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Right now, somewhere on your site, a haul truck operator is logging a load. Tray up, tray down, tonnage recorded. That data point joins thousands of others. And somewhere in an office, your GM is staring at three numbers on a screen trying to figure out if you're going to hit production target this month. Between those two points is the information triangle of operational reporting in mining. Data enters at the base, every load from every truck on every shift. As it moves up

The mining industry figured out how to drive accountability and common business definitions in reporting decades ago. Most other industries are still catching up. The Time Utilization Model is mining's answer to a definitions problem that most businesses still haven't solved.   How mining defined common KPIs across every team How did mining get it so right? By implementing strict definitions for KPIs that mean the same thing across teams and sites, because the stakes are high

The owner of a farm down the road once told me they spent $30,000 on someone building Power BI setup for them. Got everything running. Built reports that looked like they belonged in a Fortune 500 boardroom. Six months later, why-business-dashboards-fail-and-how-to-build-ones-that-worknobody's using it. They're back to Excel and manual reporting. Not even using half the valuable data they're now capturing. When I asked what happened, they gave me the shortest, most honest ans

Your supervisor just spent 11 hours keeping operations running. Equipment broke. The plan had to change on the fly. Problems got solved, decisions got made, things kept moving. Now he's supposed to make the mental shift from "get stuff done" mode to data entry clerk. Like some kind of administrative punishment for keeping things running successfully. Here's what I learned (and should have already known) about actually respecting operational people's time. The problem Supervis

Your entire business runs on a spreadsheet that Nathan built three years ago. Nathan left. Nobody knows how it works. But everyone knows that if it breaks during month end, the entire operation grinds to a halt. Sound familiar? I get it. You have a problem. You have Excel. You know how to use Excel. Why would you listen to the data guy ramble about something called "SQL" or "normalization"? Here's the go: we can skip the lecture about what you "should" be using. But please, j

There's a file name pattern that shows up in every operational business I've worked with. It starts with the best of intentions. Someone creates Production_Report.xlsx. Clean, clear, perfect (maybe a little bit plain and boring). Then reality happens. Week 2: We need to fix a minor formula - Production_Report_v2.xlsx Week 4: Someone found an error - Production_Report_v2_UPDATED.xlsx Week 6: Not sure what changed but this is definitely the last one - Production_Report_v2_UPDAT

Most operational reporting looks reasonable on the surface. Average delivery time looks fine. Average delay is only a few days. Average schedule slippage feels manageable. Yet teams are still firefighting. Customers are still frustrated. Inventory buffers keep creeping up. Everyone knows something isn’t lining up, even though the numbers say things are “mostly OK”. The problem usually isn’t the data. It’s the metric. The problem with averages in the real world An average tell

The Medallion Architecture for lakehouses (Bronze, Silver, Gold) has done wonders for helping organisations understand and communicate a modern data platform. It’s clean, memorable, and easy. But while it’s a helpful starting point, it’s not a silver-bullet data strategy. It’s a conceptual model, not a detailed roadmap. And once you begin building a real data lakehouse, cracks start to appear in the model. Bronze might be “raw,” but do we store data as-is or transform into ta

If you’ve ever felt like the world of data tools is an ever-increasing list of acronyms and buzzwords that mean less and less by the day - Excel, SQL, ETL, BI, warehouses, lakes, lakehouses - you’re not alone. Over the last decade working with organisations, especially in primary industries, I’ve seen one issue come up time and time again: We aren’t struggling because we lack data, we’re struggling because we don’t know how to use it. What tools do we need and why? A critical