ESG issues have become particularly important for businesses, leading many publicly traded firms to release more information about their related efforts.
Unfortunately, the lack of standardisation in terms of goals, policies and progress monitoring has led to a proliferation of ESG disclosure standards and metrics. While investors are prioritizing ESG, general partners and portfolio companies struggle to identify, collect, and report relevant data.
This article aims at a better overview of operations: from data sourcing to data analytics to data transformation, esg metrics are different, but approaches, methodologies are similar.
There’s currently a mix of standards and best practices, the process starts with collection, which in itself is a massive undertaking. Even relatively small companies could be drawing on wildly diverse data sets in multiple formats, covering everything from water use to carbon footprint of a key supplier or exit interviews from employees who left the company.
For most organizations, datasets about exploitation of resources are spread across 100s of operational systems, data warehouses, data hostings and vendors. Finding, organising, and making certain all of this data is of good quality is in practical terms impossible without data cleansing and preparation. Once the data set is assembled, it has to be analysed by Data Analysts, potentially loaded by Data Engineers and potentially modelled by Data Scientists .
Advanced data analytics and dashboarding allow organizations to know if they are meeting ESG goals and make necessary corrections if they aren’t. Data analytics on this scale calls for the ESG data can be centralized in modern, cloud-based storage infrastructure and data pipelines to ensure reliability and data integrity.
A modern cloud data architecture that can deal with structured, semi-structured and unstructured data coming in both batch and real-time is a foundational component. Analysis of this sort also requires that data scientists collaborate effectively and efficiently across business lines.
NLP can help verification
That being said, ESG metrics are meaningless without verification. AI can add a new dimension to verification by using techniques from NLP natural language processing (programmatically extracting information from text) to graph analytics (learning how different entities influence each other’s ESG) to modeling (e.g. Pytorch). While AI provides an opportunity to programmatically improve ESG, organizations now face the challenge of adopting and managing the broad set of machine learning tools available today to make this promise a reality.
All of this can be accomplished with a Unified Data Analytics Platform that allows organizations to easily and quickly gather data from disparate sources. They store it for accurate analysis in a highly reliable and high performing data lake that accelerates the rate at which teams can turn data into insights using machine learning and AI at scale. Big data and AI can augment the ESG rating data that many companies buy today or drive completely novel insights.
ESG metrics starts by aggregating and processing large collections of diverse data from vendors, IOT, news, geospatial, and emissions data sources. For many organizations, this requires a large infrastructure to build out.
Key To ESG enablers are reported below:
- DATA PLATFORMS Azure Synapse Analytics, for data pipelines and data platforms.
- DATA INGEST Pull data across all your different data sources, data storage and data types, including batch and streaming. Leverage a library of connectors, integrations and APIs for all your needs.
- DATA LAKES Build reliable data lakes at scale. Improve data quality, optimize storage performance and manage stored data, all while maintaining data lake compliance and security.
- DATA PIPELINES Run scalable and reliable data pipelines. Use Scala, Python, R or SQL to run processing jobs quickly on distributed Spark runtimes, without having to worry about the underlying computing.
- DATA CONSUMERS Use your data for building BI dashboards, production models and everything in-between.
When organizations obtain an assessment of the data universe that constitute a view of ESG, they can analyze it, individualize trends, predict issues in advance, make corrections and create the foundation for a virtuous cycle of resilience.
To conclude a unified input of Data Professionals (Data Engineers, Data Analyst and Data Scientists), is the key driver to realizing the world-changing promise of ESG.