How Analytics Supports ESG Reporting & Corporate Sustainability Goals

Sustainability is no longer a buzzword, it's a business imperative. This can be seen in the staggering growth of companies reporting on their sustainability efforts in just two decades.

In 2002, only 18% of the top 250 global companies reported on their sustainability initiatives. Fast forward to 2022, and that number has skyrocketed to 96%. This shift toward sustainability can be attributed to the growing importance of Environmental, Social, and Governance (ESG) factors in corporate decision-making.

Investors increasingly incorporate ESG criteria into their investment decisions and push companies to adopt sustainable business practices. Additionally, regulators are pressuring companies to adopt sustainable practices and report on their ESG performance. Inflows into sustainable funds, for example, increased from $5 billion in 2018 to more than $70 billion in 2021.

The Rise of Analytics in Corporate Sustainability

Given the massive volume of ESG data created by a firm and its supply, understanding and deriving actionable insights can be daunting. Enter sustainability analytics.

Here’s how sustainability analytics assist businesses in realizing their ESG data and articulating the supporting narrative:

Depending on a company's sustainability goals, the application of sustainability analytics may be divided into three categories:

Next, we will examine the objectives of these categories and how sustainability analytics can be utilized.

Checking the Box – Complying With Regulatory Requirements

ESG reporting is primarily concerned with meeting the transparency demands of stakeholders. However, the expectation for ESG reporting has grown and been reinforced in recent years.

New disclosure regulations are being introduced internationally. Companies that do not comply will risk legal action, damaged brand image, and fines. For instance, the newly implemented EU deforestation due diligence rule compels businesses to verify that their supply chains for commodities such as cocoa and soy are unrelated to deforestation. Companies that do not comply might be fined up to four percent of their annual revenue in an EU member state. These forthcoming rules (e.g., SEC, CSRD) mandate ESG risk disclosure.

Racing to the Finish Line – Achieving ESG Goals

Compliant companies may employ sustainability analytics to drive progress toward reaching ESG objectives. This requires the correct data in a valid format.

However, sustainability data frequently sits in several silos and may be structured and unstructured within a company. Some of this data are gathered in batches, with some provided in real time.

The complexities of sustainability data can create a hurdle for firms wanting to advance their ESG objectives. AI and data science may assist in prioritizing sustainability projects based on possibilities uncovered over time, allowing businesses to focus on the most impactful operations. For instance, scenario analysis enables companies to examine alternative processes to evaluate where near- and long-term resources are best invested.

Striving to New Heights – Integrating Sustainability Into Core Operations

Best-in-class businesses integrate sustainability into their operations. These businesses perceive sustainability as a source of value, regardless of any implementation challenges.

Prescriptive analytics, in particular, can enhance company performance by including sustainability criteria. For instance, if a grocery store wishes to reduce food waste in its inventory, it may utilize prescriptive analytics for inventory management to optimize perishable food orders from suppliers and better align shelf-life products with consumer demand. Here, analytics prioritize the sustainability criterion of reduced food waste, but the store may also save money and increase sales by lowering food waste.

By employing sustainability analytics in this manner, companies can realize industry-leading sustainability maturity and positively disrupt their industry.

Establishing a Strong Data and Digital Foundation for Implementation

A business must have a solid digital and data foundation to adopt sustainability analytics. This can be achieved by identifying key indicators, gathering the appropriate data, and establishing data lakes. After completion, structured data may be transformed into a customized visual format using SQL analytics, data science, and machine learning.

For sustainability analytics to be effective, data standards and governance are essential. A system of policies, regulations, processes, and measures must be implemented to manage the use of data and guarantee that information is reliable, consistent, and treated properly. In addition, there must be a collaboration between functional teams and business partners along the supply chain.

Closing Words

In conclusion, analytics is crucial for measuring and optimizing organizational impact no matter where a company is on its sustainability journey. With the right resources and expertise, organizations can harness the power of data to achieve their sustainability goals and drive positive change.

d governance across functional teams.