October 23, 2023
In the evolving corporate responsibility landscape, one critical but sometimes overlooked lever financial institutions need to pull in their environmental, social and governance (ESG) strategy is data.
Strategic data management is paramount to implement ESG objectives successfully. Financial institutions that align their ESG data strategy with external policy and market standards and concurrently refine their internal processes and operational methodologies can influence their impact on people, planet and profitability.
However, effective data management is challenging on its own. Layer on ESG goals and many companies need help finding their footing. “Data strategy for ESG: Leveraging data to achieve your sustainability goals,” a recent Mastercard Trends report, looks at how companies should integrate effective data management in their ESG roadmap and outlines solutions for four distinct challenges. First, it’s important to understand why data management matters to ESG.
What is data management, and what does it have to do with ESG?
Effective data management and analysis helps financial institutions (FIs) integrate sustainability into their core business practices, enabling them to:
Identify and assess ESG opportunities and objectives
Manage risk
Prioritize initiatives and allocation of resources
Measure and monitor their ESG performance
Communicate their efforts transparently to stakeholders
Improve their processes continuously, thereby ensuring their long-term sustainability, adaptability and impact
A well-conceived data strategy enables financial institutions to provide evidence and transparency into the data they collect and to validate their compliance of ESG standards to investors and stakeholders.
It also reduces the risk of “greenwashing,” potentially damaging the organization’s reputation and impacting customers, suppliers, investors and regulators.
Moreover, a robust data strategy fosters discipline, facilitating close monitoring of ESG measures through automated tracking of key performance indicators. Given a high-quality data environment, it's possible to create statistical models that generate ESG performance scores and detailed insights into the results. A company could receive a high score if it demonstrates improvements in various ESG factors over time, including reducing GHG emissions, improving employee welfare and customer satisfaction, or strengthening corporate governance and ethical decision-making.
What are the main challenges of implementing an ESG data strategy?
Most FIs have well-established data processes, so implementing a new data strategy incorporating ESG data and enabling effective ESG impact analysis can be a significant challenge.
FIs may need to reference external data from trustworthy sources or statistical indicators when adhering to evolving regulations. Establishing a robust internal data strategy is a critical first step.
Companies may leverage the expertise of third-party advisors to create and implement ESG system analyses that incorporate internal company data and external vendor data. However, the greatest challenge companies face when implementing ESG strategies is defining ESG metrics. This lack of standardization in ESG data and reporting requirements makes ESG performance difficult to compare. Indeed, several studies have shown that ESG data providers can produce vastly different ratings for the same company.1
In the financial services sector, the top data-specific challenges that have emerged in recent years include:
Low data quality
Increasing data costs
Lack of data definition and comparability
Lack of data coverage across all E, S and G components
Lack of data consistency within and across sectors
Lack of data transparency
No clear data strategy and connection to external standards and regulation
Moreover, when integrating ESG data into a company’s organizational structure and data architecture, the main peripheral challenges identified by businesses include:
Functional silos
Organizational silos often lead to disjointed processes and a fragmented data architecture that does not allow for synergies across ESG use cases. While some redundancy might be unavoidable, there is often a substantial overlap across data needs.
Effective ESG data governance requires a coordinated and centralized approach across multiple stakeholders. Organizations should foster cross-functional collaboration, diminishing the barriers of functional silos, and ensure that diverse teams work towards common ESG goals.
Process traps
A narrow focus on simplicity and standardization can complicate or prevent alignment with existing business and IT processes. However, anchoring too much on legacy processes creates unnecessary complexity and can hamstring the ESG data governance model.
Embracing continuous improvements while actively involving stakeholders can help organizations avoid falling into process traps.
Resistance to upgrading legacy IT interfaces
While it may seem like an adequate short-term solution, delaying incorporating necessary new components into a complex architecture creates operational risk in the mid-to long-term.
New ESG regulatory and business imperatives present an opportunity for banks to revise their existing enterprise architecture framework. While resistance to IT upgrades can be significant, showcasing the advantages of new systems through pilot projects and easing transitions with staff training can alleviate concerns.
Lack of ESG expertise
A specialized understanding of policies, standards and alignment can help with data collection, reporting, capital allocation, risk frameworks and emissions compliance.
Businesses can either onboard specialists or cultivate a culture of continuous learning internally, often benefitting from external industry forums and partnerships.
Learn more about designing an effective ESG data strategy in “Data strategy for ESG: Leveraging data to achieve your sustainability goals” exclusively on Markets Trends.
