Data and AI Belong At the Heart of ESG Initiatives

The primary cause of ESG failures is data: lack of data, lack of standards, and lack of understanding of how to use data.
Vincent RyanJanuary 25, 2021

Amid a tumultuous economic landscape and rapidly changing consumer expectations, companies have a new north star: sustainable transformation. For years, the C-level has focused on digital transformation to accelerate business and address consumer demands. So, some enterprise digital capabilities are becoming commoditized, especially following the COVID-19 pandemic.

Competitive advantage now hinges on sustainable transformation. Sustainable transformation builds on digital transformation and then incorporates environmental, social, and corporate governance (ESG) data and corporate social responsibility (CSR) goals into the products and services that a business commercializes. Over the next decade, we will see massive differences in performance between companies that operationalize sustainability and those that don’t.

ESG is already gaining widespread attention, yet most initiatives across industries fail. Why? The primary cause is data: lack of data, lack of standards, and lack of understanding of how to use data. With a competitive edge, data-driven companies win.

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Morgan Stanley recently illustrated that non-tech companies that specifically focus on investing in cloud, collaboration, automation, and data analytics see higher EBIT (earnings before interest and taxes) margins, higher price-to-earnings multiples, and improved revenues. Companies placing the same emphasis on sustainability as they make technological investments see similar returns; those who put data and AI at the heart of their ESG initiatives will build more resilient, sustainable, profitable, and attractive businesses. Here’s how.

Minimizing potentially catastrophic risks

With the COVID-19 pandemic being the most obvious, the past twelve months have brought no shortage of catastrophes. Last January saw the worst wildfire season in Australia’s history. Similarly, the West coast battled record-breaking wildfires. These catastrophes signal an alarming future, with experts anticipating more unpredictable climate disasters yet to come.

This grim outlook further validates the need for organizations to adopt a forward-looking approach to risk management. By taking a holistic survey of an organization’s stakeholders and vulnerabilities, data-driven ESG can do just that.

With a modern data architecture in place, organizations can quickly sift through all kinds of data, like past weather records or satellite imagery, to better model climate scenarios. Consider a national retailer. By leveraging data and artificial intelligence, it can run simulations for how climate events, be it a hurricane or a bout of wildfires, could affect consumer behavior and require store closures.

Closing the gap between brand perception and reality

Alongside COVID-19, 2020 brought a sweeping movement for racial equity, pushing organizations to take social and political stances. In response to demonstrations advocating for racial justice and police reform, major organizations like Apple and Nike demonstrated solidarity with protesters. Brands took to social media to have conversations with consumers; matched donations; announced inclusivity initiatives like board updates; and adopted Juneteenth, a holiday celebrating the emancipation of those who had been enslaved in the United States.

While these actions demonstrated a commitment to social good, consumers will expect this type of behavior from brands long after the headlines fade. A recent survey from Corporate Social Mind suggests that most Americans want companies to take positions on issues like racial discrimination. This is especially true for younger generations; a new study from Deloitte stated that three out of five millennials and Gen Zs believe business has shown a “genuine commitment to society during the pandemic,” yet these generational cohorts still don’t think business has a positive impact on society as a whole.

As businesses take these stances, a data-driven approach to ESG can ensure they measure up to their words and have a real impact. AI enables an organization to verify how it positions itself in terms of ESG and map progress toward those goals.

Consider a national grocer that advertises it only sells fair trade goods. Without data to back up its claims, consumers can only take the company’s word for it. The organization can tap into all kinds of data, such as farmers’ reported income level or reports about farming conditions where products are sourced, to determine whether all goods sold truly meet fair trade criteria. Additional AI techniques, such as embedding, allow the organization to understand how its fair trade pledge is discussed in the media, highlighting any potential dissonance.

Establishing operational resilience

As states across the country began issuing shelter-in-place orders a year ago, household goods like toilet paper disappeared from shelves in supermarkets across cities. Production for household goods was quickly ramped up to meet the high demand. Still, the alarming shortage signaled greater flaws in supply chains — an inability to predict and adapt to large-scale disruptions.

Economic fallout quickly followed. Stocks with higher ESG rankings, however, fared better than others. Bloomberg Intelligence found that at the end of March, more than half of U.S. ESG exchange-traded funds performed better than the S&P 500 index, with similar outcomes in Europe. That suggests that those companies responded and adapted better to the economic stressors posed by a disruption like COVID-19.

Much of this success lies in operational resilience, or a company’s ability to overcome operational disruptions by quickly anticipating and adapting to them. Look at the healthcare industry. In past months, hospitals have had to rapidly accommodate rising cases of COVID-19 among patients. By employing AI, hospital systems have been able to predict positive coronavirus cases from online screenings; identify high-risk patients with internet-of-things data; and determine optimal discharge timing through machine learning models. These changes will serve hospitals beyond the pandemic, helping optimize care and logistics amid natural disasters and other outbreaks.

After this year’s shifts in company operations and consumer behavior, organizations will emerge from the pandemic digitally transformed, at least at a foundational level. Now, organizations must ensure that their transformation is sustainable. Taking a data-backed approach to ESG allows companies to do just that: build organizations that can better adapt to the rapidly changing demands of stakeholders, the environment, and the economy.

Junta Nakai is the global industry leader for financial services at Databricks, where he is responsible for driving the adoption of the Unified Data Analytics Platform. Before joining Databricks, Junta spent 14 years at Goldman Sachs.