Data Driven ESG: How Technology is driving change in the ESG Achievements in Business.
ESG & Data are 2 different phenomena making buzz in the business world right now, We have all heard phrases like “Data is the new oil.” & “ESG is putting your money where your values are.” But what happens when you blend both together?, Welcome to the world of data driven ESG, with Big Data leading the digital transformation of Environmental & Social Governence policies of both major and small scale businesses.
Spends on both individually have reached astronomic heights with the total spend on Data driven technologies reaching $2.8 Billion in FY 22 (Source: Statista), While $1.8 Billion was spent on ESG (Source: Prime Database), this is no coincidence as companies are looking to leverage upon the biggest shifts in the industry.
Data Driven ESG provides an instantaneous impact with its ability to be tweaked based on reports & inputs. Nearly two-thirds of Fortune 500 firms are working towards challenging carbon reduction targets for 2050, according to a McKinsey analysis. These objectives and targets serve as more than just a new measure to monitor. They have evolved into distinct strategic objectives for long-term effectiveness and influence.
Due to investors’ increased concern about climate change and social justice, ESG metrics have also increased. According to a Capital Group ESG Global Study, 46% of North American investors view the lack of comprehensive ESG data as a barrier. And 70% of respondents agreed that standardisation of tools and data is required for ESG initiative analysis and implementation. Understanding the impact and creating data-driven initiatives require clear, succinct, and unified data. Technology and AI are the answer.
For instance, smart waste metering technology in the form of skip sensors and AI software can precisely monitor the amount of waste and recycling produced across every corporate site when measuring waste generation. The opportunity to divert more garbage from landfills and the overall effect of lowering GHG emissions will be supported by these measurements, which offer actionable insight into the quantity and kind of waste produced. IDC projects that 45% of G2000 companies will operationalize integrated sustainability in the supply chain during the next three years and successfully report impact data, enabling a 10% reduction in waste and enhancing competitive advantage. Companies have a lot to lose by not implementing sustainable-based plans and the technologies that support them given the obvious advantages of sustainable operations.
Asset managers may examine the sustainability of their assets and strengthen their company with a comprehensive, data-driven picture of their environmental, social, and corporate governance plans by utilising Apache SparkTM, Delta Lake, and MLflow. In particular, we will take the major ESG efforts as stated in the annual PDF reports and contrast them with the actual media coverage seen in news analytics data.
One might say Data Driven ESG means that while ESG revolves around a tight knit environmental socio governance policy which is Aided by Data to monitor and tweak the policies to ensure optimum function and that is certainly true, However it isn’t just about data & digital transformation driving ESG it is also the other way round. ESG is a huge source of data and digital transformation too.
ESG reports offer a lot of information about a company’s performance in these areas. Simple prompts cannot be utilized to train and create algorithms the same way that this data can.
Here are a few instances:
Environmental: ESG reports provide information on a company’s water consumption, waste production, greenhouse gas emissions, and other environmental consequences. Using this data, computers may be trained to forecast a company’s future environmental performance or to pinpoint businesses that are vulnerable to environmental issues.
scandals.
Social: ESG reports provide information on a company’s customer satisfaction levels, staff diversity, and other social consequences. Using this information, computers may be trained to forecast a company’s future social performance or to recognise businesses that may experience social unrest.
Governance: ESG reports provide information on a company’s CEO remuneration, board structure, and other governance practises. Using this data, computers may be trained to forecast a company’s future governance performance or to spot businesses at danger of governance issues.
Simple prompts are unable to convey the nuanced nature of the data in ESG reports. For instance, a straightforward directive such as “predict a company’s future environmental performance” would not be able to instruct an algorithm on how to take into account all of the various variables that can affect environmental performance, such as a company’s industry, location, and regulatory environment.
However, an algorithm may learn to recognise the patterns and correlations in the data that can be used to generate predictions if it is trained on a sizable sample of ESG reports. For instance, an algorithm may discover that particular industries have higher environmental incident rates or that businesses with diverse boards of directors have better governance procedures.
At the moment the race of research data providers to improve the data quality, availability and understanding to use and filter unstructured data such as news articles, earnings calls and company reports for additional information is starting to heat up, it is safe to say that the convergence of ESG and data is a transformative trend that is having a major impact on the business world. Companies that embrace data-driven ESG are well-positioned to succeed in the future.
