Towards a Context-Dependent Approach for Evaluating Data Quality Cost

• Published in: International journal of advanced computer science & applications Vol. 10; no. 4
• Main Authors: Belhiah, Meryam, Bounabat, Bouchaïb
• Format: Journal Article
• Language: English
• Published: West Yorkshire Science and Information (SAI) Organization Limited 2019
• Subjects: Complexity; Computer science; Cost benefit analysis; Costs; Data collection; Efficiency; Evaluation; Financial management; Information systems; Knowledge management; Organizations; Quality assessment; Quality improvement
• ISSN: 2158-107X; 2156-5570
• DOI: 10.14569/IJACSA.2019.0100471

Data-related expertise is a central and determining factor in the success of many organizations. Big Tech companies have developed an operational environment that extracts benefit from collected data to increase the efficiency and effectiveness of daily operations and services offered. However, in a complex economic environment, with transparent accounting and financial management, it is not possible to solve data quality issues with “dollars” without justifications and measurable indicators beforehand. The overall goal is not to improve data quality by any means, but to plan cost-effective data quality projects that benefit the organization. This knowledge is particularly relevant for organizations with little or no experience in the field of data quality assessment and improvement. Indeed, it is important that the costs and benefits associated with data quality are explicit and above all, quantifiable for both business managers and IT analysts. Organizations must also evaluate the different scenarios related to the implementation of data quality projects. The optimal scenario must provide the best financial and business value and meet the specifications in terms of time, resources and cost. The approach presented is this paper is an evaluation-oriented approach. For data quality projects, it evaluates the positive impact on the organization's financial and business objectives, which could be linked to the positive value of quality improvement and the implementation complexity, which could be coupled with the costs of quality improvement. This paper tries also to translate empirically the implementation complexity to costs expressed in monetary terms.