Data lakes provide an alternative method for storing and managing a company’s environmental, social, and corporate governance (ESG) information, allowing for easier management of ESG operations and compliance. High-quality ESG data, including matching and mastering linked entity and securities data, is necessary to support ESG investing objectives, but these features are not standard in data lakes.
Multiple sources of ESG data might lead to inconsistencies in the same piece of data. Information may be modeled at three different levels: instrument, issuer, and fund. The information should be structured such that it may be linked to various identifiers (such as ISINs, LEIs, and hierarchies).
Criteria for evaluating an ESG data product
In order to acquire ESG data regarding security issuers, solutions seeking ESG data must first link instrument codes with issuers. To make this data easily retrievable through these IDs, an ESG data solution should store, standardize, match, consolidate, and combine it into linked sets. Data management and analytics companies who have chosen a data lake architecture for their ESG data management are doing so in order to supplement their data framework with lightweight securities and entity masters. The concept is further improved by automating the data pipelines that supply cloud data warehouses, often via cloud data markets.
An important part of the architecture of data lake-based solutions is the data schema/model unique to ESG materiality maps (as published by the Sustainability Accounting Standards Board) and taxonomies. The data lake may serve as a lake house for such schemas. Another efficiency booster is built-in links to similar data.
ESG Investment Data Lakes
Data inside the lake, such as ESG metadata, may be cross-referenced with data from outside the lake, such as securities and entity data, in a data lake house. In this approach, data on airlines’ environmental, social, and governance (ESG) performance, such as their carbon emissions, may be aggregated and linked to particular securities issued by different airlines.
An ESG solution based on a data lake may also make use of data from non-traditional sources, such as human resources data. An internal data pipeline may provide real-time information on portfolios, traders, and accounts. This is helpful because it ensures that consumers will always have access to an accurate record of the securities and transactions, even if the investment manager or portfolio manager responsible for those assets leaves.
Having the ability to examine the data to see whether fund goals, investment objectives, or investment mandates are being fulfilled – in real time or at certain moments in the past – is another use case that would need a bespoke arrangement inside a data lake house. Even “what-if” analysis benefits from this.
In addition to the organizational and analytic benefits outlined above, data lakes provide potential savings on data storage expenses for environmental, social, and governance (ESG) information. Some data management companies in India on cloud data marketplaces are pricing and supplying specialized data sets in new, granular ways that prevent paying for masses of useless data in bigger data sets, which might be useful for those trying to save costs. Those that adopt a total cost of ownership (TCO) perspective on data lakes want to save money by streamlining the data management process. Once again, this is where efficient data pipeline services come into play.
