text: More and more, businesses are recognizing that incorporating Environmental, Social, and Corporate Governance, or ESG, into their products and services directly correlates to financial results. Doing good is no longer at the expense of doing well. For both stakeholders and customers, ESG represents another point in their decision making. As allocators of capital, financial services firms play a critical role in shaping a more sustainable and equitable future.
  For all the volatility in 2020, one bright spot was that financial services firms have further embraced ESG as a revenue opportunity with regulatory importance. Per an FT.com report, ESG exchange-traded funds, or ETFs, doubled globally in 2020 to over $120B. Similarly, investment banks are helping companies issue green bonds at a record pace. ESG represents significant business opportunities across the Buy-side and Sell-side.
  Quantifying and verifying performance around ESG metrics is difficult and requires AI and ML to spot trends, help determine materiality, and uncover value. Therefore, the foundational building block of ESG starts with data engineering.
  I had the opportunity to discuss this in more detail with Junta Nakai, Global Industry Leader of Financial Services and Sustainability at Databricks. Databricks is a data engineering, science, and analytics company and a partner of Microsoft. As Nakai put it, “Eighty percent of the data used for ESG is non-numeric. It’s unstructured data — text, images, IoT, streaming, and other complex datasets. To compound that, there are no standards for this data.”
  The lack of standards is certainly worth examining. Today, there are no enforced standards or accountability when it comes to ESG disclosures. It is incumbent upon a company to figure out what, how, and when to report their ESG goals and metrics. Once that is done, the banker, analyst, or investor needs to make sense of what’s been reported. As a result, both financial services firms and the corporates they work with or invest in have a blind spot when it comes to ESG analysis.