Through working with hundreds of data teams, we’ve identified the five root causes of data quality issues: input errors, infrastructure failures, incorrect transformations, invalid assumptions and differing definitions.
We'll break down each cause with real examples and offer practical advice to detect, resolve and prevent issues using Snowflake and your broader data stack.
In addition, we’ll dive into two frameworks for prioritization: knowable vs. unknowable and controllable vs. uncontrollable. You’ll leave with a framework to focus on what matters most: reducing the frequency and severity of data incidents while building greater trust in your data ecosystem.
