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Summit Key Note Speakers Highlight Growing use of AI in Data Management

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The application of artificial intelligence (AI) to data management processes has developed apace within the sustainability space as a necessity to help institutions make better use of the growing volumes of ESG information they need.

From helping to pull data from unstructured sources, such as reports and other written documents, to cross-referencing and matching disparate datasets, AI has become an indispensable part of ESG data managers’ toolkit.

At this year’s A-Team Group ESG Data and Tech Summit London, three Keynote addresses touched on this increasingly important aspect of the data management industry. Each of them represented companies with their own products that use AI to aid in the multiple processes required to transform sustainability information into a format that makes it easy to ingest into, and use within, firms’ systems.

Entity Matching

Customers of S&P Global Market Intelligence are struggling with the complexity of these new datasets, explained, Neil Robertson, the company’s managing director of commercial strategy. S&P Global Market Intelligence offers a suite of tools to help in the processing and management of sustainability data. Among them is its Kensho application, which is empowering clients in one of the trickiest parts of ESG data management – matching it with financial entity datasets.

A challenge faced especially by institutions that run multi-asset business models is linking unstructured data on topics such as carbon intensity or diversity to specific companies. The problem is most acute with private companies that aren’t attributed the same legal identification codes as listed public businesses.

Trying to cross reference ESG data to those firms requires huge amounts of processing time that often can’t be carried out manually. Robertson explained that this issue was alleviated by Kensho, which uses AI to link ESG datapoints to 28 million companies and 77 million securities.

Kensho is but a part of S&P Global Market Intelligence’s broader ESG data management as a service offering, which enables clients to outsource the management of key data processes to the company’s cloud-based hub of expertise and data feeds.

Data Quality

As Clarity AI’s name suggests, its business is focused on bringing AI to the data management process. At the ESG Data and Tech Summit London, the company’s chief sustainability officer, Lorenzo Saa, explained that the most important element of AI to ensure the quality and content of the data on which the models work. Get the data inputs right and the outputs would be sound. Saa explained that this was particularly important given that clients were increasingly shunning ratings and scores in favour of raw data.

Achieving this calls for not only good quality data but also full data coverage of any given ESG topic. AI can aid in this, Saa said, explaining that the technology’s ability to retrieve data from disparate unstructured sources was enabling firms to bolster their datasets and fill gaps in reported information.

AI is also capable to filling data gaps through its enormous analytical prowess, which enables the technology to make robust estimates to complete missing datapoints. Far from traditional estimation models, which have been criticised as unreliable and accused of fuelling greenwashing, Saa said AI models are able to work on vast tracts of data and variables to produce outputs that are accurate and trustworthy.

Third Parties

There was broad agreement between Saa, Robertson and the Summit’s third Key Note speaker, SoftServe’s Antonina Skrypnyk, on many topics related to AI deployment, especially on its power to transform a multitude of workflows.

One other key point of concord was the need to keep “humans in the loop” of any AI-enhanced workflow, particularly to ensure the quality of input data and the validation of outputs. Another important matter of agreement was the best practice of ensuring that firms’ entire corporate bodies were invested in any AI-led digital transformation.

AI integration can be very expensive and the process of transformation can be a long one. Consequently, said Skrypnyk – SoftServe’s VP FSI for EMEA solutions and consulting – it is vital that internal and external stakeholders are fully committed to their proposed AI “journeys”.

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