Data Quality Management Flashcards

In data quality, what does 'completeness' specifically measure?

Answer: Whether all required data values are present and not null

Completeness measures whether all required data values are present—checking for null, missing, or empty values that should contain data.

What is 'record linkage' (also called entity resolution)?

Answer: The process of identifying records across datasets that refer to the same real-world entity

Record linkage identifies records from different datasets that represent the same real-world entity, enabling deduplication and data integration.

Which data quality rule type checks that a value in one field is consistent with a related value in another field?

Answer: Cross-Field Consistency Rule

Cross-field consistency rules verify that related fields have logically consistent values, such as a ship date not being earlier than an order date.

What is the Total Data Quality Management (TDQM) approach focused on?

Answer: Treating data as a product and applying product quality management principles to improve data along its information supply chain

TDQM treats data as a product and applies product quality principles—defining requirements, measuring quality, and improving processes along the information supply chain.

What is 'data accuracy' specifically measuring in the six dimensions of data quality?

Answer: Whether data correctly represents the real-world values it is intended to model

Data accuracy measures whether data values correctly represent the real-world facts or entities they are intended to capture.

Which approach is most effective for sustaining long-term data quality improvements?

Answer: Embedding data quality rules into data entry and integration processes to prevent defects

Embedding quality checks and rules into processes at the point of entry or transformation prevents defects from occurring, creating sustainable quality improvements.