When growth pushed reporting beyond a small-team workflow
A rapidly growing midstream operator manages a dispersed reporting footprint: 278 reporting facilities and 2,883 assets spread across multiple operating contexts.
Greenhouse gas (GHG) reporting for the Environmental Protection Agency (EPA) started with a small Environmental, Health and Safety (EHS) team that could request activity data, compile inputs, validate quality and enter values into emissions management software with tight control.
As the footprint expanded, the client scaled the process by distributing data collection and entry across the organization, including field-level personnel. That shift improved speed and coverage, but it also introduced variation in how people interpreted key inputs across five to six reporting boundaries (for example region, reporting facility versus actual facility, industry segment and source type). In practice, that looked like common mismatches such as units entered inconsistently, sources mapped to the wrong facility or source type or required fields left incomplete when timelines tightened.
At the same time, methane and GHG reporting expectations continued to evolve, and the cost of late-cycle corrections increased. With thousands of asset-level records rolling up into submission totals, small inconsistencies could drive outliers, trigger EPA rejections, and pull teams into resubmittals that required substantial coordination to trace the issue back to the source.
The client took a proactive step: establish a more robust, repeatable way to flag outliers early, standardize data interpretation at scale, and keep results defensible against both EPA checks and industry expectations. The goal was decision-grade data that supports compliance today and clearer reduction priorities tomorrow.
Turning reporting requirements into a repeatable quality gate
Onterris worked alongside the client team to convert reporting requirements into a practical, repeatable workflow. The solution centered on a business intelligence (BI) tool designed to sit upstream of final reporting and run consistent checks before the submission deadline.
Onterris built validation routines that reflect EPA-style checks and common rejection triggers. Instead of discovering issues at submission, the tool surfaces exceptions early and routes them to the right data owner, where fixes are typically straightforward: confirm units and conversions, complete missing activity inputs, remove duplicate records or reclassify a source that was mapped to the wrong facility or source type. Onterris also integrated benchmarking using public GHG datasets so stakeholders can separate normal variability from true outliers and focus reviews where deviations look most material.
As data ownership expanded across the organization, we focused on repeatable checks and clear routing for exceptions. That structure lets teams validate quickly, coordinate corrections and submit with confidence without slowing down operations.
Daniel McDermott, Onterris Principal IT Data Management Analyst
Making day-to-day reporting faster and more reliable
The BI tool gives the reporting team one workflow for pre-submittal validation, facility trending and drilldown to equipment-level drivers. It runs EPA-aligned checks before submission, highlights likely issues early and makes it clear who owns each correction. The tool connects internal results to peer benchmarking and adds a planning view for methane-related requirements, including preliminary methane fee exposure screening, so the client can prioritize measurement and reduction actions sooner.
Reducing rework strengthening decisions
With the BI tool in place, the client validated reportable data more comprehensively even as data volume increased and data entry remained decentralized. The client reduced resubmittals by identifying and correcting issues prior to submittal, which improved cycle control and reduced late-stage troubleshooting.
The benchmarking and equipment-level insight also supported more targeted operational conversations. Using these views, the client identified opportunities aligned with industry direction, including evaluating options to phase out high-emission equipment configurations such as wet seal compressors. The team also identified additional emission sources to focus on, including pneumatics and flares, based on the combination of internal trending and external context.
Overall, the client established a scalable standard for how data moves from collection to submission, with the visibility needed to support both compliance execution and forward planning.
A repeatable model for reporting at scale
This model scales because it turns institutional knowledge into a system: validations that run the same way every time, exception views that make accountability clear across many contributors and benchmarking that keeps interpretation grounded as requirements evolve.