Data governance, data literacy and the management of data quality

Abstract

Data governance and data literacy are two important building blocks in the knowledge base of information professionals, involved in supporting dataintensive research, and both address data quality and research data management. Applying data governance to research data management processes and data literacy education helps in delineating decision domains and defining accountability for decision making. Adopting data governance is advantageous, because it is a service based on standardised, repeatable processes and is designed to enable the transparency of data-related processes and cost reduction. It is also useful, because it refers to rules, policies, standards; decision rights; accountabilities and methods of enforcement. Therefore, although it received more attention in corporate settings and some of the skills related to it are already possessed by librarians, knowledge on data governance is foundational for research data services, especially as it appears on all levels of research data services, and is applicable to big data. Keywords Data-intensive research, data librarian, data governance, data literacy, research data services On this background, a review of the literature was done in order to identify and examine significant constituents of the knowledge base that is crucial for information professionals, involved in supporting data-intensive research. The first constituent is data governance (DG), which is extensively dealt with mainly in the corporate (business) sector, and is explored in this paper with the belief that bringing it into the picture will enable better research data services. The second one is data literacy, about which there is a massive body of literature, among others in the form of review articles (MacMillan, 2014; Koltay, 2015a, Koltay 2015b). Data literacy is closely related to research data services that include research data management (RDM). As the concept of RDSs itself and data literacy education are still evolving, their relationship to data governance requires examination that may lead to some kind of synthesis. The management of data quality is also inspected in order to determine to what extent it plays the role of an interface between these two constituents. Accordingly, this writing is built on three core terms. Data governance can be defined as the exercise of decision-making and authority that comprises a system of decision rights and accountabilities that is based on agreed-upon models, which describe who can take what actions, when and under what circumstances, using what methods (DGI, 2015a). While the various definitions of data literacy will be discussed below, we define it here as the ability to process, sort, and filter vast quantities of information, which requires knowing how to search, how to filter and process, to produce and synthesize it (Johnson, 2012). This definitions is in accordance with the idea, expressed by Schneider (2013), that the boundaries between information in information literacy and data literacy are blurring, because these boundaries never...

The current state of data governance in higher education

“…Which type of governance should serve as the foundation of data governance for higher education and how it should it vary based on the institutional view on data and information? As Koltay (2016) suggested, "librarianship as well as library and information science also should pay attention to DG [data governance], albeit it attracted attention mainly in the business sector" (p. 304). It is our attempt to examine data governance in higher education from an information science perspective in hopes of understanding the significance of the interplay of data governance, IT governance, and information governance.…”

“…In order to collect relevant data that are meaningful for analysis, a Data Governance Checklist was developed. It is derived from key literature on data governance with seven major criteria identified that would fit for the context of higher education (Brous, et al, 2016;Khatri & Brown, 2010;Koltay, 2016;Li, et al, 2016;Otto, 2011). A coding schema was developed to examine and collect evidence of the status quo of their data governance practices.…”

“…Weber, Otto, and Österle (2009) explained that data governance includes both business processes and IT to provide organization-wide guidelines to ensure data quality and accountability. Several scholars suggested that when data are viewed as an asset in an organization, they should be monitored and managed (Brous, Janssen & Vilminko-Heikkinen, 2016;Khatri & Brown, 2010;Koltay, 2016;Otto, 2011). Culture and values of an organization are also important and will shape the behavior of individuals and organizations (Putro, Surendro, & Herbert, 2016).…”

“…The data governance body is different from the other roles supporting the process. Other frequently discussed roles in data governance are data stewards, data owners, and data committees (Koltay, 2016;Otto, 2011;Rosenbaum, 2010). Data Stewardship, Ownership, and Roles indicated positions of those who have a vested interest and actively engage in the process of data governance within their institution.…”

“…Data governance is needed to guide and facilitate information technology, data processes, and decision-making to support an organization in reaching its goals. Among library and information professionals, data management or data curation are not new concepts (Koltay, 2016). Library and information science professionals can enrich collaboration among organizations in data and information related activities, such as data governance.…”