Data quality is crucial in measuring and analyzing science, technology and innovation adequately, which allows for the proper monitoring of research efficiency, productivity and even strategic decision making. In this chapter, the concept of data quality will be defined in terms of the different dimensions that together determine the quality of data. Next, methods will be discussed to measure these dimensions using objective and subjective methods. Specific attention will be paid to the management of data quality through the discussion of critical success factors in operational, managerial and governance processes including training that affect data quality. The chapter will be concluded with a section on data quality improvement, which examines data quality issues and provides roadmaps in order to improve and follow-up on data quality, in order to obtain data that can be used as a reliable source for quantitative and qualitative measurements of research.
Part of the book: Scientometrics Recent Advances