As enterprises drive data into more of their decisions and analytics into more of their operations, they need to ensure that data tells an accurate story. Conflicting or outdated records can confuse stakeholders and lead to harmful actions. Consider the machine learning model that calls legitimate transactions fraudulent, or the sales manager that pitches a customer without knowing they just complained to the support desk. Situations like these underscore the reality that wrong or incomplete data is a liability rather than an asset. To make data an asset, enterprises need data observability.This report defines data observability, including its challenges and benefits.

Then we explore use cases for preparing, operating, and adjusting data environments, as well as managing the business aspects ofanalytics projects and applications.

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