Master data quality barriers: an empirical investigation

Abstract

PurposeThe development of IT has enabled organizations to collect and store many times more data than they were able to just decades ago. This means that companies are now faced with managing huge amounts of data, which represents new challenges in ensuring high data quality. The purpose of this paper is to identify barriers to obtaining high master data quality.Design/methodology/approachThis paper defines relevant master data quality barriers and investigates their mutual importance through organizing data quality barriers identified in literature into a framework for analysis of data quality. The importance of the different classes of data quality barriers is investigated by a large questionnaire study, including answers from 787 Danish manufacturing companies.FindingsBased on a literature review, the paper identifies 12 master data quality barriers. The relevance and completeness of this classification is investigated by a large questionnaire study, which also clarifies the mutual importance of the defined barriers and the differences in importance in small, medium, and large companies.Research limitations/implicationsThe defined classification of data quality barriers provides a point of departure for future research by pointing to relevant areas for investigation of data quality problems. The limitations of the study are that it focuses only on manufacturing companies and master data (i.e. not transaction data).Practical implicationsThe classification of data quality barriers can give companies increased awareness of why they experience data quality problems. In addition, the paper suggests giving primary focus to organizational issues rather than perceiving poor data quality as an IT problem.Originality/valueCompared to extant classifications of data quality barriers, the contribution of this paper represents a more detailed and complete picture of what the barriers are in relation to data quality. Furthermore, the presented classification has been investigated by a large questionnaire study, for which reason it is founded on a more solid empirical basis than existing classifications.

Performance Comparison of CRUD Methods using NET Object Relational Mappers: A Case Study

“…On the other side, by introducing an additional layer, performance issues arise. Depending on the ORM, this performance downsides are introduced either by internal way of entity-model design approach or, by using reflection [3].…”

Factors Influencing Master Data Quality: A Systematic Review

“…As the continuation, a substantial work performed by [47] proposing 12 factors influencing master data quality which are 1) responsibilities for specific types of master data, 2) roles concerning data creation, use and maintenance, 3) organizational procedures, 4) management focus concerning data quality, 5) data quality measurements, 6) reward and reprimand about data quality, 7) training and education of data users, 8) written data quality policies and procedures, 9) emphasis on the importance of data quality by managers, 10) IT system for data management, 11) possibilities for input in existing IT system, and 12) usability of IT system. The identified factors were empirically validated using a survey mechanism that involved 787 Danish manufacturing company.…”

“…Concerning the improvement of master data quality, understanding factors influencing the quality of the data is a pre-requisite. Even though literature focusing on factors influencing master data quality is scarce, partial contribution by a few scholars such as [1], [21], [46], [47] providing a good starting point.…”

Data quality assessment and improvement

“…Overall, poor data quality can have severe impacts on the entire enterprise, resulting in increased costs, customer dissatisfaction, and even influencing employee morale Silvola et al, 2011;Redman, 1998). Hence, data quality is not just an IT problem, but is closely related to organisational issues (Haug et al, 2013).…”