Business Intelligence Software Evaluation Testing the SSAV Model

Having the right information in the right place at the right time is fundamental although not easy for the making of significant business decisions and staying competitive. Competitive Intelligence CI allows the scanning of the environment, the recognition of risks and opportunities in the competitive arena and a better understanding of today & tomorrow’s information requirements with the support of Business Intelligence BI Software. Choosing the right BI software is critical to increase productivity and effectiveness in the organization. At the same time a very elaborating and complex process due to the fact that numerous vendors exist on the market most of which are updated very rapidly besides most of BI software selection criteria already used are vague and not complete. It is also difficult to evaluate BI effectiveness as a tool in conjunction with supporting the CI cycle different phases.The objective of this study is to develop a model and test it on a small sample of BI vendors to support organizations in selecting the BI Software that best fits their business needs as well as differentiating between different vendors in this area while developing a reliable categorization…

Contents

1 INTRODUCTION
1.1 BACKGROUND
1.2 PROBLEM FORMULATION
1.3 THESIS FOCUS
1.4 DISPOSITION
2 METHOD
2.1 RESEARCH APPROACH
2.2 INFORMATION GATHERING TECHNIQUES
2.2.1 Theoretical Study
2.2.2 Empirical Study
2.3 ANALYSIS OF EMPIRICAL FINDINGS
3 THEORETICAL FRAMEWORK
3.1 COMPETITIVE INTELLIGENCE CI
3.1.1 What is Competitive Intelligence CI
3.1.2 The role of CI
3.1.3 Competitive Intelligence infrastructure
3.1.4 CI and Technology
3.2 BUSINESS INTELLIGENCE BI SOFTWARE
3.2.1 Business Intelligence BI software Definitions
3.2.2 BI Software capabilities (technologies)
3.2.3 The role of Business Intelligence software
3.2.4 BI Market Growth
3.3 SOFTWARE EVALUATION
3.3.1 Software evaluation quality attributes (variables)
3.4 BUSINESS INTELLIGENCE BI SOFTWARE EVALUATION
3.4.1 Gartner
3.4.2 Forrester Wave BI
3.4.3 Fuld & Company CI Software evaluation
4 THEORETICAL FINDINGS
4.1 THE BI SOFTWARE TECHNOLOGICAL EVALUATION MODEL: THE SSAV MODEL
4.1.1 The framework and the Planning & directing phase variables
4.1.2 Warehousing and the Data Collection phase variables
4.1.3 Business analytics and the analysis phase variables
4.1.4 Visualization and the dissemination phase variables
4.2 THE SCALE UPON WHICH THE EVALUATION VARIABLES ARE MEASURED
4.3 THE EXTENT THE CRITERIA CAN BE USED AS A USER’S BI SELECTION TOOL
4.3.1 Human & Structural Variables
4.3.2 Users Variables
4.3.3 Vendors Variables
5 EMPIRICAL FINDINGS
5.1 LIKERT’S SCALE FINDINGS & SCORE
5.2 BUSINESS INTELLIGENCE SOFTWARE
5.2.1 Information Builders
5.2.2 QlickView
5.2.3 TIBCO Spotfire
5.2.4 Cognos
5.2.5 MicroStrategy
5.2.6 Panorama
5.2.7 Microsoft
5.2.8 Business Objects
5.2.9 SAS
5.2.10 Digimind
5.2.11 Astragy
6 ANALYSIS OF EMPIRICAL FINDINGS
6.1 THE MOST COMPETITIVE BI SOFTWARE
6.1.1 The top data collection vendors
6.1.2 The top vendors in analysis
6.1.3 The top dissemination vendors
6.1.4 The top vendors in planning & directing
6.1.5 The top vendor in certain BI functions
6.1.6 The most complete (standard) vendors
6.2 PROPOSED CATEGORIZATION FOR THE BI SOFTWARE VENDORS
7 CONCLUSIONS
8 SUGGESTIONS FOR FURTHER STUDIES
9 REFERENCES
10 APPENDICES

Author: Yasmina Amara

Source: Blekinge Institute of Technology

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