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:
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.
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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!