ESG regulations and frameworks can overwhelm even the most diligent sustainability leaders. Is it time for a more nuanced, product-oriented approach to stop data being treated like an ‘operational exhaust’?
It is always fascinating to watch history repeat itself.
When the global financial crisis struck in 2008, a litany of regulations were enacted to ward against any future collapse, from Dodd-Frank to Basel III. BlackRock called the subsequent 10 years the ‘decade of financial regulatory reform.’
The regulations touched ‘virtually every financial firm’ and demanded more stringent data reporting and collection. The problem was that the banks didn’t know what to collect at first, so they collected pretty much everything and sent it off to the regulators to sift through and hopefully find what they wanted.
If this sounds familiar to any heads of sustainability or lead ESG analysts, then you’re not alone. Regulation and standardisation of ESG is currently a minefield, from the SFDR (Sustainable Finance Disclosure Regulation) and CSRD (Corporate Sustainability Reporting Directive) on one hand, to bodies such as the CDP and SASB (Sustainability Accounting Standards Board) on the other.
It is little wonder therefore that, in the words of one chief sustainability officer (CSO) in the hotel industry, they were ‘drowning in data requests’ while their team spent ‘endless hours collecting ESG data from across [the] organisation.’
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There is no simple answer, but perhaps treating your data as a product can be a good place to start.
Suki Dhuphar is head of EMEA at Tamr, a provider of products which help organisations make sense of their data landscape. Among his career experience is a stint in financial services, from which the post-2008 panic anecdote derives. Dhuphar notes that it took banks ‘some five to seven years’ to establish a regular cadence of capturing and reporting this additional data – not to mention understanding it.
“We’re sort of at the beginning of that phase when it comes to ESG,” Dhuphar tells Sustainable Future News. “No-one has set out that clear vision of ‘what do we need to collect? What do we need to surface? How do we productise this so we can do this on a repeatable basis?’ That in itself is the first challenge.”
Each industry, each business and each vertical works very differently.
Before anything else, make sure you know what you require. Start small and work up, rather than attempting everything and inevitably falling short. If you are overwhelmed by several frameworks, find a consensus point in two or three and target that. Dhuphar outlines the primary challenge of what he calls ‘data availability.’ A simple example on the ‘S’ side is understanding who is working for the organisation, and calculating the diversity levels. It sounds easy, but there are pitfalls, from the number of employees being ever-changeable, to legal and cultural risks across different jurisdictions.
“Each industry, each business and each vertical works very differently,” explains Dhuphar. “When it comes to operating models, some organisations do work within a particular region and a particular area, and have particular vendors and products, whereas other organisations are much more global.”
This applies to external as well as internal ESG data collection. Getting suppliers to follow your lead – particularly with Scope 3 looming large – can be another headache. GoodLab notes that one potential solution here is a streamlined approach which could ‘see suppliers inputting their ESG data quarterly with companies, then reviewing the ESG data as needed for their own reporting cycles.’
Dhuphar notes that the need to link with external data seamlessly has ‘definitely’ shaped Tamr’s product strategy. “If I’m looking at my suppliers, and I want to go to Dun and Bradstreet to get more information around risk or diversity scores, or if I want to go to another third party [such as] EcoVadis to get more information, it’s easier to tag that information and bring it together, rather than having to do a lot of this manually.”
The goal with productising ESG data, as Dhuphar explains, is to not only have multiple teams be able to look at the same product, but also create something which is multi-faceted. It needs to be able to assess suppliers one day, for instance, and look at diversity within suppliers the next.
The pitfalls that a lot of people go on to do, is they build these kinds of pipelines and analysis and machine learning models for a very specific use case which changes because the board members are asking a completely different question in six months
“If you’re looking at ESG and supply chain, for example, you might be looking at your carbon footprint – and in order to understand your carbon footprint, you need to understand where you buy your products, where you sell your products, where they move,” says Dhuphar. “People will look at that as an individual product, whereas the component parts of it are very different.
“If you have a supplier ‘product’, and then a location-based data ‘product’, that gives you two separate views, and then [you] combine the two together. You can have your ESG team looking at it, your analytics team, and your procurement team.
“The challenge we’ve heard, and the pitfalls that a lot of people go on to do, is they build these kinds of pipelines and analysis and machine learning models for a very specific use case which changes because the board members are asking a completely different question in six months’ time,” adds Dhuphar.
“You need to have that flexibility built in. So build something as a product which has multi-use and that can be scalable; not just scalable in terms of volume [but] in terms of use case and applicability.”
This is where a product such as Supplier Mastering comes in, to not just answer the question of who a company’s suppliers are, but add extra flavour by noting how the company interacts with them. As Tamr itself is a mastering solution, it can cover other roles such as HR, noting the original diversity example. The theory is straightforward. If you enter a new supplier into an ERP (enterprise resource planning) system, for instance, the user’s objective is simply to source from that supplier, rather than thinking of analytics down the line. “Data is being treated as an operational exhaust – so it’s no surprise that subsequent analytics fail,” Tamr notes.
Sort out the internal data, use that to then enrich it with external data to give you that more holistic view.
Dhuphar advocates that organisations need to create a product owner, citing the chief data officer (CDO) as a good example, working in tandem with the CSO. Yet while that relationship can dovetail, many smaller businesses – where regulations still apply – may not be able to afford a CDO. “We need to get better at being able to provide those types of organisations with an out-of-the-box product that they can actually use around this,” says Dhuphar.
“Ultimately, most of this is going to be based around data, internal or external,” he adds. “So sort out the internal data, use that to then enrich it with external data to give you that more holistic view.”
