Case study

ESG Data Aggregation Efficiency Improvement Through Automation

ESG data aggregation


Europe-based ESG rating agency and proxy advisor.

Business Situation

  • SGA supported the client with ESG-related data aggregation including business assessment, screening, and controversy research
  • The client requested SGA to deploy a technology team on the engagement to explore opportunities for auto-extraction of ESG data.
  • The client mandated comprehensive implementation of the identified automated solution across all services.

Benefits and Outcomes of Our Engagement

  • SGA was able to deliver efficiency gains for the client within three months of commissioning the project.
  • Auto-extraction of data was implemented with 100% precision for structured data sources, 80% precision for semi-structured data sources, and 60% precision for unstructured and textual data sources.
  • The turnaround time of the data collection was improved by 50%–60% at an aggregated level.
  • The accuracy of the data was enhanced by 5% for quantitative data points.

SGA Approach

  • SGA deployed a team of two developers, an RPA expert, a business analyst, and an ESG research expert to explore data extraction opportunities and propose solutions.
  • The team segregated sources of all the available KPIs/data points and developed three phases for the automation project-

    ✓ Phase I: Structured data

    ✓ Phase II: Semi-structured data

    ✓ Phase III: Unstructured and textual data

  • SGA team short listed open source automation tools to achieve the stated automation objectives.
  • Key words were listed and data dictionaries were developed to enable data mapping and auto-extraction.

  • A Proof of Concept (PoC) for the project was developed to automate data extraction for a list of 100 issuers.

  • A comprehensive solution architecture and a project plan to deploy it across the coverage universe of the client was designed.

  • The project was completed in the three phases stated above and the solution was integrated with the client‘s technology infrastructure.

  • We, further designed, an exception management process for the data that could not be completely automated.

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