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Data Quality Assessment (DQA)
Definition - What does Data Quality Assessment (DQA) mean?
Data quality assessment (DQA) is the process of scientifically and statistically evaluating data in order to determine whether they meet the quality required for projects or business processes and are of the right type and quantity to be able to actually support their intended use. It can be considered a set of guidelines and techniques that are used to describe data, given an application context, and to apply processes to assess and improve the quality of data.
Techopedia explains Data Quality Assessment (DQA)
Data quality assessment (DQA) exposes issues with technical and business data that allow the organization to properly plan for data cleansing and enrichment strategies. This is usually done to maintain the integrity of systems, quality assurance standards and compliance concerns. Generally, technical quality issues such as inconsistent structure and standard issues, missing data or missing default data, and errors in the data fields are easy to spot and correct, but more complex issues should be approached with more defined processes.
DQA is usually performed to fix subjective issues related to business processes, such as the generation of accurate reports, and to ensure that data-driven and data-dependent processes are working as expected.
DQA processes are aligned with best practices and a set of prerequisites as well as with the five dimensions of data quality:
DQA is usually performed to fix subjective issues related to business processes, such as the generation of accurate reports, and to ensure that data-driven and data-dependent processes are working as expected.
DQA processes are aligned with best practices and a set of prerequisites as well as with the five dimensions of data quality:
- Accuracy and reliability
- Serviceability
- Accessibility
- Methodological soundness
- Assurances of integrity
