Corrupted data, dropped columns, stale tables, and a sudden proliferation of NULLs are all common data issues. Data issues are one of the top complaints of data-driven teams today. Data quality incidents can cause customer issues in your product, hamstring your analytics team, and feed your AI models with false information. Root-causing bugs can consume valuable analytics and engineering time, and even worse, it’s easy for issues to silently wreak havoc for months before they’re discovered.
In this whitepaper, discover a framework for taking a proactive approach to data quality as your organization builds its data stack.
