Data Quality Management: Trade-offs in Data Characteristics to Maintain Data Quality

Abstract: We are living in an age of information in which organizations are crumbling under the pressure of exponentially growing data. Increased data quality ensures better decision making, thereby enabling companies to stay competitive in the market. To improve data quality, it is imperative to identify all the characteristics that describe data. And, building on one characteristic results in compromising another, creating a trade-off. There are many well established and interesting theories regarding data quality and data characteristics. However, we found that there is a lack of research and literature regarding how trade-offs are handled between the different types of data that is stored by an organization. To understand how organisations deal with trade-offs, we chose a framework formulated by Eppler, where various data characteristics trade-offs are discussed. After a pre-study with experts in this field, we narrowed it down to three main data characteristic trade-offs and these were further analysed through interviews. Based on the interviews conducted and the literature review, we could prioritize data types under different data characteristics. This research gives insight to how data characteristics trade-offs should be accomplished in organizations.