Business dashboards are visual products of business intelligence and analytics, that are used to support decision-making. This thesis studies how dashboards can be extended with explanatory analytics. Explanatory analytics are automated diagnostics that generate probable explanations to a problem, often an exceptional value. This is especially important with the advent of big data and maturing dashboard technologies.
This thesis is conducted by design science research, where an artifact is created based on previous theoretical grounding, and then evaluated through business experts. Three separate models for explanations from different fields are compared, namely explanation formalism, informative summarization, and explanation by intervention. First, the models’ theoretical bases are detailed and compared. Then the extension is planned by the use of UML diagrams, and implemented through Python using object-oriented programming and Microsoft Power Bi as the dashboarding platform. This implementation is then evaluated with business experts, through semi-structured qualitative interviews.
As a result, it is found that business dashboards can be extended with explanatory analytics, and that the three models share many functions, while differing in others. The main differences found are the recursion logic, measure of impact, and visualisation of the models. Explanation formalism uses top-down recursion logic, with a measure of impact based on the absolute difference of actual and reference value and has a visualisation in the form of an explanation tree. Informative summarization, in contrast, uses bottom-up logic, with impact measure of both magnitude and ratio, and the result is in the form of a table. Explanation by intervention has no recursion, but calculates everything with a big bang method, measuring the impact by ratio, and the result is in the form of table. With a qualitative evaluation it was found that most business experts prefer the use of absolute difference in the measure and having a visualisation such as explanation tree to speed up the assimilation of information. Ratio as a measure of impact was seen as including insignificant explanations when solving business problems.
|Key words||Business dashboard, Business intelligence, Analytics, Explanatory analytics|
Aaro Askala (firstname.lastname@example.org) has worked on this topic.