USING INEQUALITY AND OVERLAP INDICES TO IMPROVE DECISION-MAKING PROCESSES IN BUSINESS INTELLIGENCE ENVIRONMENTS

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

Abstract

Recent years have witnessed a major transition toward a decision-making culture that is based on data collection and analysis (Davenport, 2006). This shift leads organizations to collect vast amounts of data and make increasing investments in Data Warehousing (DW) and BI (Business Intelligence) tools (March and Hevner, 2007; Watson and Wixom, 2007). The BI/DW infrastructure intensifies the ability to analyze the data and gain insights with high business value.
Online Analytical Processing (OLAP) tools are a common form of BI applications. OLAP tools let end-users navigate effectively in large datasets (Tremblay et al., 2007). The navigation is often supported by an OLAP-cube – a multi-dimensional data model that classifies attributes (variables) into dimensions (key business entities– e.g., customers, products) versus facts (numeric measures–
e.g., quantity, sales amount). To benefit from the use of OLAP tools, the end-user must be familiar with the different variables offered and their business meaning. However, the growing volumes and complexity of data warehouses often turn this need for familiarity into a great challenge. End-users are not always proficient enough in the search space that was created and often do not even know what questions can be asked and answered - what might turn the process of search and navigation in large datasets into complicated and inefficient.
Original languageEnglish GB
Title of host publicationProceedings of the 6th Israel Association for Information Systems (ILAIS) Conference July 2, 2012
Editors Daphne Raban, David Bodoff, Irit Hadar
Pages97-100
StatePublished - 2012

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