The Blueprint for Data Strategy in ESG: Why it's Important and Where to Start (2024)

In the last article, we established that ESG factors are a growing consideration for businesses looking to drive sustainable growth and demonstrate their commitment to responsible corporate citizenship. We also established the importance of data in demonstrating an organization’s ESG impacts and driving ESG performance.

However important, organizations are consistently struggling to effectively use data in the ESG context. With increasing demand for transparency from investors, regulators, and consumers, many organizations will need to turn to a ESG-focused data program to deepen their ESG data capabilities.

In the next few articles, I will cover key components of an ESG data program like data strategy, data governance and management, architecture and value creation. This article focuses on the first topic – data strategy.

Sidenote: I am cognizant that not all readers may be exposed to data-related terminology so let's start with some definitions.

The Basics: Some Broad Definitions

Before diving into the crux of the topic, let's take a moment to cover some important terminology related to a data program. To simplify, I'll use the water cycle as an analogy. Here goes:

1. Data Lifecycle: The data lifecycle is the journey of data from when it's created to when it's not needed anymore. It includes steps like creation or acquisition, storage, use, and disposal. The lifecycle is a tool we use the chunk different things. In our analogy, it is the conceptual depiction of the different steps of the water cycle.
2. Data Architecture: Data architecture is like a blueprint for how data is handled across the data lifecycle in different systems. It helps make sure data is organized in a way that makes sense and is easy to use. In our analogy, it would be the lakes, the clouds, the rivers, etc. - things that hold or transport water.
3. Data Model: A data model is like a map that shows how different pieces of information are connected. It helps users understand, connect, and work with different data sets effectively. In our analogy, think about how water from multiple sources can come together to form a larger river.
4. Data Governance: Think of data governance as the roles and responsibilities, escalation routes, and guidelines for handling data. It ensures that data is handled in a way that's secure, accurate, and follows the right procedures. In our analogy, it would answer the questions: Who gets to use the water? Who makes sure it’s clean and safe to use? Who do I tell if there is an issue?
5. Data Strategy: The data strategy is a plan for how an organization uses and takes care of information to help the organization succeed in its goals. In our analogy, it would answer the questions: What do we want to do with the water we have? How can we make the best use of it in the long term? This will inform how we clean it, store it, etc.
6. Data Management: A broad term that encompasses all the processes and activities related to acquiring, organizing, storing, and using data within an organization and encompasses all the above. In our analogy, it is the culmination of all the processes the water goes through.

Don't worry, you don't need to memorize all of this. However, it's good to understand that there are quite a few different practices involved in a data program.

The Relevance: Where does Data Strategy Apply to ESG?

Brace yourselves – we are going to take the scenic route to answer this question.

ESG is a complex topic. ESG factors affect all facets of business and therefore they are usually embedded within the strategies of different functions. To reconcile these, organizations have initiatives across different areas such as Climate (e.g. Net Zero, Decarbonization), Inclusion and Diversity, Sustainable Supply Chain Management, and others. These initiatives also feed into an organization’s ESG strategy, which, at a high level, lays out how to integrate ESG factors into the broader corporate strategy.

The ESG data strategy then complements these initiatives and the overall ESG strategy by laying out how data should be used, the gaps in the data when compared to the needs of the initiatives, and what needs to be done to fix those gaps.

In essence, an ESG data strategy provides the step-by-step to help organizations use data to advance their ESG initiatives. It's a key driver for accurate reporting, data-driven decision making, risk mitigation, stakeholder trust, and overall adaptability and resilience.

The Blueprint: Key Components of a Data Strategy for ESG

Building an ESG data strategy is complex. So to break it down, here are a few key components. Keep in mind that there is no one size fits all, but rather these components can act as the bones of the strategy.

Establishing Clear Objectives and Priorities

Before delving into data collection and analysis, it is essential to define specific ESG goals that align with your organization's overall ESG strategy and disclosure requirements. It is important to consider which ESG factors are most relevant to your industry and stakeholders. These objectives will also play a crucial role in prioritizing your data needs.

Identifying Key ESG Metrics

Once objectives are established, the next step is to identify the key ESG metrics that will aid in tracking and reporting progress. These metrics should be specific, measurable, and relevant to your business operations. For instance, in the environmental category, metrics could include GHG emissions, water usage, and waste production. For each objective, there might be one or many KPIs – each KPI requiring data from different sources and varying thresholds of data quality.

Focusing on Streamlining Data Collection and Quality

To gather accurate and reliable ESG data, it is crucial to establish systematic data collection processes. This involves integrating data sources across various functions through a unified ESG data model and architecture. Implementing validation checks and data governance protocols helps minimize errors and inconsistencies. Regularly verifying the accuracy of your ESG data ensures alignment with industry standards and reporting frameworks.

Promoting Cross-Functional Collaboration

As mentioned earlier, ESG initiatives necessitate collaboration across different functions within an organization. Establishing clear communication channels and cross-functional teams ensures that relevant stakeholders are involved in data collection and reporting processes. This collaborative approach breaks down silos and fosters a unified approach to ESG data management.

Embracing AI and Automation

New AI tools and automation techniques can significantly accelerate ESG data management. These tools automate data collection, validation, and analysis, allowing for more efficient and accurate reporting. Additionally, they can uncover insights and trends that might otherwise go unnoticed.

Aligning to Regulatory Requirements and Strategic Imperatives

The regulatory landscape for ESG reporting is continually evolving along with how businesses are responding. Staying informed about the latest guidelines, standards, and reporting frameworks relevant to your industry and region is crucial. This knowledge helps align your data strategy with current and future compliance requirements, as well as changing corporate strategy objectives.

The Path Forward: Where to get Started

Embarking on a journey towards building your foundational ESG data strategy can begin today. There is a real opportunity for organizations to use the market's momentum in their favour to build our their ESG data program and the strategy is what holds it all together.

I hope you found this insightful. As usual, if you have any questions, don't hesitate to reach out. In the following articles, I will talk about other parts of a data program, and how they come together in support of the strategy. Bye for now!