How Do We Solve The ESG Data Problem?

Somil Goyal , Jishnu Gupta and Kumalin Nair discuss how to apply best practices to ESG Data challenges.

Governments and businesses are being evaluated on how they counter ecological degradation from economic activity, a goal called #NetZero. So today, it is crucial for all companies to have a plan for improving their environmental and social impact, coupled with appropriate governance (collectively ESG). This is fast-becoming a legal requirement in most countries, as well as a baseline expectation from investors, lenders, employees and customers.

This expectation creates demand for ESG Data, which is already a billion-dollar industry and growing at 20% pa. It underpins a multi-trillion-dollar market in ESG funds & green bonds, and is now coming into other sectors like commodities, loans, derivatives and supply chain. However the ESG Data industry also faces growth pains, some even say an existential crisis.

ESG Data industry leader Daniel Klier of Arabesque recently called the industry landscape "... full of mistakes, very expensive, outdated ...” Ouch!

As ESG Data become a key element for global commerce, we need to anticipate and tackle challenges to data quality and credibility. Or else, we will not build trust in data, and fail to counter climate change. The stakes could not be higher!

Since 2005, a financial industry association called EDM Council has promoted standards, best practices and training in enterprise data management. Recently they focused their considerable brainpower and experience on ESG Data problems. Their recommendations speak for themselves, but today we highlight our interpretation of three principles to help improve ESG outcomes. We also provide one practical step for each principle that ESG Data users can take to great advantage.

Our top 3 ESG Data improvement tips:

1. Transparency from good process;

2. A modern, extensible framework;

3. Data assurance and automation.

Principle One: Transparency from good process

ESG regulations, environmental technology and our expectations are evolving at pace. Changing too are data frameworks, attributes and measurements. As of today, there may be limitations and a lack of confidence in some data. There sometimes can be bias, and temptation to over-simplify linked to greenwashing risk. This can lead to ESG Data being seen as "full of mistakes". One solution to this tag is transparency.

A practical way to achieve transparency is to set up decision-making on granular ESG data elements, rather than composite "black box" ratings. So rather than saying "let us invest in companies with an agency rating of AA or better", we need to go a step deeper and describe specific attributes (e.g. emissions, water consumption, consumer safety) which drive the decision thesis. This means we need to acquire, maintain and use granular ESG Data - complete with confidence ratings - rather than fall for the false precision of a monolithic rating.

Principle Two: A modern, extensible framework

There is no universally accepted ESG Data model yet, and there might never be one. So the framework you use today will change tomorrow. However, this is not a bad thing. Extensible models are used in many enterprise systems to drive efficiency. The technology and processes needed to manage them are well understood.

So as a user of ESG Data, the practical step you can take is to ask your "ESG data person" (that might be your ratings vendor, a colleague in IT department, a consultant, or even an intern with a fancy spreadsheet) about their plans on extensibility. Your should explain to them how frequently you expect your data needs to change, and in what possible ways. You should ask for a solution with agility & flexibility. This investment will pay itself many times over.

Principle Three: Data assurance and automation

Being audit-ready in ESG Data will help you in many ways. You will be able to tackle queries - now and in future - from regulators, auditors, investors, customers and the boss. As you start to look at granular data & confidence intervals (the transparency principle), a good audit trail will provide explanations and aides-mémoires. You will use ESG Data better, and improve it continuously. Over time, this audit trail will feed automation, continuous improvement and machine learning, giving you an edge in the markets.

In practical terms, this means your ESG Data solution (be it a dedicated software, a reporting tool like Tableau, or even a big spreadsheet) has to allow for regular or ad hoc review of data collected. It also has to allow for making data changes, with space to record who made the change, when and why. And it has to envisage the use of automation.

Taking these practical steps is not hard. But it does need commitment and clarity. And the benefits from these steps are very substantial.

In summary, we agree with observations on current shortcomings in ESG Data solutions. We think that ESG Data users can address these: cost through automation, quality through transparency and quality assurance, and outdated platform through an extensible framework.

To discuss your ESG Data challenges and share your insights, do please get in touch!