Under the Carbon Emissions Spotlight:
How Accurate, Audit-Ready Sustainability Data Unlocks Financing

THE CHALLENGE

An Oil and Gas Company wanted to issue a large bond to finance major infrastructure improvement projects. But the emissions data it provided to its investment bankers was several years old, assembled by hand, and very coarse. The bankers put the financing on hold until fresh, granular, audited sustainability data was provided. The oil and gas company had to start all over, this time with a bottom-up approach to sustainability data preparation. With a sprawling operational footprint, the challenge was to gather and prepare data on hundreds of locations, each with different vendors. The utility services had been added over the years by various staff members and it was difficult to map the accounts and usage to specific sites. As the oil and gas company searched for a solution, they set out the key requirements:

• A Full Audit-Trail: The output files reviewed by the auditors must track back to the source data, with documentation about every step of data handling.
• A Single Solution: The sustainability data must be prepared in a complete system, one that is compliant with international reporting and audit standards.
• Validation and Verification: There must be a method to verify that the data was complete, and also did not contain duplicates.
• Speed: And with financing on the line, there was no time to waste.

THE SOLUTION

The oil and gas company transferred a first set of source data files to GLYNT.AI, and data preparation started immediately. Data from 20 sites were used as a fast-path test set, and a customized field list was established. The field list was tweaked several times to make sure it would meet the required reporting needs. GLYNT.AI then processed hundreds of vendors, and a second review at the customized field list was done. This led to some output changes, which were quickly enabled for all data by GLYNT.AI. At the same time, GLYNT.AI began noticing a significant number of duplicate files entering the processing system. Internal metrics from GLYNT.AI showed that duplicates arose from several sources: Duplicate copies of an invoice were entered into the customer’s ERP system because the invoice was categorized in several ways; the same invoice was posted in two different ERP systems; and the vendor issued multiple copies of the invoice. In addition, the method to query and pull invoices from the ERP system used multiple overlapping queries to ensure that all files were pulled. Using AI tools, including mathematical fingerprinting of every incoming data file, GLYNT.AI eliminated the duplicates. The customer used these findings to tighten their own workflows. The requirement for high-quality, audit-ready data led to a final challenge: How to deliver a defensible record that all of the required data was included in sustainability reporting? The customer knew their locations, but lacked a system that mapped vendors and invoices to those locations. GLYNT.AI solved this problem by surfacing unique IDs by records, and a data model that links records to defined sites. The customer reviewed the results and adjusted it as needed. Going forward, there was a tight, defensible method to track all data sources. The result was that a full year of audit-ready data in just weeks. The data files prepared by GLYNT.AI passed an internal audit and were then opened up to the external auditors. Success! Today, sustainability data is being processed by GLYNT.AI on a quarterly basis, and that will move to a monthly basis after the first two quarters of 2024 are complete.