A public-power utility took analytics off its billing system and cut a 3-hour report to 40 seconds.
A large municipal utility's billing system couldn't support analytics, and ad-hoc extracts were degrading it in production. We moved reporting onto an elastic cloud analytics layer with one governed source of truth.
Challenge
The utility's customer billing system was never built to be an analytics platform, yet every report was pulled straight from it. The ad-hoc extracts that fed those reports degraded the performance, stability, and uptime of the production billing system itself. Point-to-point integrations had bred data silos, so teams burned time reconciling numbers that disagreed, with no single version of the truth across business units — and earlier data-warehouse attempts had failed to fix it. Aging and financial-summary reports over 100–200M+ records took around three hours to run.
Approach
Rather than push the billing system harder, we took reporting off it. We stood up an elastic cloud analytics layer alongside production, with the lightest possible footprint on the billing platform, and modeled the data through a metadata-driven semantic layer so every team drew from one governed definition of usage, charges, payments, and aging. A federated data-lake approach kept storage cheap and the foundation able to scale as volumes and needs changed — then we handed it to the utility's own team to run.
Technology
An elastic cloud data lake and compute layer offloading the customer billing system, a metadata-driven semantic layer as the single source of truth, and a near-real-time reporting layer serving 400+ reports across usage, charges, payments, aging, and financial summaries.
Outcome
Key analytics and reports ran up to 37× faster — the three-hour aging and financial reports over 200M+ records now return in about 40 seconds — and 400+ reports came online with instant access to current and historic trends. The governed model gave every business unit one trusted view instead of reconciling their own, surfaced theft, fraud, and misuse for investigation, and powered targeted collections to bring down bad debt. The platform did it on a fraction of the storage and infrastructure a traditional data warehouse would have needed — while relieving the production billing system it used to strain.
We publish our case studies anonymized as a matter of policy. The engagement and every figure here are real — we describe the work, not the client’s name.
Explore the analytics
Synthetic data — a hands-on view of the kind of dashboard this engagement delivered.
Bad Debt Analytics
Bad debt and attribution across metered and non-metered services — synthetic data.