Information and data quality in networked business

Abstract

Information and data quality is a topic of highest importance for enterprises. A survey among 173 companies in the German-speaking parts of Europe, for example, has shown that data quality management ranks among the five most important information technology (IT) topics (out of 32) (Capgemini 2011). Basically, information is defined as “data processed” (Van den Hoven 1999). This notion corresponds with the understanding of information being a product manufactured from data as a raw material (Wang 1998; Wang et al. 1998). The analogy to the world of physical goods is true also for the understanding of information and data quality. Quality is subjective and, consequently, whether the quality of information or data is high or low always depends on the user’s context. The quality of logistical data describing the dimensions and weight of a product, for example, might be of minor relevance to the demand planning department of a consumer goods manufacturer. The same issue is a top priority, though, for the logistics department of the same company, because high-quality information about product dimensions and weight prevents the company from wasting money for redundant transportation capacity (if weight values held in computer systems are too high, for example). Besides being context-dependent, the quality of information and data is also a multidimensional concept, i.e. there is no single characteristic describing quality as an integral whole. There is rather an array of dimensions which are used to describe information and data quality. Typical information and data quality dimensions are accuracy, consistency, timeliness, and completeness. A customer address record, for example, might be complete (i.e. no field contains a null value) but at the same time inaccurate if the information given in the fields for street and zip code refers to a previous address of the customer. High-quality information and data is a critical prerequisite for enterprises to meet a number of strategic requirements. In the telecommunications industry, for example, the change of business models from being mainly fixed-line carriers toward customer-oriented information and entertainment service providers has led to increased awareness regarding the role of information and data quality. Or, as a consulting company puts it: “Data ascends from the basement to the boardroom” (Deloitte 2009). Another strategic requirement is posed by a growing number of regulations companies must comply with. The reformation of the European insurance market (also known as Solvency II), for example, requires insurance companies to formalize information and data quality management as a corporate function (European Commission 2008). Apart from that, the continuing trend toward reduced vertical ranges of B. Otto (*) Institute of Information Management, University of St. Gallen, Muller-Friedberg-Strasse 8, 9000 St. Gallen, Switzerland e-mail: boris.otto@unisg.ch