TY - GEN
T1 - Integrating value-driven feedback and recommendation mechanisms into business intelligence systems
AU - Kolodner, Yoav
AU - Even, Adir
PY - 2009/12/1
Y1 - 2009/12/1
N2 - 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.
AB - 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.
KW - Business intelligence
KW - Data warehouse
KW - Decision support systems
KW - Metadata
UR - http://www.scopus.com/inward/record.url?scp=84870689945&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84870689945
SN - 9788861293915
T3 - 17th European Conference on Information Systems, ECIS 2009
BT - 17th European Conference on Information Systems, ECIS 2009
T2 - 17th European Conference on Information Systems, ECIS 2009
Y2 - 8 June 2009 through 10 June 2009
ER -