**5. Data quality improvement**

*Scientometrics Recent Advances*

technological factors.

*4.2.3 Governance process*

proper DQ management [17].

*4.2.4 Training*

business rules, etc.

well as cleansing erroneous data.

**improvement of data quality** in terms of analyzing the root cause of the errors as

The application of such DQ managerial processes has already been implemented to some extent in CRIS systems that contain research information. For example, the Flanders Research Information Space, also termed FRIS, is a research information portal sustained by the Department of Economy, Science and Innovation in Flanders, Belgium that collects research information from a wide range of Flemish stakeholders in the research field, that is, research universities, higher education colleges, strategic research centers and research institutions (www.researchportal. be) [17]. Underlying the FRIS architecture, a conceptual metamodel was developed in order to model all concepts, attributes and relationships that are contained within FRIS. This conceptual model is based on the CERIF standard, but customized to the Flemish context. In addition, in line with the use purposes of this CRIS system, business rules were drafted to safeguard the quality of the contained information. These business rules were translated to validation rules that are used for the automated quality control of the research information received. If non-compliances to these rules are detected, the research information is rejected, and the information providers receive a notification thereby allowing for immediate data cleansing. Furthermore, the Flemish government also performs manual quality checks on a regular basis in order to validate the research information contained as validation rules in general are not well suited for detecting unpredicted errors. Such errors generally provide valuable input for root cause analyses that can identify important underlying problems which can be caused by human, process, organizational or

A third group of CSFs encompasses the governance processes associated with DQ management. These processes can be largely summarized as the **commitment of an organization's top management** to set DQ management as a priority and to stimulate a culture change throughout the entire organization in this respect. In the field of information governance, Gartner Research defined information governance as '*the specification of decision rights and an accountability framework to encourage desirable behavior in the valuation, creation, storage, use, archival and deletion of information*' [18]. In practice, information governance basically comes down to allocating budget and resources to the process of DQ management by defining roles and responsibilities, making agreements on related concepts, terms and associated DQ processes, including the monitoring, control and improvement thereof. The FRIS-system as indicated above has included data governance in order to ensure

Although an organization might have all operational, managerial and governance processes perfectly in place, a complete implementation of DQ management also requires the investment in training throughout the organization. A first and foremost important goal is to inform people on the importance of qualitative data to the organization. Secondly, people should receive training via training programs, course series, mentorships on the rules as set out in the operational, managerial and governance processes in order to ensure a systematic implementation of DQ throughout the entire organization. Finally, a continuous follow-up is also needed which allows for swift adjustments in case of unpredicted errors, adjustment of

**10**

In order to safeguard the continuous monitoring of data quality and the adoption of measures to improve data quality, a DQ improvement workflow needs to be established. This workflow essentially comprises a repetitive workflow of five consecutive phases, that is, the definition, measurement, analyze, improvement and control phase as depicted in **Figure 2**. A best practice is to formalize this data quality improvement process, in terms of properly documenting all related processes and activities in each phase, as this allows for the tracking of progress throughout the entire DQ improvement workflow.
