There is no denying that the 21st century is data driven, with many digital industries relying on careful collection and analysis of mass volumes of information. A Chief Data Officer (CDO) at a company is the leader of this process, making the position an often daunting one. The Chief Data Officer Management Handbook is here to help.
With this book, author Martin Treder advises CDOs on how to be better prepared for their swath of responsibilities, how to develop a more sustainable approach, and how to avoid the typical pitfalls. Based on positive and negative experiences shared by current CDOs, The Chief Data Officer Management Handbook guides you in designing the ideal structure of a data office, implementing it, and getting the right people on board.
Important topics such as the data supply chain, data strategy, and data governance are thoughtfully covered by Treder. As a CDO it is important to use your position effectively with your entire team. The Chief Data Officer Management Handbook allows all employees to take ownership in data collaboration. Data is the foundation of present and future tech innovations, and you could be the leader that makes the next big impact.
What You Will Learn
Apply important elements of effective data management Gain a comprehensive overview of all areas of data (which are often managed independently Work with the data supply chain, from data acquisition to its usage, a review of all relevant stakeholders, data strategy, and data governance
Who This Book is For CDOs, data executives, data advisors, and all professionals looking to understand about how a data office functions in an organization.
By:
Martin Treder
Imprint: APress
Country of Publication: United States
Edition: 1st ed.
Dimensions:
Height: 235mm,
Width: 155mm,
Weight: 694g
ISBN: 9781484261149
ISBN 10: 1484261143
Pages: 435
Publication Date: 19 September 2020
Audience:
Professional and scholarly
,
Undergraduate
Format: Paperback
Publisher's Status: Active
1 Understand your organisation 221.1 Five implicit Data Governance models 241.2 Behavioural patterns in data matters 372 Aspects of effective Data Management 502.1 Maturity Assessment 512.2 The two main gaps 522.3 Subsidiarity 532.4 Business-orientation 542.5 Commercial Orientation 592.6 Collaboration 602.7 Motivation 662.8 The Data Supply Chain 662.9 Cross-functionality 832.10 Change Management 902.11 Data Literacy 903 Data Vision, Mission and Strategy 953.1 Data strategy - seriously? 963.2 Vision 1033.3 Mission 1083.4 Strategy 1133.5 Your individual measure of success 1154 Masterdata Management 1184.1 Isn't static data old-fashioned? 1194.2 What does Masterdata cover? 1214.3 Managing Masterdata 1374.4 MDM and Masterdata software 1415 Data Governance 1525.1 Shape a set of Data Principles 1535.2 Develop data policies 1565.3 The target state of managed data 1635.4 Scope of Data Governance 1645.5 Decision-making and collaboration 1666 The Data Language 1756.1 Don't we all speak English? 1766.2 The Data Glossary 1766.3 Data Rules and Standards 1876.4 The Data Model 1926.5 Choosing a software solution 2037 Data processes 2067.1 Why prescribing processes? 2077.2 Process Development Aspects 2137.3 General considerations 2147.4 Concrete Process Groups 2197.5 Manage Data in business processes 2318 Roles & Responsibilities 2358.1 Introduction 2368.2 Data Owners and Data Champions 2378.3 Data Creators and Consumers 2408.4 Other business roles 2428.5 Centralised roles 2449 Data Quality 2559.1 Why is Data Quality important? 2569.2 Dangerous Data Quality standpoints 2589.3 How to deal with Data Quality? 2699.4 Management of Business Metrics 27910 Shaping Data Office Teams 28810.1 The effective creation of data teams 28910.2 Data Architecture and Glossary 29010.3 Analytics 29710.4 Document Management 30610.5 Data Quality 31110.6 Organising Masterdata Management 31310.7 Data Project Office 31710.8 Data Service function 32210.9 Attracting and retaining experts 32610.10 Six Sigma 33611 Typical Challenges of a CDO 34911.1 Why is it so hard to be a CDO? 35011.2 Struggle for Supremacy 35611.3 Lack of Awareness 35911.4 Business Silos 36211.5 Lack of Ownership 36511.6 Opt-Out Attitude 36711.7 Disengagement 37011.8 Scepticism 37211.9 Business Arrogance 37311.10 Summary: Prerequisites for success 37512 How (not) to behave as a CDO 38312.1 Don't rely on formal authority 38412.2 Start small, and pick your battles 38412.3 Be humble 38512.4 Present yourself as a facilitator 38612.5 Avoid suboptimal language 38812.6 Go out and talk to people 38913 Stakeholders 39113.1 Determine your Executive allies 39213.2 Have the right stories 39313.3 Manage stakeholders at all levels 40613.4 Know the motives of your allies 40813.5 Shape your Data Network 40913.6 Orchestrate your Data Network 41813.7 Plan to consider different audiences 41913.8 Frequently stated concerns 42114 Psychology of Governance 44114.1 Don't claim covered ground 44214.2 Design an acceptable starting setup 44314.3 Base your authority on accepted authorities 44314.4 Balancing two extremes 44514.5 Shape your Data brand 45014.6 Elevator pitch 45115 Data Business Cases 45715.1 Business Cases for data - Why? 45815.2 Business Cases in a perfect world 46015.3 General Challenges 46615.4 Data-specific Challenges 47315.5 Eight Secrets of data business cases 47915.6 Use Cases for Data as an Asset 49516 Data Ethics and Compliance 50216.1 Ethical behaviour and data? 50316.2 GDPR - All done? 51617 The Outside World 52117.1 Why look beyond my organisation? 52217.2 Sharing Data across organisations 52317.3 External Data 52717.4 The CDM and external data 53117.5 Data Quality as a Service? 53717.6 Global Standards 54017.7 Cloud Strategy for Data 54517.8 Blockchain 56218 Handling Data 56918.1 The Virtual Single Source of Truth 57018.2 Single Source of Logic 57418.3 Configuration vs Standardisation 58218.4 Effective Date Concept 58418.5 Making Data international 58718.6 Data Debt Management 59218.7 Agile and Data 59718.8 Starting with the Happy Flow? 60119 Analysing data 61319.1 Preconditions of meaningful Analytics 61419.2 General limits of AI 61919.3 Recommendations around Analytics 63619.4 Explainable AI (XAI) 65520 Data in mergers and acquisitions 66720.1 What is going wrong today? 66820.2 Integration Planning 66920.3 The Data Approach 67320.4 Data Mapping 67921 Data for Innovation 68721.1 How can data drive innovation? 68821.2 Supporting data-driven innovation 69721.3 Commercialising Data Ideas 70922 APPENDIX 71522.1 Table of Figures 71522.2 List of Theorems 71822.3 Index 72123 Bibliography 730 *
Martin Treder is a seasoned, hands-on data executive and advisor with 25 years of experience in international corporations. During the past decade, Martin established and led the international data management organisations of DHL Express, TNT Express, and FedEx Express, covering the areas of data governance, masterdata management, data modelling, data quality, data science, and data analytics. While being a studied mathematician (main topics operations research and applied statistics), Martin has always focused on creating long-term commercial value through well-governed data, and on shaping a data-conscious culture. Today he helps companies transform into data-driven organizations.