Title

Integrating value-driven feedback and recommendation mechanisms into business intelligence systems

Abstract

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.

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