Gema Gutierrez‡, Moisés Rodríguez§, Javier Garzás‡, Mario Piattini§Corresponding author: Gema Gutierrez ( gema.gutierrez@urjc.es ) © Gema Gutierrez, Moisés Rodríguez, Javier Garzás, Mario Piattini. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Citation:
Gutierrez G, Rodríguez M, Garzás J, Piattini M (2026) OKR4DQ: A Case of Study for Improving Data Quality with Objectives and Key Results. JUCS - Journal of Universal Computer Science 32(6): 895-920. https://doi.org/10.3897/jucs.166944 |  |
As organizations advance their digital transformation efforts, the strategic importance of data quality becomes critical. Legal and security aspects, along with the economic value of data, further emphasize the need for high-quality, reliable, and trustworthy datasets. In particular, the effectiveness of artificial intelligence techniques heavily depends on the integrity of the underlying data. However, organizations often lack structured methods to assess and improve data quality in alignment with business goals. This paper addresses this gap by introducing OKR4DQ, a methodology that leverages Objectives and Key Results (OKRs) to systematically improve data quality. The approach combines quality assessments based on the ISO/IEC 25012 standard with the definition and implementation of OKRs targeting specific quality characteristics requiring enhancement.
We present the full methodology and its application in a real-world case study, demonstrating measurable improvements in data quality and offering practical insights into the challenges and benefits of aligning data quality initiatives with business performance objectives.