ESG data analytics helps companies measure, manage and monitor their reputation, sustainability profile, performance and risk compared to the competition. It is progressively becoming a competitive advantage as companies increasingly place greater importance on their ESG strategy and the business world becomes unquestionably attuned to ESG investing.
Here we take a closer look at the uses of ESG data analytics and the importance of this new buzzword to corporates and investors alike.
Project management and transformation, as well as business as usual where efficiency and effectiveness improvements are critical, are all more likely to fail in the absence of sound governance, causing uncertainty for investors and other stakeholders.
A growing number of companies are beginning to see the importance of adopting ESG data analytics and Artificial Intelligence (AI) into their decision-making processes because they cannot ignore the implications this technology has for corporate governance and the commercial world.
In today’s business landscape, corporate governance – and therefore ESG data analytics – is of paramount importance. Investor protection is no longer the exclusive focus of good governance. Good corporate governance has become critical for firms in an era where all types of stakeholders expect transparency and responsibility.
To meet that need, Permutable offers one of the most comprehensive databases of environmental, social, and governance (ESG) data available, offering complete transparency and allowing for screening of companies using 60 different ESG metrics and ESG scores to objectively measure a company’s ESG performance. This includes key areas such as racism and discrimination, air pollution, mining of minerals and metals and recycling, as well as slavery and other forms of forced labour.
Consumers have stepped up their efforts and consumer advocacy in order to counteract global warming, reduce emissions, and reduce business carbon footprints. Meanwhile, there has never been a greater awareness of the social component of ESG includes issues such as labour standards, human rights, workplace culture, and health and safety practices, including those related to child labour, as there is today.
Within the ESG framework, ESG data analytics provide extensive detail on environmental factors such as emissions and waste management, and the effective use of energy as well as key social issues. Having access to high quality ESG data analytics is vital for companies to be able to manage their reputational risk across these ESG markers.
Armed with the knowledge provided by ESG data analytics, businesses can raise their “social score” as long as they have a strong relationship with their local community and have earned a “social license.”
The adoption of mandated ESG due diligence standards for firms shows the growing global attention on corporate ESG responsibility. The EU, its Member States, and other countries seek to guarantee that corporations take efforts to prevent and resolve negative consequences on human rights, the environment, and good governance in their supply chains and business connections.
This has led to mandated ESG due diligence requirements and punitive measures/enforcement actions for corporations that fail to identify and handle ESG risks in their supply chains. The legal and regulatory frameworks that govern corporate activities and supply chains are increasingly considering ESG issues, including human rights.
The adoption of voluntary international reporting and due diligence norms has already occurred in a few corporations. Due diligence in the supply chain will most likely be standardized by imposing penalties and debarments on EU and non-EU enterprises doing business in the EU.
Artificial intelligence (AI) systems are superior to humans when it comes to sorting through massive amounts of data. Information synthesis, risk assessment, and information analysis and discovery are the most common uses of AI due diligence.
Because of their ability to analyze and interpret data more quickly than humans, AI, and machine learning are less prone to make mistakes. AI due diligence – such as that provided by Permutable – is faster, more accurate, and more proactive than traditional due diligence since it anticipates problems before they arise.
Human errors in due diligence and investment risk assessments are being reduced, eliminated, or automated through the use of artificial intelligence (AI).
Due diligence can be automated to a significant extent. However, there are still some areas that require a more hands-on approach for verification. Due diligence can take a long time, and Permutable’s ESG data analytics engine can help speed things up and target resources where they’re needed most.
Analysis of corporate strategy communications’ investor transcripts is essential. Net-zero carbon emissions are one of the main difficulties that a corporation has to deal with. What questions are being asked and by whom?
As an example, over 20,128 transcripts were examined as part of our investor transcript study. The most commonly used terms include “sustainable” and “renewable.” Although this is a positive development, we must be cautious about the issue of greenwashing (we will cover this in another article). It’s encouraging to see “CO2” in the top five most often used words.
When it comes to the sheer volume of inquiries we fielded on topics like climate change action and social responsibility, the big banks dominated the field of 938 entities. These two subjects are roughly split 50/50.
This article explains why ESG data analytics is becoming vital to the very thread of every business. ESG data analytics can unlock long term value for both companies and investors alike.
Permutable’s new ESG data engine launch can help you achieve a competitive advantage by giving you a detailed overview of your company’s or stakeholders’ ESG metrics and performance.
See a snapshot of our ESG data analytics reports and sign up for more ESG reports here