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The Modern Asset: Big Data and Information Valuation

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The Modern Asset: Big Data and Information Valuation The Modern Asset: Big Data and Information Valuation by Jacques B. Stander Thesis presented in partial fulfilment of the requirements for the degree of Master of Science in Engineering Management in the Faculty of Engineering at Stellenbosch University Supervisor: Prof. P.J. Vlok Co-supervisor: Dr. J.L. Jooste December 2015 Stellenbosch University https://scholar.sun.ac.za Declaration By submitting this thesis electronically, I declare that the entirety of the work contained therein is my own, original work, that I am the sole author thereof (save to the extent explicitly otherwise stated), that reproduction and pub- lication thereof by Stellenbosch University will not infringe any third party rights and that I have not previously in its entirety or in part submitted it for obtaining any qualification. Date: . Copyright © 2015 Stellenbosch University All rights reserved. i Stellenbosch University https://scholar.sun.ac.za Abstract The Modern Asset: Big Data and Information Valuation J. B. Stander Department of Industrial Engineering, University of Stellenbosch, Private Bag X1, Matieland 7602, South Africa. Thesis: M.Eng (Engineering Management) August 2015 The volatile nature of business requires organizations to fully exploit all of their assets while always trying to gain the competitive edge. One of the key resources for improving efficiency, developing new technology and optimizing processes is data and information; with the arrival of Big Data, this has never been more true. However, even though data and information provide tangible and often indispensable value to organizations, they are not appropriately val- ued or controlled. This lack of valuation and control is directly related to the lack of a reliable and functional valuation method for them. This study takes a qualitative and inductive approach to developing Deci- sion Based Valuation (DBV); a proof-of-concept information valuation method. DBV addresses the need to correctly value the data and information an organi- sation has and may require. Furthermore, DBV is presented with its valuation framework and value optimization and performance assessment tools. These tools address the issue of management and control of information, following in the footsteps of Physical Asset Management (PAM). By using complimentary valuation methods and attributes from PAM in combination with intangible asset valuation methods, DBV is able to capture what is essential to the value of information. Beginning with a background to Big Data and PAM, their value is made clear to reader. Furthermore, the difficulty and need for a valuation method catered towards information is presented. This will set the stage for the intro- duction of data and information principles as well as physical and intangible asset valuation methods. These methods are drawn upon for the development of DBV as well as the valuation framework it is based upon. The valuation ii Stellenbosch University https://scholar.sun.ac.za ABSTRACT iii framework acts as the foundation of DBV and addresses the core principle of information valuation. After detailing DBV in full, proposed value optimiza- tion and performance assessment tools are described. These tools are created to assist with the control and management of information. Concluding this study is the validation of both the method itself and the need for it. Combin- ing depth interviews and case studies, the need and importance of a method such as DBV will become clearer to the reader. Furthermore, the success of DBV as a proof-of-concept is illustrated. The method presented in this study shows that it is possible to create a reliable and generic valuation method for Big Data and information. It sets a foundation for further research and development of the Decision Based Valuation method. Stellenbosch University https://scholar.sun.ac.za Uittreksel Die Moderne Bate: Groot Data en Inligting Waardebepaling (“The Modern Asset: Big Data and Information Valuation”) J. B. Stander Departement Inudstriële Ingenieurswese, Universiteit van Stellenbosch, Privaatsak X1, Matieland 7602, Suid Afrika. Tesis: M.Ing (Ingenieurswese Bestuur) Augustus 2015 Die wisselvallige aard van die omgewing sake vereis besighede om hulle ba- tes ten volle te benut, maar om terselfdertyd ook ‘n mededingende voordeel te bewerkstellig. Een van die belangrikste hulpbronne om doeltreffendheid te verbeter, nuwe tegnologie te ontwikkel en prosesse te optimeer is data en inligting. Met die koms van die konsep van Groot Data is data an inligting belangriker as tevore. Selfs al verskaf data en inligting tasbare en noodsaaklike waarde vir besighede, word die waarde daarvan nie behoorlik bepaal of beheer nie, wat direk