The exponential growth in interest related to ESG disclosures and investing has forced many reporting companies, consulting firms, investors, hedge funds, and regulators to increasingly rely on technology to keep abreast with the evolving ESG landscape and also respond quickly to its challenges. ESG has become an unavoidable, critical business issue. If you are a large company in the upper echelons of the business world with vast resources to invest in armies of data scientists and technologists, and infrastructure, you are off to a great start.
But what about all of the companies out there that do not have this luxury? How do you scale your ESG domain expertise and knowledge to compete with the big players or comply with ESG reporting mandates?
In particular, given the multiple ESG reporting frameworks and the lack of a global standard, how does a resource-constrained company efficiently conduct ESG research, gather important data, and execute analyses to drive business and social strategies alongside growth… at scale?
Can you afford to employ limited resources to manually search ESG reports, copy and paste data into a database or an excel spreadsheet, one report at a time, and this is even before you begin the important work of analyses.
Human-Centric Machine Learning Framework
Democratizing access to data, and its curation is part of our DNA at idaciti. That is why we built a Machine Learning Operations platform to empower ESG experts at companies to unlock, clone and scale their expertise and domain knowledge with the muscle of computing power. This levels the playing field so that even the smaller ESG consulting or research firms can automate the extraction of ESG disclosures and data from a repository of company reports or from a securities regulator like the Securities and Exchange Commission.
ESG experts without any technical knowledge of machine learning or data science can train the computer using our easy-to-use application to execute searches and extract relevant data. We let the experts tap into their domain knowledge to "seed the model", and our application takes that knowledge and captures the data at scale, presenting the results for your review. This end-to-end process takes as little as 15 minutes for 3,000 companies. Deploy the model to production with a click of a button. We designed it to be as simple as that.
XBRL-First Approach
Building on our XBRL(eXtensible Business Reporting Language)-first approach, we parse, structure, and digitally convert company disclosures so that financial, non-financial, quantitative, and qualitative metrics are extracted in real-time as reports are filed with the regulator or updated in a repository. In addition, since we "XBRL-ize" the report, all metadata related to extracted data, including traceback to the source location, is re-inserted back into the source document, so there is always full traceability to any data point.
Our end-to-end ML Ops platform provides a unique solution for companies who just want to get on with their business and do what they do best - analyze ESG disclosures, related data and impart their expertise to help reporting companies and investors alike. We are just here to eliminate the tedious so that you are focusing on the important stuff.
