Implementing a data quality tool, as we discussed in our last review, provides an important solution: ensuring that your data is correct. It also, unfortunately, reveals another, often hidden challenge. Now that you trust what’s in your tables, the question becomes: what’s it costing you to access them?
Your data can pass every quality check and still leave you with a warehouse bill that’s out of control. This new reliability comes with an operational expense. While your tool ensures the data is correct, it does nothing to ensure you’re using it efficiently. The very pipelines that populate your validated datasets, and the analysts who query them, can be burning money with every execution.
Today, we’re moving from data quality to cost quality. We’re going to hunt down the most expensive line item in your data stack: inefficient SQL. This isn’t an abstract concern. I’ve seen teams celebrate a successful data model rollout, only to get a warehouse bill 300% over budget the following month. The culprit is rarely one catastrophic failure. It’s death by a thousand cuts, or, in this case, a thousand queries.
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