Enhancing User Interaction with Business Intelligence Dashboards

The goal of this thesis is to improve visualization dashboards in order to help decision making and Business Intelligence (BI) analysis, and make them accessible to a larger user audience. We identify current trends and use practices of visualization dashboards in the BI domain, and following a user-cantered design approach we provide enhancements and solutions. More specifically we address the following challenges: making dashboard construction and use accessible to visualization novices, enhancing dashboards with advanced annotation capabilities to help BI analysts in the analysis process, and add storytelling functionality to dashboards to help the communication between analysts and decision makers. Our user-centered approach consists of gathering user requirements (through background search, in-depth interviews or prototyping sessions with BI experts), iterative prototype development, and evaluation of our prototypes with representative users. First, our work reports gathered insights regarding novice user practices on Exploration Views (EV), a system that allows novice visualization users to easily build and customize BI information dashboards, but also provides functionality needed by experts. We evaluated EV with both expert and novice visualization users, and found differences in how experts and novice users interact with the interface. Based on those observations we provide new guidelines that augment previous work on designing for visualization novices, in the context of interactive visualization systems in the form of dashboards. BI analysts using dashboards (experts or novices) need support in record-keeping of their analysis, their data discoveries and gathered insights. This support is lacking in most dashboard visualization systems. We conducted in-depth interviews with BI experts that led to new annotation needs for multi-chart visualization systems (such as dashboards), on which we based the design of a dashboard prototype that supports data and context aware annotations, shared across visualizations and datasets. We focused particularly on novel annotation aspects, such as multi-target annotations, annotation transparency across charts, and annotations that can be shared among different data-sources and BI applications. We identified and provided solutions to challenges in using context aware annotations, notably issues arising when the annotation’s ”context” changes (e.g. annotated data are deleted or changed). Our prototype was evaluated with BI experts that were able to easily perform several tasks that they deemed important in their work. To increase the value of an analysis, recorded insights and knowledge need to be organized and communicated to others, and made available for future analysts to learn from. This communication step is very important, as often the analyst of data and the decision maker are two different people. To achieve this we need an easy insight organization and sharing mechanism to facilitate the transfer of knowledge, experiences, and stories, to decision makers and other analysts. We interviewed BI analysis experts and collected new requirements related to BI visual storytelling. Based on these requirements we designed and implemented a storytelling prototype that is integrated in a dashboard analysis tool, and allows easy transition from analysis to story creation and sharing. Our system was identified by experts as having great potential for training other analysts. Moreover, it can help BI analysis authors organize and communicate their findings faster to decision makers, and they can reach a broader audience since the results of a data analysis presented in the storytelling tool can be read with little to no training.