Data Asset Quality Evaluation Framework Based on a Hybrid Multi‐Criteria Decision‐Making Method

Abstract

This study presents a new framework for evaluating data asset quality using a hybrid multi‐criteria decision‐making (MCDM) approach that integrates the decision making trial and evaluation laboratory (DEMATEL), best–worst method (BWM), and fuzzy‐technique for order of preference by similarity to the ideal solution (TOPSIS) techniques. First, the framework considers data as both a product and an asset, leading to the development of quality indicators beyond the traditional dimensions. Subsequently, the interrelationships among indicators are addressed using the DEMATEL method, allowing for the identification of key indicators that significantly influence data asset quality in a given scenario. The BWM method is then employed to determine the weights of these key indicators, enabling a more precise assessment of their importance. After that, the TOPSIS method, incorporating triangular fuzzy numbers, is utilized to rank the data asset quality of different companies. Finally, the effectiveness of the framework is demonstrated by applying it to a group of companies, and the results of the company's evaluation are discussed, along with the corresponding data asset quality improvement initiatives.