Companies spend upwards of $15 million dollars per year firefighting bad data, with data engineering teams spending 30-50 percent of their time tackling broken pipelines, errant models, and stale dashboards.
It’s no secret: data quality isn’t given the diligence it deserves. Fortunately, some of the best data teams are investing in new, smarter approaches to solving it.
In this guide, we’ll discuss these best practices and much more, including:
- How to measure data quality
- How to design an effective data quality strategy
- How to apply end-to-end testing and observability across your data ecosystem
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We will also highlight how teams at Intuit, AutoTrader, and Yotpo build healthy, resilient data products at scale by leveraging the new rules of data quality.