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Around the World in 30 Days – Data Management Insights from Around the Globe

Daniel Kennedy

Putting ESG data to work: overcoming data management and data quality challenges

Environmental, Social and Governance (ESG) based investing is growing rapidly. The data landscape to support ESG use cases includes screening indicators such as board composition and energy use, third-party ratings as well as primary data such as waste and emissions. There is a wide range of primary data sources, aggregators and reporting standards. ESG ratings in particular are very dispersed reflecting different methodologies, input data and weights – which means investors need to go to the underlying data for their decision making.


Role of ESG in investment operations

Depending on the investment style, ESG information plays a key role in research, fund product development, external manager selection, asset selection, performance tracking, client reporting, regulatory reporting, as well as voting. In short, ESG data is needed through the entire chain and must be made available to different stakeholders across the investment process.

Increasingly, ESG is becoming an investment factor in its own right. This means ESG indicators and ESG-based selection criteria need to be distilled from a broader set of primary data points, self-declarations in the annual report and third-party assessments. Additionally, ESG information needs to be standardized, to roll up company-based information to portfolio-level information, track ESG criteria against third-party indices or external reporting requirements. However, a lot of corporates do not (yet) report sufficient information causing a need to proxy or estimate missing data points or leaving them outside investment consideration altogether.


Data management challenges

Legislatures are promoting sustainable investment by creating taxonomies that specify which economic activities can be viewed as environmentally sustainable. From a data management perspective, this classification refines and is an additional lens on the traditional industry sector classifications.

Other ingredients are hard numbers such as carbon footprinting (detailing scope 1, 2 and 3 emissions, clarifying whether scope 3 is upstream or downstream and so on), gender diversity, water usage and board composition. More qualitative data elements include sustainability scores, ratings and other third-party assessments that use some condensed statistics. A key requirement is the accurate linking of financial instruments to entities.

As ESG investment criteria become operationalized, ESG data management is rapidly evolving. Whenever new data categories or metrics are introduced, data management practices typically start with improvisation through desk level tools including spreadsheets, local databases and other workarounds. This is gradually streamlined, centralized, operationalized and ultimately embedded into core processes to become BAU. Currently, the investment management industry is somewhere halfway in that process.


ESG data quality issues

Given the diversity in ESG data sources  and the corresponding variety in data structures, as well as different external reporting requirements, ESG data quality issues prevent effective integration into the end-to-end investment operation.

In the table below, we highlight some of the more common data quality and metadata considerations with typical examples of those in financial services and how they surface in the ESG data space.

Table 2: example ESG data management challenges
Table 2: example ESG data management challenges

What is required to fully embed ESG data into investment operations?

To overcome these data quality issues, firms need a process that seamlessly acquires, integrates and verifies ESG information. The data management function should facilitate the discoverability of information and effective integration into business user workflows. In short, data management should service users from the use case down, not from the technology and data sets up.

ESG data management capabilities should facilitate the easy roll-up of information from instrument to portfolio and blend ESG with pricing and reference data sets, so it becomes an integral part of the end-to-end investment management process.

Data derivation capabilities and business rules can spot gaps and highlight outliers, whether it concerns historical patterns or outliers within a peer group, industry or portfolio. Additionally, historical data to run scenarios can help with adequate risk and performance assessment of ESG factors. Having these capabilities in-house is good news for all users across the investment management process.

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