Data Analytics and Sustainability: What Investors Need To Know

data analytics and sustainability

Now that sustainability investing is becoming mainstream, there will be increased scrutiny on asset managers to show their workings on sustainability data analytics. The onus will be on investment practitioners to thoroughly screen portfolio companies for greenwashing.

Intensified in 2020 by the Covid pandemic, an increasing number of investors are pursuing strategies that take into account environmental, social and governance (ESG) factors. But there are still many challenges when it comes to collecting accurate and reliable ESG data.

Find out what constitutes sustainability data, how it’s sourced and processed, and discover how new technologies are helping to improve the accuracy of ESG assessments.

What Is Sustainability Data?

Currently, sustainability data isn’t standardised. There’s no standard set of metrics used across the board to easily compare ESG compliance between companies. That means that different companies measure different KPIs or they use different methods to obtain the same metrics with varying levels of accuracy and reliability.

Here are some examples of KPIs used in sustainability analytics:

  • Annual emissions
  • Polluting emissions per unit of production
  • Water stress
  • Electricity use per employee/customer
  • Waste production and control
  • Fines or infractions for not complying national and international standards
  • Product life cycle and disposal

Other metrics relating to social and governance factors include:

  • Workforce diversity
  • Gender pay
  • Gender composition of the board
  • Labour practices
  • Workplace related injuries

Some companies selectively focus on what they consider to be the most “relevant” ESG data for their industry, while others give a more comprehensive set of information across all ESG factors.

How Do Investors Get This Data?

ESG reporting isn’t mandatory, but an increasing number of companies recognise the value of publicly disclosing ESG data. ESG reporting is particularly transparent within the public sector, though private equity assets are slowly following suit.

Such outlets include:

  • Annual reports
  • Corporate Social Responsibility (CSR) reports
  • Disclosure surveys from third-party non-profit organisations such as the Carbon Disclosure Project (CDP)
  • Satellite and sensor data

If a company doesn’t publicly disclose ESG surveys, in some instances you can directly source them from these third-party data providers. Some of these organisations offer fee-based subscriptions to access their full database, helping to facilitate ESG comparisons.

An increasing number of companies disclose their information to these third-party organisations. The CDP reported that in 2021 over 13,000 companies (worth over 64% of global market capitalisation) disclosed data through them, a 35% growth since 2020.

The Problem with Sustainability Data

Promising as this is, there are several issues regarding the consistency and accuracy of these disclosures. For example, companies often fail to fully disclose all emissions data across their supply chain.

CDP report (PDF) indicates that of all the food value chain companies that disclosed data to the CDP, only 16% of climate change disclosers engage with all levels of the value chain.

Until it becomes mandatory to disclose sustainability data and relevant data is clearly defined, the challenge of sourcing and centralising information continues. This means that investors still need to conduct their own research, drawing from numerous sources to get a full and honest understanding of their assets’ environmental impact.

How AI is used in Sustainability Data Analytics

Parsing relevant information

ESG research is time-consuming, especially when you consider that data is sourced amongst lengthy annual reports and CSR reports.

Relevant information is often embedded amongst paragraphs of unstructured text. Manually leafing through these reports is not only a lengthy process, it’s also draining on the researcher, increasing the potential for human error.

Investors are turning to AI technology to expedite sustainability data analysis. Natural language processing (NLP) technology, such as we offer at Symanto, can automatically identify relevant excerpts and run sentiment analysis on reports to assess a company’s commitment to ESG issues.

Corroborating claims

It’s easy for a company to write sustainability into their corporate communications and publish them on their website. But taking action and enacting meaningful change requires more commitment. It isn’t enough to take sustainability claims at face value or send out box-ticking ESG surveys.

NLP text analysis technology is being used to to weed out companies that give a false or misleading impression of their ESG commitments. Compare the sentiment used within corporate communications against data such as annual emissions and international standard infractions to see whether this data aligns with their sustainability claims.

You can also apply the NLP technology to other sources such as news reports, employee reviews, social media and blogs to either corroborate or disprove claims made in corporate reports.

Reduce risk

AI algorithms can quickly identify patterns in large datasets and provide insights into potential risks such as polluting suppliers or unsafe working conditions.

Enhanced transparency

AI can be used to create a more transparent reporting process and help companies track their supply chains, giving them better insight into their sustainability performance. Investors can then use this data to evaluate whether investments align with their ESG goals and create a positive impact.

Make predictions

AI can help investors predict the likely impacts that investments may have on social, environmental and economic factors by analysing data from previous ESG-related decisions.

AI can also be used to identify possible cost savings or efficiency gains associated with companies’ sustainability endeavors.

Get Started With Symanto

At Symanto, our NLP technology helps ESG investors and asset managers improve the integrity of their portfolio.

Our agile team of data scientists, AI researchers, and industry experts develop bespoke solutions that can help you unlock the potential of your ESG data.

To find out how we can help make sustainability data more accessible and actionable for you, get in touch today.