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Ever had someone in a meeting say "we can't trust what operations is telling us" while gesturing at a report with their soft, manicured hands? Well, yeah. Because, more often than not, your best operators and supervisors have been turned into the world's most expensive and least enthusiastic data entry clerks. The data entry tax Here's what I saw once: Supervisors spent up to an hour every shift filling in numbers across 3 different Excel documents. Each sheet asked for essen

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