Having laid the groundwork for why gross margin matters and how we’ve rebuilt the engine behind it, this post focuses on the human experience — how we’ve made margin reporting more accessible, insightful, and immediate.
Lives, not Just Loads — that’s a mantra we live by at ENSEK, and it resonates with me every day. In my role working on ENSEK’s gross margin reporting (GMR), I’ve seen firsthand how focusing on real outcomes for real people (not just moving numbers around) guides everything we do. This mindset was front and centre when we set out to overhaul our GMR capability. We knew that if we got it right, we’d be improving lives, not just balancing loads on a spreadsheet.
One of the biggest risks GMR helps us manage is revenue leakage — where billing errors, data mismatches or system issues cause income to slip through the cracks. Across energy retail markets globally, revenue leakage can escalate quickly — in some regions reaching hundreds of millions in unbilled or misbilled income
At ENSEK, we had a gross margin model that got the job done in the past, but it was reaching its limits in terms of capacity and speed. It ran on an aging SQL Server that could take over 24 hours to process a full report — not because the logic was wrong, but because the platform just couldn’t handle the volumes we needed it to.
That kind of delay didn’t stop us spotting issues, but it did slow down how quickly we could act on them — and in this space, time is of the essence. We needed a faster, more reliable system to help our customers stay ahead of problems, not just react to them.
To solve this, we rebuilt the entire GMR model on Databricks — a cloud-based data platform designed for high-speed, large-scale processing. This shift reduced our processing time from days to hours. The cause-and-effect was clear: by processing multiple data tasks at once (parallelising our data workflows) and moving off legacy infrastructure, we unlocked near-real-time reporting.
But speed wasn’t enough. We also needed to improve how people interacted with the data. Accountants often default to Microsoft Excel, which adds risk and slows things down. So, we’ve rolled out Sigma — a tool that lets users explore data through interactive dashboards. Now, instead of copying numbers into spreadsheets, users can click through to see the detail behind every figure. That means fewer copy-paste jobs and more time spent actually understanding the numbers.
Our system is distinctive in two key ways: performance and precision.
Performance: At other companies I’ve worked with, a gross margin run could take up to a week. Even ENSEK’s earlier system needed more than a day. Now, we’ve cut that to just a few hours — delivering complete reports by 9:00 AM each day. That’s not something I’ve seen happen often elsewhere in the industry.
Precision: Our model is forensic — meaning it can trace and explain discrepancies down to the smallest detail. It reconciles revenue at both the top level and the account level, ensuring that every penny is accounted for. If there’s a discrepancy, we can trace it back to its source. That level of granularity builds trust and enables faster, more informed decisions.
And importantly, the model is built to adapt to increasing data granularity — whether driven by market changes, smart‑meter rollouts, or industry‑wide settlement reforms such as those seen in the UK with MHHS.
Real-World Results: Faster Reports, Greater Confidence
The impact has been immediate. Faster reporting means our customers can catch issues early — before they snowball into major problems. For example, if a meter reading doesn’t match billing data, we can flag it the next day, not weeks later.
This shift has reduced firefighting and increased confidence. Teams now spend less time chasing errors and more time on strategic work. And because the data is accurate and timely, everyone — from finance to operations — can rely on it.
Next, we’re working on integrating cost data into the model – which will let us calculate true gross margin (revenue minus cost) almost in real time. We’re also looking at forecasting capabilities. With historical revenue and cost data, we’ll be able to project future margins and identify trends early, which will support more informed forecasting and earlier visibility of emerging trends.
Finally, we’re developing ways to automate root-cause analysis — identifying the underlying reason behind a data issue. If there’s a revenue discrepancy, the system will not only flag it but also suggest why it happened — whether it’s a data glitch, a process issue, or something else. That’s how we spot the glitch, fix it, and stop it happening again.
Note: ENSEK’s Financial Assurance capability is delivered through our Ignition platform and is not available as a standalone product. This ensures seamless data integration, governance, and audit-grade controls.