*3.1. Data and Data Quality*

Before going to the concept of data quality dimensions, let us review the first-order questions that arise from the history of the data quality domain. What is data, and what is data quality? Liebenau and Backhouse [43] defined data as "linguistic, mathematical or other symbolic representation that is universally accepted to represent people, things, events, and ideas." Data represent objects or processes in the actual world in their most basic form. Thus, while addressing data quality, we may argue that poor data quality results from an inaccurate depiction of the real world [44]. Abedjan et al. [45] addressed the tools used for detecting data errors. The study of data quality assessment began in the 1950s, particularly regarding the quality of products and services. Several researchers published several definitions, though no universally accepted definition of data quality exists. Wang and Strong [46] defined data quality as information usable by data consumers, and Crosby [47] defined it as "conformance to requirements." The General Administration of Quality Supervision, 2008, defined data quality as "the degree to which a set of inherent characteristics fulfil the requirements" [15]. At the same time, Fu and Easton [48] explained that data quality is commonly referred to as a collection of "characteristics" of data, such as precision, exhaustiveness, consistency, and timeliness. Most of these characteristics dictate the various dimensions along which data quality may be represented. A low degree

of data quality can significantly influence the overall effectiveness of the associated data applications [49].
