In the rapidly evolving world of sustainability, we are seeing a massive influx of information. We often hear about the rising need for risk management, reporting, and stewardship, but none of these are possible without reliable data. After all, you cannot manage what you cannot measure.
As the demand for information grows, so does a critical question for investors, regulators, and corporations, how do we know this is reliable?
That’s where our decision-grade data comes in. What sets it apart is its embedded traceability. It is no longer enough to simply report a number; organisations must now show the digital breadcrumbs that led to it.
What is data traceability?
Data traceability is the ability to track every single data point back to its original source. In the sustainability context, it means moving away from “black box” metrics and toward a transparent system where every insight is grounded in fact. Whether a data point is a public disclosure or a scientific estimation, traceability and transparency ensures that the information is auditable and defensible.
How we embed traceability
At GIST Impact, we have a database comprising 20,000 companies. If a data point has been disclosed in the public domain, we capture it. To do this, we use a proprietary tool called SustainData.
SustainData uses advanced AI to automatically find and read company PDF reports. It doesn’t just “scrape” text; it uses contextual analysis to identify and extract the most relevant KPIs. To ensure accuracy, every data point is assigned a confidence score:
- High Confidence: The data is extracted with high certainty and moves directly to final quality control.
- Medium to Low Confidence: The data is flagged for a detailed review process where our team of dedicated analysts step in to ensure no errors are passed through.
Where public disclosures are incomplete, we use advanced machine learning (ML) models to provide estimations that ensure a complete picture of organisational impact. Before any processing begins, our data is rigorously cleaned and mapped to NACE classifications, which enables both high-level sectoral overviews and detailed, activity-specific insights to maintain analytical consistency. By using an ensemble of decision trees, our models identify patterns across industries to fill missing data with market-leading accuracy. Our models are regularly retrained, addressing data gaps and generating reliable and actionable insights.
Drill-down to source
The ultimate test of traceability is transparency. Don’t just take our word for it, have a look on our Data Portal – pictured below. Each disclosed data point is linked directly to its source report. By simply clicking on a number, you can view the original document it came from.
By combining advanced AI/ML capabilities with rigorous human and geospatial validation, we provide more than just a dataset. We provide a clear, auditable trail that turns raw information into a foundation for action.
Get in touch with our team for a demo to see our traceability feature in action.
