UNIVERSITY OF LJUBLJANA FACULTY OF ECONOMICS MASTER'S THESIS THE EXTENT OF AUTONOMOUS DATA ANALYSIS FOR NON-IT STAFF WITH SELF-SERVICE BUSINESS INTELLIGENCE TOOLS Ljubljana, October 10th, 2018 Christian Piechorowski AUTHORSHIP STATEMENT The undersigned Christian Piechorowski, a student at the University of Ljubljana, Faculty of Economics, (hereafter: FELU), author of this written final work of studies with the title The extent of autonomous data analysis for non-it staff with self-service business intelligence tools, prepared under supervision of Prof. dr. 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Ljubljana, ________________________ Author’s signature: _________________________ (Month in words / Day / Year, e. g. June 1st, 2012 TABLE OF CONTENTS INTRODUCTION .................................................................................................................... 1 1. ANALYSIS OF POTENTIAL BENEFITS OF SSBI .................................................... 3 1.1 Basic Concepts of Business Intelligence ................................................................. 3 1.2 Business Intelligence Architecture ............................................................................... 5 1.2.1 Operational Source System ......................................................................................... 6 1.2.2 Extract, Transform, Load ............................................................................................. 7 1.2.3 Online Analytical Processing ...................................................................................... 8 1.2.4 Business Intelligence Applications .............................................................................. 9 1.3 Dimensional Data Modelling ........................................................................................ 9 1.4 Self-Service and Self-Service Business Intelligence .................................................. 17 1.5 Self-Service Business Intelligence: Benefits and Shortcomings ............................. 21 1.5.1 Levels of Self-Service Business Intelligence ............................................................ 24 1.5.2 Data Governance and SSBI ....................................................................................... 26 1.5.3 Data Quality and its Dimensions. .............................................................................. 27 1.5.3.1 Accuracy ............................................................................................................. 28 1.5.3.2 Completeness ...................................................................................................... 28 1.5.3.3 Time-related Dimensions ................................................................................... 28 1.5.3.4 Consistency ......................................................................................................... 29 1.5.3.5 Other Considerations ......................................................................................... 29 1.6 Self-Service Business Intelligence and the importance of correct Ontology .......... 30 2 METHODOLOGY, LIMITATIONS, AND EXECUTION OF THE EXPERIMENT 33 3 RESULTS ........................................................................................................................ 41 4 DISCUSSION .................................................................................................................. 47 CONCLUSION AND OUTLOOK ....................................................................................... 50 REFERENCES ....................................................................................................................... 53 LIST OF TABLES Table 1 Overall results averaged over the Demographic Data…………………………….....43 LIST OF FIGURES Figure 1 DW/BI Architecture ..................................................................................................... 6 Figure 2 Entity Classification of the AdventureWorks database ............................................. 13 Figure 3 Hierarchies in the Sales case ...................................................................................... 15 Figure 4 Database after the transformation .............................................................................. 16 Figure 5 Levels of SSBI ........................................................................................................... 25 Figure 6 Gartner Magic Quadrant ............................................................................................ 35 Figure 7 Sales by Territory (2011-2014) .................................................................................. 36 Figure 8 Average Sales Amount per Customer by Territory (2011-2014) .............................. 37 Figure 9 Sales by Quarters (2011-2014) .................................................................................. 37 Figure 10 Direct Sales and Sales through Resellers (2011-2014) ............................................ 38 Figure 11 Adjustment of the Ontology .................................................................................... 39 Figure 12 Box Plot of the investigated Variables .................................................................... 41 Figure 13 Funnel with the Success Rate of the Experiment .................................................... 42 Figure 14 Correlation Matrix of the Variables ......................................................................... 44 Figure 15 Clustering of the relevance of Numeric Charts in connection with Avg. Perceived Usefulness ................................................................................................................ 45 Figure 16 Clustering of total steps completed in connection with Avg. Ease of Use .............. 46 LIST OF ABBREVIATIONS 3NF 3rd Normal Form A Anticipated Use ATM Automated Teller Machine BI Business Intelligence BIS Business Intelligence System DAS Data Staging Area DAX Data Analysis expression DM Data Mining DM Dimensional Modelling DQM Data Quality Management DW Data Warehouse E Ease of Use ERD Entity Relationship Diagram ERP Enterprise Resource Planning ETL Extract, Transform, Load GDPR General Data Protection Regulation GIGO Garbage In, Garbage Out HOLAP Hybrid OnLine Analytical Processing ID Identifier IM Information Management IT Information Technology J Enjoyment MDDB Multidimensional Database MIS Management Information Systems MOLAP Multidimensional OnLine Analytical Processing MS Microsoft O Perceived Characteristics of the Output OLAP OnLine Analytical Processing OLTP OnLine Transactional Processing, RDBMS Relational Database Management System ROLAP Relational OnLine Analytical Processing SDK Software Development Kit SDLC Software Development Life Cycle SQL Structured Query Language SSBI Self-Service Business Intelligence SSIS SQL Server Integration Services SSMS SQL Server Management Studio SST Self-Service Technology Intelligence TAM Technology Acceptance Model TDQM Total Data Quality Management TIST Trust in Information Systems Technology U Perceived Usefulness INTRODUCTION Not only do companies nowadays compete in the efficient and effective use of human capital and their assets, but also of data. Data has become an integral part of every business that is competing in the market today. There are many ways the data can be used; however, this thesis will focus on the field of Business Intelligence