Ever had your CEO look at a report and say the numbers look way off? Has a customer ever called out incorrect data in one of your product dashboards? If this sounds familiar, data reliability should be the cornerstone of your data engineering strategy. In this talk, I’ll introduce the concept of ‘data downtime’ — periods of time when data is partial, erroneous, missing, or otherwise inaccurate – and how to eliminate it in your data lake, as well as the rest of your data ecosystem. Data downtime is highly costly for organizations, yet is often addressed ad hoc. We’ll discuss why data downtime matters to building a better data lake and tactics best-in-class organizations use to address it – including org structure, culture, and technology.
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