From reactive to proactive ESG data management

As a result, companies must step up their ESG data management efforts. 

Effective data management practices and a robust data governance program can significantly help companies manage ESG data. Our insights suggest that specific strategies can further improve the handling of ESG data – and these strategies frequently encompass capabilities and approaches, rather than solely relying on tools or systems.

Deeper insigth into ESG data management in our previous blog

In this blog we explore common challenges of ESG data management and share best practices for tackling them. Whether your organization is just beginning its ESG journey, or aspiring to enhance existing practices, we hope you’ll find valuable ideas to help strengthen your approach.

Understanding ESG Data

ESG data – a specific data creature.

When people talk about ESG, they often think about sustainability data. While sustainability is a key component, it’s only part of the picture. ESG data encompasses a broader spectrum, combining environmental, social, and governance data. It includes facts about the carbon footprint, employee and board diversity, ethical practices, waste management, community engagement, fair compensation, and energy and water usage, just to mention some examples.

From a data perspective, giving an accurate description of a company’s ESG involves gathering information from various locations, often with differing quality and formats. The more complex the organization, the more challenging this process becomes.

Even within a single company, different business units may calculate ESG-related metrics using varying methods. Some units may calculate numbers from aggregated factors, dividing them based on specific measures. Others may obtain figures for the same issue from an IoT device in real-time.

Developing data literacy can help tame the ESG data creature, but knowledge of ESG itself is equally important. Managing ESG data requires both technical and strategic skills. Automated data lineage, combined with clear policies and governance, is essential.

ESG data caters to many needs

For regulatory compliance, companies must ensure their ESG data is accurate and reliable to meet evolving standards and avoid penalties. Investors rely on ESG data to make informed decisions and identify opportunities based on a firm’s sustainability practices. For customers, ESG data enhances transparency and trust, showcasing a company’s commitment to environmental and social responsibilities. And last but not least, current and future employees are often very interested in what a company says about its ESG targets and achievements.

Managing ESG data effectively pays off. It helps companies navigate regulations, attract responsible investors, become an employer of choice, and strengthen customer relationships. However, all these different contexts also pose a challenge to ESG data management.

ESG data isn’t just for regulators. It impacts investors, customers, and employees alike.

Regulatory requirements demand precise and timely data, largely in pre-defined formats. After addressing that, you have investors to consider. They seek transparency and comparability, which can be difficult to achieve due to fragmented reporting standards and the need for consistent, high-quality data. Especially comparability suffers from the lack of tightly defined data models for reporting. In some cases, investors can be more interested in what isn’t said or reported than the information provided.

When communicating ESG issues to customers and employees, the key challenge lies in providing accessible and understandable information that builds trust and meets their expectations.

Challenges in ESG Data Management

ESG data is often used for some other purpose first.

Context-awareness is one of the key components of successful data management. Understanding where and how certain data originated is imperative for an individual making business decisions based on that data, and crucial for an organization under an audit.

The importance of context-awareness naturally applies to ESG data as well. It cannot be discussed without considering its various roles and contexts.

Data, in general, can serve multiple purposes, and ESG data is often just “regular data” given a specific meaning in the ESG framework. In other words, raw data for ESG reporting often already exists before it is needed for ESG. HR data on salaries is collected to pay wages, and resource usage data might come from the supplier of the resource in question, e.g., water or energy. Thus, the context and meaning of each data item must be carried along with the actual data – metadata is needed to describe how certain data origins.

In contrast, community engagement data usually needs to be captured through surveys, interviews, or third-party assessments – but the process of community engagement is rarely straightforward, or a core process of the company at all. The upside is that in this context, the data collection can often be structured from the start to serve reporting needs.

Different stakeholders, different demands

To pile on the pressure, all data must be reported using a standard format in regulatory reporting. Customers, in turn, may provide their own templates for information submission. Additionally, the company may have its own templates for annual reporting or marketing purposes.

Transforming data from the source and handling systems to the final reports and reporting systems can result in metadata losses and loss of original context. This can lead to issues in later stages when additional questions arise regarding the reported data.

To recap, companies are now confronted with the challenge of managing data owned by multiple stakeholders (both internal and external). This data varies in quality and exists in numerous formats and contexts that are often not directly comparable. Additionally, it must be organized into several differently structured templates, each based on distinct pre-conditions.

Taking all this into consideration, is it even possible to proactively manage ESG data as a whole?

In short, the answer is yes – but you need a strong strategic approach that goes beyond just managing the data itself.

Strategies for Effective ESG Data Management - turning compliance into a competitive advantage

Navigating ESG data management is complex, but by taking a proactive approach, companies can turn compliance into a competitive advantage that drives business performance.

If proactive ESG data management is your goal, our recommendation at Tietoevry Tech Services is to start by assessing the current situation, for instance through a pre-study, and then designing a solid ESG data management framework to guide ESG reporting and related development. Based on these results, you can then start developing necessary capabilities, and we are happy to help you in this endeavour.

Here are some key steps to set you on the right path:

• Don’t depend solely on generic ESG or data management frameworks. Consider the unique features of your processes, data, and business – and then adjust a robust data governance and management framework to align with your organization’s specific ESG needs.
• Begin by developing fundamental capabilities through practical and essential business use cases, focusing on manageable tasks rather than attempting to solve everything simultaneously.
• To keep all the stakeholders happy, find some practical enablement use cases that are easy to implement and will provide immediate value (so-called low-hanging fruits), e.g., small-scale automation cases. These initiatives can be scaled and replicated in other contexts.
• Tie ESG data management to overall digitalization initiatives and data development items. If your organization is doing data productization, find an ESG use case where data products can be piloted.
• Set up a project management function or ESG data office to drive these cases. Provide it sufficient empowerment and authority to accomplish its tasks effectively.
• Remember that context-awareness is the key to successful data management. Knowing how and why data was generated prevents misinterpretation and enhances reliability. ESG data handling should always start at the sources: during stages where ESG-related data is generated and captured, and where it’s requested by stakeholders in a specific format.

AI can help – once your data is in order

As a final note, remember that AI can be a huge help in ESG data management – once your data is in order.

AI, particularly generative AI, is often viewed as a potential silver bullet for solving issues related to ESG data management, but AI is only as good as the data it relies on. Data is the engine oil for AI-based engines. 

Read more on improving data foundation on this blog 

Certain AI tools can definitely assist in managing data in its source, but a strong data management framework must first be built. At Tietoevry Tech Services, we utilize well-established data management and governance frameworks such as EDM Council DCAM for capability assessments and DAMA DMBOK for general data management frameworks. We combine best practises from these frameworks with real-world insights when working with clients on ESG data management (or any other data management projects) and customize our approach to ensure it aligns with each client’s unique requirements.

Ready to take your ESG data management to the next level? Let’s talk. My colleagues and I at Tietoevry Tech Services are here to help you navigate your ESG data – understanding its nuances, tackling ESG data management challenges, and building an effective data governance program.