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Vendor Strategy: Datactics Gives Context to ESG Data

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One of the chief challenges to successful deployment of ESG data by financial institutions is putting that information into context; it’s almost an unwritten maxim that sustainability data is useless if it can’t be used in conjunction with other data.

Often the difficulty is one of matching ESG data and metrics to the entities with which it is associated. Linking an asset’s carbon score to a company can be difficult when the data has come from a source other than the issuer, for instance.

This data quality problem is one that Datactics specialises in solving. The Belfast, Northern Ireland-headquartered company has begun offering its largely financial services clients a platform to clean and match their ESG data as demand has grown for sustainable investing and sustainability reporting.

Its services are particularly in need within the sector because the nature of ESG data means it needs more quality processing. Often unstructured, gathered from disparate sources and often incomplete, it needs to be cleaned and mapped to enable its incorporation into financial data systems.

That’s where Datactics steps in.

“We have a powerful data quality and matching platform which we provide to our clients to help them with all manner of use cases,” Seaward says. “That’s from a chief data officer’s continuous data quality monitoring and remediation point of view to servicing use cases using our powerful matching capabilities.”

Financial Services

Datactics has been in operation for two decades, deriving more than two-thirds of its business from financial services clients on the buy and sell sides. Datactics services them in ESG use cases that range from regulatory compliance and portfolio management to risk assessment and sanctions screening.

Datactics’ matching capabilities help solve a large proportion of the data challenges facing its clients. But it’s not easy to do. As Seaward explains, there is no “ESG button that can be pressed and off it goes”.

Take, for a simple example, the matching of a person to the role of beneficial owner in a company, a matter that occupies companies when onboarding clients or employees as part of the know your customer compliance processes.

A person whose name has a specific unusual spelling can be more easily matched than someone who has a name that can be spelled many different ways. While this is a situation specific to KYC, the principle is the same with ESG data matching: it’s not always obvious where each piece fits. Datactics has to consider a multitude of a data point’s attributes to get a successful match.

But the task doesn’t end there. Datactics has to ensure that the matches are correct. A system of checks and monitors scans results and then presents red flags when anomalies turn up. These have to be calibrated according to clients’ levels of tolerance – not all red flags will be problematic and to dwell on all of them may be counterproductive.

“Hopefully, the majority of your data passes the data quality measures and checks but there are going to be some which fail,” Seaward says. “You might not want to alert users as to every individual data quality break, but certainly ones that you’ve tagged as business critical.”

That’s where clients’ data stewards will step in, identifying what anomaly needs attention and then making decisions on what to do and begin a remediation process that will involve Datactics.

Standards Convergence

The fractured nature of sustainability information and the challenges faced in normalising have been a focus of critics of the ESG project, who have argued that such data challenges are enabling greenwashing, both intentional and accidental. Behind these shortcomings, argues Seaward,­ is the absence of a unified regulatory reporting framework across financial jurisdictions.

“It’s fundamentally a data problem because there’s a lack of standardisation; the measures associated with ESG data from different providers are all derived differently,” Seaward told ESG Insight. “The challenge that clients have is integrating that data into their internal systems and products and attaching some of those measures and data to their internal products.”

The variety of reporting frameworks to which companies work around the world are slowly converging. The formation of the International Sustainability Standards Board in 2021 has seen a number of standards setters coalesce under its umbrella. The Taskforce for Climate-related Financial Disclosures has set the benchmark for many standards companies, and new regulations being framed by the US and UK regulators will cleave to its rules.

Even with a move towards unification of reporting frameworks, Seaward argues that data quality and matching services will still be in demand.

“Everyone’s job will be easier with standardisation,” he says. “But I don’t think it eradicates the need for companies like Datactics in the ESG space.

“There will still be a need to be diligent about this. The challenges are not all going to just disappear as soon as you get standardisation. The quality challenge is never going to be completely “solved”.

Mushrooming Demand

Moreover, data quality solutions will remain in demand in the future as the volume of data gathered for ESG use cases mushrooms. That means more matching of data to other data and metrics.

Servicing this anticipated expansion in demand for ESG data will form the basis for much of Datactics’ future plans. The company is in talks with exisiting and prospective clients about ESG use cases. It also continues to explore more use cases for its ESG clients for which it can create a standardised approach.

“It’s still early days for us on ESG but there definitely will be a need for data quality and matching related to the ESG space,” Seaward says. “And this will obviously grow with, and be helped by, the standardisation of reporting by regulators.”

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