As the European Union’s (EU) environmental, social and governance (ESG) reporting requirements become increasingly stringent and frequent, companies are grappling with how to keep up. ESG and sustainability reporting software developer Greenomy has delved into how data lakes can help companies improve their ESG data management, leading to improved compliance processes.
A major takeaway from the first year of EU Taxonomy reporting is the vital role efficient collection and standardisation of ESG data plays, especially as organisations prepare for the Corporate Sustainability Reporting Directive (CSRD). The task is undeniably complex, with companies facing challenges in collating ESG data from multiple IT systems. In the quest for improved data quality, the key may lie in Data Lake architecture.
Data management platforms have become indispensable tools for companies overwhelmed by copious amounts of data and the associated quality issues, it said. The surge in Cloud hosting services and the ever-growing need for data storage sparked the necessity for centralised, synthesized and clean data. Among these platforms, the terms “Data Warehouse”, “Data Lake”, and “Data Lakehouse” feature prominently.
A data warehouse provides a unified repository for data from various sources, supporting structured data which uses SQL programming language. The data is easily accessible for ad-hoc analysis by business users. On the other hand, a data lake permits the storage and manipulation of large amounts of structured, semi-structured and unstructured data in its raw format. This flexibility benefits data scientists and engineers, who can gain new insights from raw data.
The most recent development, the data lakehouse, marries the structure and data management features of a data warehouse with the low-cost storage, scalability and flexibility of a data lake. An excellent platform for advanced analytics, machine learning and data science tools, a data lakehouse could be the solution for an ESG data management system, Greenomy explained.
Data lakes offer a feasible solution to the daunting EU Sustainable Finance reporting requirements — the CSRD and EU Taxonomy. Accommodating a diverse range of ESG data, they allow for quality control, data integrity verification and handling of potential inconsistencies. Regardless of whether the data comes from structured sources, unstructured energy consumption documents, or even Internet of Things (IoT) sensors, a data lake can collate, cleanse, and standardise it into a unified model.
This process lends itself particularly well to the production of audit-ready reports. Mapping data against the ESG data model of the reporting platform ensures traceability, transparency and auditability. Such an audit trail is key to complying with the limited assurance requirement from the CSRD.
To illustrate its points, Greenomy offered an example involving HR data. The ESRS S1 or “Social” pillar of the CSRD necessitates that HR data from various sources within an organisation be reported. The data lake architecture transforms this information into a standardised global list of employees per gender, function and salary range. This prepares it for the appropriate calculation methodology outlined in ESRS S1.
The real power of data lakes goes beyond compliance. By structuring ESG data, companies can connect their Business Intelligence (BI) tools to gain actionable insights. Integrated artificial intelligence (AI) tools offer predictive ESG analytics and advanced use cases. Companies can use the data model to formulate both short- and long-term sustainability goals, align initiatives and make informed decisions.
Greenomy offers a pioneering CSRD reporting platform and ESG Data Model that can help collect the required data and define an ESG data infrastructure. This allows organisations to meet regulatory requirements while also driving transformative decision-making and advancing their sustainability goals. The benefits of streamlined CSRD reporting include improved stakeholder engagement, investor confidence, and competitive advantage.
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