Posted on : June 24th 2022
Author : Praveen Vaidyanathan, ESG Practice Head at Straive
Often associated with financial analysis, alternative data is a term that typically refers to externally sourced information about a particular company to gain additional business insights. Common alternative data types include customer credit and debit card transactions, emails, geo-location (or foot traffic) metrics, mobile application usage, satellite or weather data, social and sentiment data, web-scraped data, and generic internet traffic, among others.
However, alternative data analytics is in an early adoption stage. Although some specialized data vendors sell alternative data to various enterprise stakeholders, many companies currently only explore alternative data for its potential value.
With environmental, social, and corporate governance (ESG) investing gaining prominence, not being able to trust company-reported ESG measures is a key industry issue. Greenwashing, the deliberate practice of making a company appear more sustainable (or green) than it really is, is a public relations and moralized corporate marketing exercise to appease conscientious investors. Because of greenwashing, investors are seeking methods to validate a company’s ESG credentials.
Alternative data sets are emerging as a critical element in the toolkit when developing a holistic and reliable ESG picture of a company.
Because traditional self-reported ESG data, such as company corporate social responsibility (CSR) reports, are released at particular times, they lack a real-time connection. But alternative data offer a quasi-real-time view of market sentiments using various routes, for example, social media chatter, news articles, satellite images, independent nongovernmental organizations’ reports, and others.
Hedge funds and investment banks have advocated using alternative data regarding ESG reporting for years. Because unstructured and alternative data typically do not have definite formats, such as a logical tabular row-and-column structure, or a predefined data model, many asset managers deploy advanced natural language processing analytics, an application of artificial intelligence (AI), to quantify the intent surrounding ESG disclosures by companies.
NLP is a branch of artificial intelligence that helps machines understand and interpret human language. Broad CEO statements, like an intent to reduce carbon emissions by 30%, don’t say anything, but AI and machine learning (ML) techniques can quantify and parse specificities behind ESG statements, making them more meaningful.
Because companies are not legally bound to make ESG disclosures, alternative data sources are central to creating a comprehensive picture of reported sustainability metrics. For example, European asset managers must comply with ESG reporting requirements of the EU Sustainable Finance Disclosure Regulation, such as the particular metrics that need to be disclosed for ESG-labeled investment products. Without alternative data, these metrics are nearly impossible to identify and track down.
Analyzing unstructured data is difficult, time-consuming, and laborious, with its scalability limited by manual processes. Thus, sophisticated AI and ML tools are needed to curate and analyze alternative data sets. Detecting market truth signals from large volumes of alternative and unstructured data in real-time for a 360-degree view—at scale—necessitates AI/ML automation.
Tracking alternative ESG data beyond what companies disclose is a cumbersome and challenging but, in several cases, productive task. For small and mid-cap companies, ESG disclosures are not mandatory and rarely reported in company publications. Moreover, because the company’s reputation may be at risk, voluntary corporate disclosures are reported infrequently, are historical, and don’t cover all controversies and other adverse ESG events.
A platform-led approach using AI/ML to transform unstructured data into usable and meaningful insights helps process massive amounts of unstructured data at scale. It leads to intelligent automation. The Straive Data Platform (SDP)—our proprietary end-to-end cloud-centric and modular Data Lifecycle Management Platform—uses data sources like annual reports, CSR documents, 10Ks, news, corporate filings, and more to identify the ESG data points for extraction.
The ESG sector realizes the need to factor in a new generation of data for the early detection of stronger signals to gain a comprehensive and independent view of ESG performance. Moving beyond traditional data sets and including advanced and automated alternative data-based analytics at scale is fundamental for effective risk management.
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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