Data Quality and Ranking System

**3**

**Chapter 1**

**Abstract**

*Sadia Vancauwenbergh*

data quality improvement

**2. Definition of data quality**

approach towards defining the concept of data quality.

**1. Introduction**

Data Quality Management

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.

**Keywords:** data quality, data quality measurement, data quality management,

Over the past decades, research organizations, administrations and researchers have been collecting data that describe both the input as well as the output side of research. This has resulted in an enormous pile of data on publications, projects, patents, … researchers and their organizations that are collected within database systems or current research information systems (CRIS). Such data systems are created according to specific goals and use purposes of individual organizations, which reflects their specific nature and the surrounding context in which they operate. However, over time these data systems, institutions as well as the research ecosystem at large have evolved, thereby potentially threatening the quality of the collected data and the resulting data analyses, particularly if no formal data quality management policy is being implemented. This chapter introduces the readers into the concept of data quality and provides methods to assess and improve data quality, in order to obtain data that can be used as a reliable source for quantitative and qualitative measurements of research.

In general, data can be considered of high quality if the data is fit to serve a purpose in a given context, for example, in operations, decision making and/or planning [1]. Although this definition of data quality seems to be straightforward, many other definitions exist that differ in terms of the qualitative or quantitative
