Integrating value-driven feedback and recommendation mechanisms into business intelligence systems
Business intelligence (BI) systems and tools are broadly adopted in organizations today, supporting
activities such as data analysis, managerial decision making, and business-performance measurement.
Our research investigates the integration of feedback and recommendation mechanisms (FRM) into BI
solutions. We define FRM as textual, visual, and/or graphical cues that are embedded into front-end
BI tools and guide the end-user to consider using certain data subsets and analysis forms. Our
working hypothesis is that the integration of FRM will improve the usability of BI tools and increase
the benefits that end-users and organizations can gain from data resources. Our first research stage
focuses on FRM based on assessment of previous usage and the associated value gain. We describe
the development of such FRM, and the design of an experiment that will test the usability and the
benefits of their integration. Our experiment incorporates value-driven usage metadata - a novel
methodology for tracking and communicating the usage of data, linked to a quantitative assessment of
the value gained. We describe a high-level architecture for supporting the collection, storage, and
presentation of this new metadata form, and a quantitative method for assessing it.
Kolodner, Yoav and Even, Adir, "Integrating value-driven feedback and recommendation mechanisms into business intelligence systems" (2009). ECIS 2009 Proceedings. 328.