Business-intelligence (BI) tools are broadly adopted today, supporting activities such as data analysis, decision making, and performance measurement This study investigates a new approach for designing BI tools – the integration of feedback and recommendation mechanisms (FRM), defined as embedded visual cues that provide the end-user with usage and navigation guidelines The study focuses on FRM that are based on assessment of previous usage, and introduce the concept of value-driven usage metadata - a novel methodology for linking the use of data resources to the value gained A laboratory experiment, which tested the design of FR-enhanced BI with 200 participants, confirmed that FRM integration will improve the usability of BI tools and increase the benefits that can be gained from using data resources Further, the experiment highlighted the potential benefits of collecting value-driven usage metadata and using it for generating usage recommendations.

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.

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.

Business intelligence (BI) systems support activities such as data analysis, managerial decision making, and businessperformance 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.