, and ensure that financial and sustainability disclosures align. This shift from voluntary reporting to mandatory compliance raises the cost of poor data governance. According to EY, banks score only 46% for climate disclosure quality. This is a low score relative to other sectors, and is particularly concerning for a sector so dependent on climate and ESG data to assess risk and inform decisions.
Reliable governance helps organizations respond to these expectations with clarity and confidence.
Data quality is the foundation of ESG success
ESG data governance refers to the policies, processes, and controls that guide how organizations collect, validate, and manage ESG information. Strong governance ensures that data is accurate, complete, consistent, and ready for audit. For banks, this also supports alignment between sustainability information and core financial processes.
Common issues like mismatched emissions factors, inconsistent methodologies, or incomplete data from regional operations weaken the credibility of disclosures. The same EY study notes that banks show good qualitative alignment with climate-related financial impacts but lag when it comes to quantitative analysis. This gap exists because quantification requires consistent definitions, structured data lineage, and controlled assumptions, all of which rely on strong governance.
High-quality ESG data enables better decisions across lending, investment, operations, and supply chain management. When the data foundation is strong, teams can focus on insight rather than cleanup.
Common ESG data governance challenges
Many organizations still rely on manual workflows and spreadsheets. Teams pull information from different tools, compile data in shared drives, and follow inconsistent templates. This creates version control problems, siloed metrics, and delays that make year-end reporting more difficult.
A typical workflow may involve a sustainability team collecting energy data from facilities, a finance team calculating emissions using separate assumptions, and a risk team applying its own methodology for climate stress tests. Without shared standards, each group produces slightly different numbers. This becomes visible when disclosures must be consolidated.
These processes take time because climate-related data is not processed or controlled with the same rigor as financial data. This creates gaps in accuracy and consistency that make it difficult for banks to produce high-quality climate disclosures.
When no one owns the accuracy of a metric from start to finish, teams spend more time reacting to reporting deadlines than building long-term governance.
| Case study: How a global tech leader improved ESG data quality with automated disclosure checks A global technology company needed a clearer way to assess the accuracy and completeness of its climate disclosures. The team managed transition and physical risks across data centers and products, but there was no consistent method to verify data quality or benchmark against evolving standards. One team member explained that regulatory documents were “dense” and “confusing,” and that keeping up with changes often meant “starting from scratch.” By adopting Manifest Climate, the company introduced automated validation and cross-checking into its workflow. The team described the platform as “a one stop shop for doing your initial cross check among the various standards out there.” Automated benchmarking also replaced a two-week manual review process. What previously required about 80 hours could now be completed in roughly 10 hours, including setup. Most importantly, the tool improved confidence in the underlying data. As one user said, “Its cross-checking abilities gives us all the more assurance that we aren’t missing anything and can move ahead with doubling down on an existing initiative and filling in our gaps.” Clear audit trails and structured analysis helped the team identify inconsistencies early, strengthen data governance, and prepare for future regulatory requirements. Read the full case study |
Consequences of poor data governance
Weak governance can expose organizations to reputational and operational risks. Inconsistent disclosures increase the likelihood of misinterpretation. Regulators may request corrections. Investors may question whether the organization fully understands its risks. Companies may miss opportunities to use insights from ESG data for product design, risk forecasting, or strategic planning.
These outcomes are avoidable. With structured governance, organizations can treat ESG data with the same rigor as financial data.
What strong ESG data governance looks like
A mature governance program includes clear ownership structures, standardized definitions, consistent validation steps, and comprehensive documentation. These elements help organizations maintain accuracy over time and respond quickly to new regulatory expectations.
Ownership typically involves data stewards embedded in business units, centralized teams for consolidation, and oversight from finance, risk, or internal audit functions. Standardized taxonomies help teams use the same definitions for emissions categories, energy metrics, human capital indicators, and other ESG datapoints. Audit trails connect each reported metric to its source.
The most effective governance programs align ESG data with broader enterprise data practices. This avoids parallel systems and ensures that sustainability information fits within the organization’s existing control environment.
Cross-functional collaboration and board oversight keep accountability high and integrate ESG insights into financial decision-making.
How to strengthen ESG data governance
Organizations can strengthen their data governance by focusing on a few practical steps:
- Assign clear data owners and stewards for every material ESG dataset
- Establish shared definitions and taxonomies to ensure year-over-year and cross-team consistency
- Implement audit trails to trace metrics back to their original data sources
- Adopt tools that automate data capture, validation, and reporting workflows
- Build checkpoints throughout the year to identify missing or inconsistent information
Teams can also use simple self-assessment questions to understand their current maturity. For example: Who owns each dataset? What validation steps already exist? How often are assumptions updated? Are definitions shared across teams?
These steps create a more controlled and scalable approach to managing ESG information.
Moving beyond compliance
Strong governance supports compliance, but its value extends further. High-quality data makes it easier to run scenario analysis, evaluate transition risk, and incorporate climate considerations into credit decisions. Banks that integrate ESG data into their risk and finance systems can more effectively evaluate client exposure to physical and transition risks. And banks that commit to ESG data transparency can improve corporate reputations and social license to operate.
For example, an organization can use ESG data to assess whether climate-related events could affect asset quality or lending portfolios. It becomes easier to connect emissions profiles to capital allocation, underwriting, or product development.
The market is also shifting. Banks are expanding their use of physical risk analytics and adopting AI-enabled reporting tools to speed up workflows. As regulatory complexity grows, organizations that treat ESG data as enterprise data gain a competitive advantage. They can update systems faster, maintain audit readiness, and operate with greater clarity.
Building ESG governance maturity
Manifest Climate helps organizations centralize and structure their ESG data to improve governance at scale. The platform brings information from multiple sources into one place and provides frameworks that help organizations stay aligned with global standards.
Built-in benchmarking tools let teams compare their disclosures to peers and track how they measure up against ISSB, CSRD, and OSFI B15 requirements. Automated checks highlight gaps, flag inconsistencies, and identify areas that need improvement. These capabilities make it easier to maintain data quality year-round.
From fragmented data to actionable insights
Many organizations struggle with complex regulations, inconsistent methodologies, and limited resources. Manifest Climate simplifies these challenges by turning ESG data management into a more automated and reliable process. Instead of compiling spreadsheets or searching through past reports, teams can work with a centralized system that supports faster, clearer decisions.
Better governance builds stakeholder confidence. It also creates a stronger foundation for climate strategy, risk management, and long-term resilience.
Build credibility through data-driven ESG practices
Reliable ESG data strengthens reporting, supports risk management, and builds trust with stakeholders. Effective governance is the foundation for all of this. Manifest Climate provides the structure and tools organizations need to manage ESG information with confidence and lead with transparency.Drive trust and transparency in your ESG journey.
Book a demo to see how Manifest Climate can help you build stronger ESG data governance.