verband hou met die gebrek aan ‘n betroubare en funksionele waardbepalingsmetode vir data en inligting. Hierdie studie volg ‘n kwalitatiewe benadering en ontwikkel ‘n model vir "Besluit Gebaseerde Waardasie” (BGW) - ‘n konsep inligting waardasieme- tode. BGW spreek die behoefte vir korrekte data en inligtingwaarde vir besig- hede aan. Die metode verskaf die waardasie raamwerk en waarde optimerings- en evaluering van prestasie metodes. Hierdie metodes spreek die probleem van bestuur en beheer van inligting binne die fisiese batebestuur omgewing aan. BGW is in staat om die waarde van inligting te bepaal deur die gebruik van ’n kombinasie van die waardasiemetodes en eienskappe van fisiese batebestuur asook die waardasiemetodes van ontasbare bates. Om die waarde van beide te verduidelik, word die agtergrond van “Big Data” en fiesiese batebestuur omgewing, waarna die probleme en vraag na ‘n waardasiemetode vir inligting geillustreer word. Dit baan die weg vir die iv Stellenbosch University https://scholar.sun.ac.za UITTREKSEL v bekendstelling van data en inligting beginsels asook die fisiese en ontasbare waardasiemetodes. Hierdie metodes dien as fondasie waarop die BGW waar- dasiemetodes en -raamwerk gebaseer word. Dit dien as die basis vir BGW en spreek die kernbeginsel van die waardasie van inligting aan. Na oorweging van die besonderhede van BGW, word die waarde optimerings en prestasie evalue- ringsmiddele beskryf. Hierdie middele is geskep om die beheer en bestuur van inligting aan te help. Hierdie studie word afgesluit met die bekragtiging van beide die waardasiemetode asook die behoefte daarvoor. Dit word bewakstellig deur die kombinasie van in diepte onderhoude en gevallestudies. Verder word die sukses van BGW as ‘n waardasiemetode uitgebeeld en bewys. Die BGW metode bewys dat ‘n betroubare en generiese metode vir die waardasie van “Big Data” en inligting geskep kan word. Dit dien as grondslag vir verdere navorsing en ontwikkeling van die waarde-gebaseerde besluitneming methode. Stellenbosch University https://scholar.sun.ac.za Acknowledgements I would like to express my sincere gratitude to the following people for their valuable industry insights they provided. Riaan Nagel Manager in Strategy & Operations PwC South Africa Brian Williams Director in Power & Utilities PwC Middle East Dean Griffin Director in Asset Management Gaussian Engineering Grahame Fogel Director in Asset Management Gaussian Engineering Dr. Barry van Bergen Director in Global Strategy Group KPMG London Johan Paulsson Manager Asset Management - Expert Services Tetrapak Sweden Ian Beveridge Asset Management Consultant Gaussian Engineering vi Stellenbosch University https://scholar.sun.ac.za Contents Declaration i Abstract ii Uittreksel iv Acknowledgements vi Contents vii List of Figures ix Nomenclature x 1 Introduction to Research 1 1.1 Introduction . 2 1.2 Big Data . 2 1.3 Physical Asset Management . 6 1.4 Research Problem and Rationale . 8 1.5 Research Objectives . 9 1.6 Research Design . 11 1.7 Chapter Summary . 17 2 Literature Review 18 2.1 Principles of Data and Information . 19 2.2 Physical Asset Valuation . 28 2.3 Intangible Assets Valuation . 33 2.4 Information Valuation . 44 2.5 Amortization of Data . 46 2.6 Cost of Data . 48 2.7 Chapter Summary . 49 3 Valuation Framework 51 3.1 The Top-down Approach . 52 3.2 Identifying Decisions . 53 vii Stellenbosch University https://scholar.sun.ac.za CONTENTS viii 3.3 Selecting Critical Information . 56 3.4 Identifying Processing Techniques . 57 3.5 Determining Data Required . 58 3.6 Determining Collection Methods . 59 3.7 Chapter Summary . 60 4 Decision Based Valuation Method 62 4.1 Classification of Data . 63 4.2 Decision Nodes . 71 4.3 Determining the Properties of Information . 80 4.4 Value Calculations . 90 4.5 Chapter Summary . 97 5 Value Optimization and Performance Assessment 99 5.1 Adjusting Calculated Value . 100 5.2 Extracting Value from Information . 101 5.3 Lean Data Management . 102 5.4 Optimizing Decision Node Value . 104 5.5 Chapter Summary . 107 6 Validation 109 6.1 Industry Interviews . 110 6.2 Case Studies Introduction and Data Collection . 116 6.3 Application of Decision Based Valuation . 118 6.4 Case study results . 130 6.5 Chapter Summary . 131 7 Conclusion and Recommendations 133 7.1 Conclusion . 134 7.2 Recommendations . 136 Bibliography 138 Appendices 151 A Decision Based Valuation Process 152 Stellenbosch University https://scholar.sun.ac.za List of Figures 1.1 Study Roadmap . 16 2.1 Data Value Chain . 25 2.2 Data Stream . 25 2.3 Multi-Data Value Chain . 26 4.1 A Decision Node’s Attributes . 71 4.2 Decision Node Example Part 1 (Created with Apple Numbers) . 77 4.3 Decision Node Example Part 2 (Created with Apple Numbers) . 78 4.4 Decision Node’s Cost . 86 4.5 Decision Node Amortization . 91 4.6 Decision Node’s Value . 93 4.7 Decision Node’s Performance .
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