Data Science: Your Ally in Achieving ESG Goals

By Vicent Benet and Albert Castella, Executive Managers

Social and environmental responsibility has become very important in recent years. The European Directive on Corporate Sustainability Reporting (CSRD), published in 2022, requires large organizations to report a significant amount of ESG information starting in fiscal year 2025. The growing development and evolution of the legal framework around sustainability has generated a lot of interest in large companies, who face the challenge of capturing and consolidating heterogeneous information to comply with regulations. In this case, data analysis plays a crucial role in the effective implementation of ESG (Environmental, Social, and Governance) policies.

The Importance of Data in an ESG Strategy

A good data strategy is key to achieving sustainability objectives. Having a single platform where quality data is centralized is the fundamental basis for streamlining the process of creating regulatory reports, defining an effective sustainability plan, allowing continuous monitoring of its execution and carrying out the necessary corrective actions.

Companies with a solid technological and organizational foundation around data will be at an advantage, as much of the information required by regulations will already be available on their platform. Data on Human Resources, purchasing, production, logistics and sales are some of the assets needed for analysing corporate sustainability in three main areas:

1. Measuring Environmental Impact: This is necessary to control greenhouse gas emissions in the supply chain, water and energy consumption in production facilities and even waste generation and management, which includes pharmaceutical products.
2. Evaluating Social Practices: Diversity and inclusion in the workforce, as well as cross-sector collaboration to share good practices, are some examples. Equitable access to medicines in disadvantaged communities or ethical practices in clinical trials and marketing can also be analysed.
3. Governance and Regulatory Compliance: It is essential to control transparency in drug pricing, risk management in the supply chain and, of course, compliance with ethical and regulatory standards.

Having a unified data platform, however, will not guarantee success on its own. There are three major challenges in the process of collecting and analyzing ESG data; which also influence information management. Firstly, the availability of the data, which is accessed directly and easily, or on the contrary, it may belong to other agents in the supply chain. In this sense, indirect calculation methodologies must be defined or collaborations with clients and suppliers must be explored. Secondly, it is important to study the quality and detail of the data in order to define actionable and measurable plans and identify the origin of inefficiencies. The last challenge is the automation and frequency of the analysis. Automating the data collection process allows for quarterly, monthly and even ad-hoc analyses to be carried out that help to identify deviations from the sustainability plan in advance and propose actions to achieve them.

Strategies for Effective ESG Data Management

Beyond the definition of a clear strategy around data, there are other complementary initiatives that ensure good adaptation to the new ESG data environment and its regulatory framework.

1. Analysis Phase: It is important to functionally define all the data points required by the regulations, to prioritize the capture of data with a greater impact in ESG terms.
2. Market Products Versus Customized Solutions: Specialized products simplify the process of creating, sharing, and certifying regulatory sustainability reports. However, the functional analysis process and the automation of data capture are still necessary steps that current products do not effectively cover.
3. Use of AI: Artificial Intelligence can help automate data capture from unstructured sources, such as customer and supplier documents.
4. Collaboration with Suppliers and Customers: This is crucial to obtain complete and accurate data throughout the value chain. In this sense, the analysis phase - mentioned above - is important to prioritize collaboration with the agents with the greatest impact and define temporary or alternative strategies for the rest.
   
   

In addition, it is important to present a sustainable, efficient and responsible vision in the design and implementation of this type of project. To this end, some sustainable technological trends are emerging strongly in the market, such as Carbon Aware Computing, which proposes the development of algorithms with greater computational efficiency, and Circular Data Economy, which allows the efficient reuse of data to reduce storage costs. Of course, the development of responsible AI that detects biases in models and ensures privacy in data processing is very important.

In short, Data Science is the backbone of an effective ESG strategy in the pharmaceutical sector. The power of data enables companies to measure and report their impact, while driving positive and sustainable change in the industry and society at large. Likewise, ESG data integration also supports strategic decision-making. Pharmaceutical companies that master ESG data management and analysis will not only meet regulatory and investor expectations, but will also discover new opportunities for innovation and value creation. At SDG Group we are committed to helping our clients achieve all their ESG objectives through Data Science.

Original article published on PMFarma, here.