In CPMAI, what is the difference between data quality and data readiness?

• A. Data quality refers to accuracy, completeness, and consistency; data readiness refers to data being accessible, cleaned, labeled, and suitable for model training and evaluation
• B. Data quality is about data access speed; data readiness is about data volume
• C. Data readiness is solely about governance and policy alignment
• D. Data quality and data readiness are interchangeable terms

Answer: (B)

The main idea here is the distinction between how good the data is and how ready it is to be used in a modeling workflow. Data quality refers to the intrinsic attributes of the data itself—accuracy (are the values correct?), completeness (are key fields present?), and consistency (do the same values align across sources and time). These quality aspects tell you how trustworthy and usable the data would be in general.

Data readiness, on the other hand, looks at the data’s state for a specific task, like model training and evaluation. It means the data is accessible to the right systems, has been cleaned of obvious errors, is labeled when needed, transformed into usable features, and organized so it can be split into training and validation sets. So readiness is about process and usability, not just the raw quality.

A dataset can have high quality but low readiness if it’s not easily accessible or lacks labels or proper preprocessing, making it hard to use for modeling. Conversely, data can be readily available and labeled but suffer from quality issues, which would undermine modeling results. In CPMAI terms, quality is about the data itself, while readiness is about how prepared and usable the data is for model work.