A Systematic Review of Tools for AI-Augmented Data Quality Management in Data Warehouses

This supplementary material supports two studies:

• “AI for Data Quality Management: Toward AI–Task–Technology Fit” (Nikiforova, A., Lnenicka, M., Kaosaar, K., D., Gill, A., Siddiqui, S., 2027)

• “From Data Quality for AI to AI for Data Quality: A Systematic Review of Tools for AI-Augmented Data Quality Management in Data Warehouses” (Tamm & Nikiforova, 2025)

The repository contains supplementary materials associated with the empirical and methodological components of these studies, including materials supporting the development, operationalization, and evaluation of the AI-DQ-TERF framework and the systematic identification and assessment of AI-enabled data quality management tools.

Materials associated with the AI-DQ-TERF study

The supplementary materials for “AI for Data Quality Management: Toward AI–Task–Technology Fit” document the empirical and methodological basis for the development and evaluation of the AI-DQ-TERF (AI–Data Quality–Technology–Task Fit) framework. They comprise:

1. tool analysis / systematic review supplement — documentation of the systematic identification, screening, selection, and evaluation of AI-enabled data quality management tools used to examine the applicability of AI-DQ-TERF;

2. expert interviews — documentation of the expert interview study used to identify practitioner needs, challenges, requirements, and technology–task fit considerations related to AI-enabled data quality management;

3. AI-DQ-TERF evaluation protocol — the full protocol used to operationalize the framework, including evaluation dimensions, criteria, assessment questions, response values, and coding guidance.

Materials associated with the systematic review study

The repository also contains supplementary material supporting “From Data Quality for AI to AI for Data Quality: A Systematic Review of Tools for AI-Augmented Data Quality Management in Data Warehouses”. These materials document the systematic identification and analysis of AI-enabled data quality management tools and provide methodological details underlying the review.