• Yann Bloch, Head of Product and Pre-Sales Americas at NeoXam


• 15.03.2023 01:45 pm
#data

ESG is an increasingly data-hungry business. As it establishes its status in the mainstream, more and more information is needed to meet requirements across the back to front office. This craving for data has understandably created a market for a host of ESG rating agencies and other specialised data vendors.  

So now that the data requirement is being addressed, investment firms are left with the daunting tasks of determining what data they need, how they plan to analyse it, how they will record and classify it, how they will maintain and update it, and how they will report it. Whilst investing strategies increase their focus on saving our physical landscape, firms need to consider what’s happening in the corresponding data landscape. 

Firstly you have raw data, that has been measured or reported by the issuer. It can also be processed, often as the result of analysts’ work. Some data may also be inherently unstructured, such as commentary, in which case processing involves techniques such as Natural Language Processing (NLP).  

Beyond that there’s the matter of whether that data is quantitative, covering energy consumption, CO2 emissions, or average gender pay gap; or qualitative, reporting on controversies, or describing community outreach programs for example. 

What’s more is that no data vendor covers all bases. There are generalists, that tend to cover a broad spectrum of data domains and issues without providing too much depth in any specific topic; digital natives that focus on alternative ways of collecting and interpreting data, often using NLP and machine learning; environmental specialists that have built their reputation of the “E” of ESG; and last but not least, niche players, who specialise in specific issues, such as biodiversity or climate change.  

All this means that financial institutions frequently find the increasing plethora of information that they receive from varying sources difficult to consume within the context of portfolio management. It arrives in very different formats that are difficult to integrate directly with their financial data, and significant changes might be made to datasets day-to-day without prior alerts or checks. 

The heat is turned up further by regulators, with initiatives such as SFDR requiring data from multiple sources and vendors. This requires robust data management capabilities to collate and align the data and organize it into the correct classifications, and there is still a substantial knowledge gap between the demands of ESG reporting and regulation and the practical capabilities to meet them. 

More than that, it’s all well and good investing in ESG funds, but let’s not forget the bottom line. To maximise your competitive edge in ESG investing, your data analytics need to be up to scratch. ESG data should not live in a silo, separated from the financial data. It is increasingly becoming an integral part of the decision process and overall operations.  

Having data stored all over the place, financial institutions do themselves no favours. The diversity in data, and fragmentation of its organisation leads to firms spending unnecessary time (and money) trawling through data, or in some instances paying twice for it. What they need is robust and efficient internal data infrastructure so they can focus on diversity and sustainability where it matters most – in the assets they’re investing in. 

The fact that investors are using ESG data to minimise risk or maximise performance is shifting sustainable finance from the extra financial to the financial sphere, which means a real transformation in investment practices. So therefore as ESG becomes more and more prominent in investing practices, firms need to make sure they have the data management and governance to match.