The Prudent Pursuit of ESG Initiatives for Data-Driven Organizations
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Over the last few years, there has been a strong focus on environmental, social, and governance (ESG) concerns. ESG represents the standards used by socially conscious investors to screen potential investments. It takes a holistic view that sustainability extends beyond environmental issues, and it helps investors to better understand how different organizations manage risks and opportunities related to sustainability.

Despite being in practice for many years, there has been no standard ESG framework barring a broad consensus on the issues that it covers. However, with the recent input from various government and non-governmental organizations and the increase of environmental and sustainability concerns in the public consciousness, ‌ESG has gone mainstream, with an increasing number of ESG rating agencies and frameworks.

The Data Challenges of ESG 

Organizations will always need to ask how well they understand their ESG metrics in light of how well they are performing against their ESG goals. To build such matrices and reports, companies need access to highly available data that is complete, reliable, and well-governed. ESG data can take multiple forms, especially in large organizations, where data resides across multiple sources, in varied formats. This data can be directly related to the environmental pillar (data about energy consumption, carbon emissions, water consumption, or waste management), the social pillar (data about health and safety, human rights, labor rights, diversity, or inclusion), or the governance pillar (data about accountability, ethical conduct, or executive remuneration).

Gathering this data and contextualizing it in the right manner through unified data and analytics, becomes important to getting the right insights to take the appropriate measures to meet ESG goals. IDC states that 90% of existing data is replicated. If we think of this percentage in the context of the amount of money and resources organizations are spending on maintaining and storing this data (energy costs and environmental impact), it should make organizations consider their current practices to see if they are doing enough to reduce their carbon footprint before they claim to do it for their organization. Therefore, the solution to this challenge starts with efficiently collecting and integrating data. It is only through collecting data from disparate sources in the right way, that is, integrating data without replication and the unnecessary buildup of data pipelines, in a way that does not complicate the data infrastructure and increase IT overhead, and in a way that makes it easy for organizations and investors to prove that ESG means something and that they are here to make a positive impact. 

The Role of Data Virtualization in ESG  

Data virtualization, core to the Denodo Platform, is a widespread technology when it comes to integrating vast amounts of inconsistent and incompatible data from heterogeneous data sources, without creating a data sprawl or a data pipeline mess.

One of the largest European investment banks wanted to ensure that its investments go toward companies that are well-governed and socially and environmentally compliant. However, one of the bank’s challenges was the inability to effectively integrate third-party data sources, which it needed in conjunction with internal data sources, to create ESG models for gaining a fuller understanding of the companies in which the bank invests. This triggered the bank to implement data virtualization through the Denodo Platform.

This data virtualization layer integrates all kinds of data sources, regardless of their format, efficiently and intuitively. By using APIs through the Denodo Platform, the bank can connect internal, proprietary models that have been developed in different departments of the organization, for holistic investment analysis. Since implementing the Denodo Platform, the bank has been able to produce ESG views of its portfolios in record time, enabling the company to compare market signals with its proprietary models. This, in turn, has enabled fund managers to manage the ESG risk of their portfolios, making investment decisions to reduce exposure to industries that are harming the planet or have a high social-cost-to-impact ratio. The bank is also now on track to unlocking enterprise data for secondary reuse by analytics and quantitative teams, to create an internal sharing economy.

ESG may be becoming a priority for organizations across industries, but the will is not enough to excel on the ESG front. It is only through the amalgamation of the will, the right practices, and the right enabling technology that organizations that can deliver on the ESG promise without falling prey to over-hyped expectations and fast-changing market conditions. 

Sutender Mehta