Data Quality

Purpose

Data Quality supports the automation of data quality production by automating traditional statistical tests and introducing ML approaches to perform required analysis on dimensions such as accuracy and completeness. It also supports documentation of the work performed and includes a human-in-the-loop validation interface to validate results.

Primary users

Not specified. The provided information does not identify a specific primary user for Data Quality.

Where it fits (process/stage/trigger)

Data Quality fits into data quality production activities, particularly when analysis is required to assess data quality dimensions such as accuracy and completeness. It is intended for cross-industry use and can be applied during quality assessment and validation workflows.

Key capabilities / workflow

Data Quality automates traditional statistical tests, applies ML approaches to support data quality analysis, enables human-in-the-loop validation of results through a UI, and documents the work performed. The workflow focuses on analyzing data quality, validating results, and producing documentation.

Inputs

Not specified. The provided information does not identify the required input data, source systems, file formats, datasets, or user-provided materials for Data Quality.

Outputs / Deliverables

Data Quality produces documentation of the work performed. Other outputs or deliverables are not specified in the provided information.

Value

Data Quality helps automate data quality production by reducing reliance on fully manual statistical testing and adding ML-assisted analysis. Its value comes from supporting analysis, validation, and documentation within a cross-industry data quality workflow.