Posted on : August 10th 2021
Author : Sudhakaran Jampala
There are currently no mandatory compliance requirements around ESG reporting, especially for private companies, and such reporting is voluntary. While many large companies report on ESG as part of CSR, growing awareness among investors and consumers about ESG has led to this becoming a more widespread practice. Despite this upturn, many privately held firms do not have the inclination, resources, or stakeholder pressure. Thus, self-reported ESG data is challenging to come by in the case of these private firms.
NGOs tend to focus on public or large private firms’ activities because the information is generally easier to research. Thus, private companies typically suffer from both – a lack of voluntary reporting and an insufficient focus from external bodies.
A lot of data about private companies is scattered in news reports and company websites across several sources and spread over varying periods. Thus, there are complex issues around data acquisition for these companies. One source that can provide some input is news. Unfortunately, constant, round-the-clock surveillance of all news items by humans is impossible. However, automated and AI-based solutions can continuously search for references about a particular company through advanced text and image intelligence.
Also, ESG information covers a vast universe. Not all of it is required for a private company. For example, a private company’s operations could only be local, thus preventing the need to check whether their labor practices abroad are fair or ethical. Therefore, defining the components of ESG that are relevant for tracking could help in targeted searches and research. That way, the triad of text intelligence, public intelligence, and vision intelligence can be optimized for maximum impact for initiatives like data scraping.
Customized metrics, too, may have to be constructed for exceptional cases. For example, to measure ethical compliance around environmental protection, a metric around the ratio of total buildings owned or rented by a company to the number of green-certified buildings could be helpful. Currently, two available solutions can be tried in terms of reporting reliably on ESG for private companies.
One of the solutions is to hire a specialist ESG firm that focuses on private firms. Such specialist ESG companies are likely to have tools and models ready to extract information about any given private company that does not do voluntary disclosures nor is covered extensively by the media. Alternatively, private companies that enjoy good resources can consider building an internal division to analyze and report ESG data. For this, they may require consulting support.
In summary, ESG reporting for private companies is not an easy task due to a lack of readily available data or fully customizable ESG metrics. However, it is vital to extract the best from what is available. Thus, starting with media references, the search can branch out to more advanced and sophisticated sources such as satellite images if a situation so warrants. Again, AI and automation will be the key to trawling across scores of sources for references.
The process of data extraction involves identifying and recovering alternative and semi-structured data from various data sources such as files, XMLs, JSON, etc.
Capital markets are an excellent example of a perfect competition. The nature of the market is such the participants have to be competitive and result focussed. For instance, brokerages and investment banks have to deliver passive gains for their clients and, at the same time, earn a margin for themselves.
Today’s ESG analytics require processing data, patterns, and hidden connections to provide insights that investors, asset managers, and companies need. For example, Straive deploys advanced machine learning algorithms to analyze reams of documents to collect evidence across executive statements for signs of vagueness or obfuscation.
Talking about using data to gain insights is easy. But actually doing it will uncover a newer set of challenges, especially when it comes to unstructured data.
Integrating ESG data into commodities trading operations requires structured, easy-to-consume data. By their nature, ESG data resist such integration, and highly scalable data solutions across the data life cycle are needed to allow stakeholders to deploy end-to-end data solutions for a successful data-to-intelligence journey.
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