Data Quality

Data Quality: What Can Actually Go Wrong You’ve built an elegant data pipeline. Your data model is clean. Queries are fast. Your dashboard shows revenue up 50% this quarter. The Business controller makes decisions based on it. And then, three weeks later, you discover: the revenue calculation was wrong the whole time because someone entered “500,000” as “500,00” in a legacy system, and nobody caught it. That’s a data quality problem. And here’s the scary part: nobody noticed until you dug deeper. Your pipeline ran perfectly. Your warehouse loaded perfectly. Your SQL was correct. But the data itself was broken, silently, invisibly, for weeks. Think of it like a supply chain Imagine you’re running a restaurant and ordering ingredients. Good supply chain: You order 100 tomatoes. They arrive. You inspect them: most look good, but you spot 3 that are rotten. You remove them. You use 97 good tomatoes. Dinner is great. Bad supply chain: You order 100 tomatoes. They arrive in unmarked boxes. You dump them in the fridge without checking. You don’t know if any are rotten until a customer bites into a bad one during dinner. Now you have an angry customer and a problem. Your data pipeline is the supply chain. If you don’t check quality before it reaches your customers (dashboards, reports, decisions), you’ll serve them rotten data without knowing it. ...

Data Quality: What Can Actually Go Wrong You’ve built an elegant data pipeline. Your data model is clean. Queries are fast. Your dashboard shows revenue up 50% this quarter. The Business controller makes decisions based on it. And then, three weeks later, you discover: the revenue calculation was wrong the whole time because someone entered “500,000” as “500,00” in a legacy system, and nobody caught it. That’s a data quality problem. And here’s the scary part: nobody noticed until you dug deeper. Your pipeline ran perfectly. Your warehouse loaded perfectly. Your SQL was correct. But the data itself was broken — silently, invisibly, for weeks. Think of it like a supply chain Imagine you’re running a restaurant and ordering ingredients. Good supply chain: You order 100 tomatoes. They arrive. You inspect them — most look good, but you spot 3 that are rotten. You remove them. You use 97 good tomatoes. Dinner is great. Bad supply chain: You order 100 tomatoes. They arrive in unmarked boxes. You dump them in the fridge without checking. You don’t know if any are rotten until a customer bites into a bad one during dinner. Now you have an angry customer and a problem. Your data pipeline is the supply chain. If you don’t check quality before it reaches your customers (dashboards, reports, decisions), you’ll serve them rotten data without knowing it. ...