COMBINING ENTERPRISE ARCHITECTURE AND OPERATIONAL DATA TO BETTER SUPPORT DECISION-MAKING Master Thesis by R.K.M. Veneberg Combining Enterprise Architecture & Operational Data to better support Decision-Making Master Thesis by R.K.M. Veneberg Author Name: Roelof Klaas Marcel Veneberg Programme: MSc. Business Information Technology Institute: University of Twente School of Management and Governance Enschede, The Netherlands Student number: s0173126 E-mail: [email protected] Graduation Committee University of Twente: dr. Maria-Eugenia Iacob (First Supervisor) University of Twente: dr. ir. Marten van Sinderen (Second Supervisor) BiZZdesign: dr. Lianne Bodenstaff (Supervisor Company) Date: June 6th, 2014 I ACKNOWLEDGEMENTS Over the past period writing this thesis I have dived into subjects I had not been working with thoroughly before. The subjects enterprise architecture and business intelligence were interesting and gave me the motivation to put the utmost energy in writing this thesis, to give something back to the research field and the people I worked for during the past period. Though sometimes complex and time-consuming, I have tried to explain my thoughts on the subject. Of course I would have been nowhere without the help of my supervisors, dr. Maria-Eugenia Iacob who has been a creative source of inspiration and motivation that helped me creating ideas and writing them down on paper, dr. ir. Marten van Sinderen who made sure my formulations were accurate and tested my ideas thoroughly while motivating and handing useful information to proceed writing the thesis, and dr. Lianne Bodenstaff who was my daily source of strength during the week at the office, giving me ideas and questioning my proposed work to the level I deliver in this document. I am grateful to have worked with these people who also worked very hard on writing a paper to the EDOC 2014 conference in Ulm, Germany, where it was accepted as a full research paper. During times outside the office, writing the thesis paid a toll to my friends and family who have had to cope with me when times were getting rough, when I was running into a deadlock writing my thesis. They gave me the motivation to go on and finish it, throughout my whole life. I would like to specially thank my girlfriend Evelien who understood the importance of finishing things up, sacrificing time with me to let me type the thesis. I am thankful for the fellow interns at BiZZdesign, who were my daily pleasure at the office. I remember the funny and serious discussions we had, the struggles we shared all writing the thesis and going through the same phases, and most of all the weekly Tuesday sandwiches we ate. I am sure we will keep in touch after finishing my thesis. I am grateful to BiZZdesign who provided me a place to do my graduation on an interesting topic. I hope I have been a good colleague and a good working force, delivering a useful work on a topic relevant for the company and its clients. A special thanks to Henry Franken for handing me the option to do an exam in ArchiMate 2.0, which I luckily passed, Bas van Gils for his talks on data, and Boudewijn Meyboom for his support in learning how to script. Most of all, I am thankful to God, for Whom I live, Whom I love. The strength and guidance I was given has been the most critical source for me to be able to write a thesis. Humbly, I try to share the love and message that is my inspiration for life, which I wish everyone to have. For God so loved the world that He gave his one and only Son, that whoever believes in Him shall not perish but have everlasting life. John 3, verse 16. The Bible. I hope you will enjoy reading this thesis. If you have any questions, please do not hesitate to get into contact with me. Yours truly, Roel II ABSTRACT Decision-making may be complex in large organizations dealing with many stakes and situations. Currently, decision-making is often done using business intelligence solutions, combined with data warehousing technologies storing operational data. Enterprise architecture is often used for strategy purposes and provides an overview of complex organizational architectures, showing business entities and relations. Data warehouses (DWs) storing operational data are used as a source for business intelligence to provide decision support (Sun & Heller, 2012), but they lack data traceability regarding enterprise processes and entities: business people need an explanation of the data to trace it back to their organization (Pettey & Van der Meulen, 2012). Enterprise architecture provides this type of traceability using familiar organizational entities and relating them in a structure (Zachman, 1987), but is not suitable as a source for business intelligence applications, since it does not store operational data (Johnson, Ekstedt, Silva, & Plazaola, 2004). Combining these two techniques hands possibilities for improving decision support to directly hand business people the data they need as evidence for decision-making, without having to trace it back to the location the data is showing something about: enterprise architecture may provide the meta data for operational data needed for business people to make decisions for their organization. Currently the field of enterprise architecture and business intelligence lacks methodology in combining both worlds. This thesis provides a structured approach to address this gap. Going through six phases of the Enterprise Architecture Intelligence Lifecycle (EAIL) gives any organization dealing with complexity, having an enterprise architecture and operational data in-house, the chance to develop a better source of data for decision-making. The EAIL provides two ways of combining enterprise architecture and operational data in different approaches, namely by adding operational data to an enterprise architecture resulting in an ‘enriched enterprise architecture’ or by adding enterprise architecture meta data to an operational data source resulting in an ‘enriched operational data source’. Moreover, the thesis provides a model to store enterprise architecture, operational data and time, also known as the Concept Match Library (CML). Using the CML hands possibilities for forecasting and other analysis types, while maintaining the original data sources and adding the ‘best of both worlds’ into a single data source: meta data from enterprise architecture providing a context to operational data from data sources. These new technologies hope to bring more accurate and better structured data provision for decision-makers. The EAIL is demonstrated using the Timber case: a real life case problem based on a case that was performed in the Netherlands in the pensions sector. Using a cost perspective, the EAIL was walked through all of its phases, showing the differences of normal business intelligence solutions with our results: a more concise and more accurate view on data and meta data, which was carefully selected using the EAIL. Our work was rewritten in a paper and sent to the EDOC 2014 conference, where it was accepted as a full research paper. The EAIL and CML are presented in September, 2014 in Ulm, Germany. III TABLE OF CONTENTS Acknowledgements .................................................................................................................................. I Abstract ................................................................................................................................................... II Table of Contents .................................................................................................................................. III List of Figures ...................................................................................................................................... VII List of Tables ......................................................................................................................................... IX List of Abbreviations ............................................................................................................................. XI I. Introduction .................................................................................................................................... 1 1.1 Problem statement ...................................................................................................................... 1 1.2 Objectives ................................................................................................................................... 3 1.3 Research Questions .................................................................................................................... 4 Conclusions Part I ............................................................................................................................... 5 II. Research Methodology ................................................................................................................... 7 2.1 History of Design Science .......................................................................................................... 7 2.2 Design Science in Information Systems Research ..................................................................... 8 2.3 Design Science Research Model ................................................................................................ 8 2.4 The Anatomy of a Design Theory .............................................................................................. 9 2.5 Applying Design Science ........................................................................................................