![• Definition of data quality 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 approach towards defining the concept of data quality.
• Research organizations worldwide are using data on research input
and out-put, 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.](https://image.slidesharecdn.com/dataqualitymanagement-210308052932/85/Data-Quality-Management-5-320.jpg)
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Data Quality Management
- 1. Data Quality Management By Sadia Vancauwenbergh
- 3. • 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 fac-tors 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.
- 4. • 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, pat-ents, … 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.
- 5. • Definition of data quality 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 approach towards defining the concept of data quality. • Research organizations worldwide are using data on research input and out-put, 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.
- 6. • . In order to safeguard correct data analysis, research-related data must be assessed on all relevant quality. 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.