Making money with clouds: Revenue optimization through automated policy decisions

Yoav Kolodner, Adir Even

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.
Original languageEnglish GB
Title of host publicationEuropean Conference on Information Systems (ECIS) 2009 Proceedings
StatePublished - 2009


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