Quality Issues of CRIS Data

Abstract

In recent years, current research information systems have become an integral part of university IT landscapes, and in this regard, their importance in research has greatly increased. Through the use of CRIS, scientific institutions can provide a current overview of their research activities, collect, process and manage information about their scientific activities, projects and output as well as integrate them into their web presence. Furthermore, CRIS can contribute to rationalize and optimize academic research activities, through highly efficient procedures and added value information. In the context of the scientific Big Data, research and development studies often focus on the “3 Vs”, i.e. volume, velocity and variety. Our paper addresses the 4th and 5th Vs, ie data variability (inconsistency), and data veracity (quality) as a specific problem for research information systems. Here, we distinguish between the quality of the system and the quality of the content, in particular of the data input.

Data trustworthiness plays an important role in transforming the data into meaningful information. Therefore, the success or failure of a CRIS in a scientific institution is significantly related to the quality of the data input available as a basis for the CRIS applications. The most beautiful business intelligence (BI) tools, such as reporting, are worthless when incorrect data is entered.