TY - GEN
T1 - Designing business-intelligence tools with value-driven recommendations
AU - Even, Adir
AU - Kolodner, Yoav
AU - Varshavsky, Roy
PY - 2010/12/1
Y1 - 2010/12/1
N2 - 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.
AB - 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.
KW - Business Intelligence
KW - Data Warehouse
KW - Decision Support Systems
KW - Metadata
KW - Recommender Systems
UR - http://www.scopus.com/inward/record.url?scp=79955123937&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-13335-0_20
DO - 10.1007/978-3-642-13335-0_20
M3 - Conference contribution
AN - SCOPUS:79955123937
SN - 3642133347
SN - 9783642133343
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 286
EP - 301
BT - Global Perspectives on Design Science Research - 5th International Conference, DESRIST 2010, Proceedings
T2 - 5th International Conference on Global Perspectives on Design Science Research, DESRIST 2010
Y2 - 4 June 2010 through 5 June 2010
ER -