**5.4 DQ improvement trajectories**

In the next phase, the focus resides on finding solutions to eliminate the root cause of the problem. These solutions, also termed remedies, are in fact changes to data systems or processes in order to prevent data inaccuracies from happening including the swift detection upon their occurrence. While some solutions might be oriented towards improving the data registration, others might focus on the implementation of validation rules or periodic data profiling. In addition, re-engineering of associated data processes and even training of the data provider and user community on data quality aspects, should be considered. Data cleansing might be applied as well, however this mostly is not a solution to eliminate the root cause itself.

Although solutions might be found using common sense, in most cases more efforts are needed. A frequently used method encompasses the organization of topic-oriented brainstorm sessions in the presence of all stakeholders. This approach has the benefit to tackle the problem from multiple viewpoints and at the same time enables a higher engagement of the stakeholders. Importantly, all relevant solutions to the problem should be listed and effects of the proposed solutions should be investigated carefully. In general, continuous, short-term improvements are to be preferred as these might result in quick wins which can result in additional business benefits (as DQ improvement is mostly not a goal in itself).

In our example many solutions can be found that focus on improving the correct registration of the author name. However, if an author ID would be registered and coupled to an author name, the specific focus on registering the name perfectly in a wide variety of bibliometric sources diminishes. Although this seems an easy solution at first glance, this strategy also includes the re-engineering of business processes, that is, the authentication of research publications by an author using its author ID. In order to investigate the effect of this proposed solution, one could investigate the number of publications that can be attributed to a group of authors that has registered and authenticated their research publications versus a group of authors that have no author ID (i.e., the control group) in an experimental setting. By measuring the DQ of both groups in terms of accuracy and completeness, one can see the effect of the proposed solution.
