As governments and regulators are scaling back sustainability disclosure requirements and postponing compliance and reporting deadlines, many organizations (including most public companies) are continuing their pace voluntarily and undeterred, while others are breathing a sigh of relief and starting to think where to re-allocate their time and resources next. Our suggestion: Use the regulatory lull to strengthen your sustainability infrastructure and governance and get your data management in order.
Honing your data game is a good idea on any day, for almost any reason. Data management initiatives are strategic initiatives, where considerations like automation and AI further highlight the need for secure, trusted and resilient data. When it comes to sustainability data specifically, there is much that still needs to be wrangled – new internal and third-party data to be sourced, data management processes defined and risks managed. (This is true regardless of whether a company reports voluntarily or under a regulatory regime.)
While regulatory pressure to acquire and accurately report that data may be easing for the time being, other kinds of pressures are not – investors still want to know, by the numbers, how resilient or prone to climate risk your business is; your customers still want to know if your products are represented accurately as sustainable; and you operational managers and finance leaders are still keen on knowing where they can optimize resource usage to cut down costs. None of that can happen without trusted data.
Why design a sustainability data management program
Most companies have some kind of data management program in place and may view the management of sustainability data as just another use case in which to apply the catalog of data management disciplines.
While that’s true, sustainability data also comes with some unique challenges. For example, much of the information required to produce reports exists outside the corporate ERP system, in spreadsheets, PDF reports, investor narratives, utility bills and other structured and non-structured formats. In some instances (e.g., climate transition risk), the data required and the best ways to consistently capture it are still being determined.
Figuring out how to bring all this information into a sustainability data management program is a good challenge for companies to tackle right now. It will build corporate-wide understanding of sustainability objectives, foster discipline around sustainability reporting, mitigate current and future regulatory and reputational risks stemming from data inaccuracies, and prepare the enterprise to provide investors and other stakeholders with data in comparable formats that will help companies compete for better funding and returns.
Key components of a sustainability data management program
The goal of a sustainability data management program is to establish a robust single source of truth from which the company, leveraging a combination of human specialists and AI agents, can draw insights, generate reports and track sustainability goals. The five components below form the basis of a sustainability data management program:
- Data identification and scoping. This includes identifying relevant data based on the company’s sustainability objectives and relevant regulatory frameworks; establishing data boundaries or scope of data collection (for example, by geography or time period); and identifying data categories (e.g., GHG emissions, energy and water usage, waste generation) that allow the tracking and measurement of progress.
- Data collection and storage. This includes identifying data collection methods (e.g., manual, sensors, third party); developing data collection tools and processes; implementing data quality controls (validation, verification); and storing sustainability data as securely as other publicly disclosed data to protect corporate integrity.
- Data governance and management. This includes assigning roles and responsibilities for data collection, management, analysis and reporting; establishing data quality standards; implementing data security measures; educating data owners on best data practices; and proactive and ongoing monitoring of third-party data sharing using third-party risk management principles.
- Data analysis and reporting. This includes cleansing, standardizing and organizing the data; identifying patterns, trends and insights; and generating reports on sustainability performance and progress for the appropriate audience.
- Continuous improvement. This may include periodic review of data management processes; integrating feedback from stakeholders; and staying abreast of sustainability reporting frameworks to ensure ongoing alignment and compliance.
Watch for pitfalls and manage the risks
As we said earlier, sustainability data comes with its own unique challenges and risks. They include:
- Source complexity: Managing large volumes of sustainability data from disparate sources can be complex and resource intensive.
- Data accuracy: Sustainability data often involves complex metrics and measurements, which can lead to inaccuracies if the data is not collected and managed properly.
- Compliance risks (postponed, not gone): Certain jurisdictions (notably the EU’s Corporate Sustainability Reporting Directive) require data used in reporting to be presented in specified formats. Failing to comply with these regulations can result in fines, penalties and legal and reputational consequences for businesses.
- Data privacy and security: Sustainability data may contain sensitive information about a company’s operations, suppliers, employees or stakeholders.
Mitigating these risks requires thoughtful governance, properly designed data quality controls and cross-functional collaboration among various departments. Companies may want to perform independent reviews, internal audit checks and external audit readiness tests. We recommend engaging third-party assurance providers where the regulatory risk is high.
Sustainability data management tools and considerations
Most ERP solutions already store some (but not all!) of the data needed for sustainability purposes and possess the core technical capabilities to ingest and analyze it. In addition, many ERP systems today offer sustainability modules, facilitating the management of sustainability data across the entire lifecycle. Thus, extending an ERP system into the sustainability domain is a natural fit and a logical first step for companies.
However, given the plethora of topics encompassed within sustainability and the breadth of business use case requirements, a larger ecosystem of technology products and services may be required to meet these needs.
There are many tools in the market advertising their capabilities and competing for a place in that ecosystem. Not all of them are created equal – some are mere topic-specific data collection tools, others are designed to enable broad data collection and robust data analysis and modelling, while others specialize specifically in reporting and disclosure.
An organization will need to understand its desired sustainability outcomes to effectively evaluate its data collection, analysis and reporting needs and/or obligations before deciding how to build or supplement its tooling ecosystem. It will also need to ensure that the technology infrastructure it builds or the tools it invests in are flexible enough to adapt and evolve in synch with the fast-changing regulatory pace.
The hardest use case to solve
One benefit of solving the ESG data puzzle is that it is one of the hardest to solve, as information from such disparate sources as HR, finance, operations, facilities, suppliers and third-party data sources (both structured and unstructured) needs to be consolidated into a single narrative or report using quantitative and qualitative information. Giving the proper attention to this challenge now while regulatory pressure is lower will raise organizational data competence and build up the data technology stack, positioning companies to not only meet their sustainability reporting obligations with confidence but to also become stronger, data-driven competitors in the market.
Do you have questions about setting up your sustainability goals, strategy or data management program? Contact the authors or visit us.
