ESG Data Lifecycle Automation
ESG has quickly grown out of investment philosophies into a broader set of core strategic positioning for businesses. In this shift from meaningful change to measurable value, the biggest challenge was to extract Environmental data assets (Carbon Emissions specifically) from a variety of documents. SageX provided a quicker path to automation and a shorter time to the consolidated data, empowering businesses to align their actions with their core objectives.
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Challenge
A Global Asset Manager needed to build an ESG framework in line with GRI and SASB. However, the required data was not available in any structured database for them to get the data consolidated for all the portfolio companies. Some of the critical challenges the client faced were:
- The data extraction process was run manually with 6 people in the DataOps team with significant delays in achieving the business objective
- 85% of the companies in the portfolio were privately held and information was only available in unstructured documents including self disclosures, audit reports, management discussion reports etc.
- The source data was highly varied with different context, formats, layouts, structures and semantic understanding across each company.
Solution
Using the “SaaS First” experience, SageX configured a multi-cluster environment where the initial human touch points were used to accelerate the domain specific learning. Each of the human touch points in the workflow continued recalibrating the model framework to account for high variation in the source data. Together, the engine transformed the data into “ready for consumption” structured data downstream for the target system
Interested in simplifying your Unstructured Data Lifecycle?
