The growing body of ESG data is posing a few challenges to financial institutions as they try to make sense of the tsunami of sustainability information entering their systems.
Data managers are turning to sophisticated analytical tools including artificial intelligence (AI) to help them clean, digest and utilise the data. How they do that and what technology they deploy to achieve it, however, is a matter of intense debate with board rooms.
A-Team Group’s ESG Insight has put together a panel of speakers from the data-provision and data-consumer sides of the debate to look over how companies are dealing with this fast-moving aspect of ESG. Among the topics they will discuss at the September 29 event are: best practices on capturing and containing relevant data sets; how to ensure data quality; how to master, integrate and track ESG data; and, what technologies, tools and services would be most useful in achieving the best outcome.
“Analytics is used in the entire data flow because there’s a vast amount of data out there,” says Boyke Baboelal, strategic solutions director Americas for Alveo. “What I see now is that a lot of focus is on getting the data but it’s not the entire process that is being discussed.”Seizing Opportunities
One of the key drivers behind the development of ESG data analytics has been AI, which is helping in the ingestion, mastering and utilisation of ESG information. It’s also being deployed to standardise and clean unstructured data, such as that scraped from PDF documents, websites and social media.
Baboelal said that while many companies had eagerly seized on AI, may were not using it to the technology’s full advantage.
It is important for any company using ESG data to plan how they would apply analytics. Too many, he said, were focusing their attention on obtaining data. They ought to spend more energy on establishing a goal for their analytics ambitions and draw up a strategy to achieve that.
The growth of the ESG data market was highlighted earlier this year in a report by management consultant Opimas, which estimated data providers would reap US$1.3 billion, up from $1bn last year. About three-quarters of that would be accounted for by data analytics, the report added.
Getting it Right
Getting ESG data right is critical to financial institutions’ operations. Without the right data – or the correctly processed data – companies would struggle to find value from the information for which they are paying huge fees.
One of the most important tasks in achieving that is to shape ESG data in a way that enables its easy integration with other forms of information, including time-series price data, enterprise data and other financial data, Baboelal said.
“If it’s done well – that linking of information with reference data, time series data and so on – and when it’s integrated with all its aggregates as well, you will have one single view of an entity with all its securities and so on,” he said. “That will make it easy to apply the big queries and do all kinds of analytics.”
Joining Baboelal on the webinar, which is entitled “Approaches to ESG data for analytics”, will be Roshini Johri, head of ESG analytics at HSBC and Nirav Shah, head of ESG analytics at M&G.
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