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Accuracy

As explained in Effective Data Analysis

Data accuracy is the degree to which data correctly represents the real-world process or phenomenon it claims to describe.

Data accuracy is the degree to which data represents the real-world process or phenomenon it claims to describe. It supports reliable, usable information for decisions and may also matter for legal compliance. section 11.3.2

Check for values that cannot represent reality, such as negative values in a column where they are not allowed, and for duplicate data. Integrating checks into data-pipeline steps can catch problems before they affect downstream users. section 11.3.2

How Effective Data Analysis covers it

How deep it goes
Explains real-world representation, implausible values, duplicates, and proactive checks across data-pipeline steps.
Examples
Uses prohibited negative values and duplicate records as accuracy problems, alongside checks on forecast dates and weather values.
What you can do after
Apply pipeline checks to identify implausible values and duplicate data before they cause downstream problems.
How it connects
Connects data accuracy with data quality and ETL workflows.
Cover of Effective Data Analysis
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This explainer was generated by Manning's knowledge graph from the text of Effective Data Analysis. The section links point to the parts of the book it draws on.