**6. Conclusion**

Research organizations worldwide are using data on research input and output, that is, publications, patents, research data nowadays for a wide variety of use purposes, such as evaluation, reporting and visualization of a researcher' or research organization's expertise. This places high demands on the quality of the data gathered for these purposes, which have—in most cases—largely outgrown the initial intentions when the data systems were constructed. Moreover, the research world has evolved in a global, dynamic manner in which research data are increasingly being used in order to monitor the efficiency of research processes, the research productivity and even strategic decision making. In order to safeguard correct data analysis, research-related data must be assessed on all relevant quality

**15**

*Data Quality Management*

zation's future prospects.

**Acknowledgements**

**A.Abbreviations**

**Author details**

Sadia Vancauwenbergh

DQ data quality

*DOI: http://dx.doi.org/10.5772/intechopen.86819*

Economy, Science and Innovation, Flanders.

BPMN Business Process Model Notation

CRIS current research information systems FRIS Flanders Research Information Space

ECOOM-Hasselt and Hasselt University, Hasselt, Belgium

provided the original work is properly cited.

\*Address all correspondence to: sadia.vancauwenbergh@uhasselt.be

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

Information

DQM data quality management

dimensions, and inaccuracies must be addressed using data quality improvement trajectories as discussed in this chapter. The integration of a data quality management policy, is the only way to ensure the fitness for use of research-related data for various applications and business processes across the research world as the impact of inaccurate date can have tremendous effects on a researcher's or research organi-

This work is carried out by the Expertise Centre for Research and Development

Monitoring (ECOOM) in Flanders, which is supported by the Department of

CASRAI Consortia Advancing Standards in Research Administration

CERIF Common European Research Information Format

*Data Quality Management DOI: http://dx.doi.org/10.5772/intechopen.86819*

dimensions, and inaccuracies must be addressed using data quality improvement trajectories as discussed in this chapter. The integration of a data quality management policy, is the only way to ensure the fitness for use of research-related data for various applications and business processes across the research world as the impact of inaccurate date can have tremendous effects on a researcher's or research organization's future prospects.
