The VALUE DRIVEN DATA Workbook: Practical exercises, templates, and tools for data value creation

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VALUE DRIVEN DATA THE WORKBOOK Practical exercises, templates, and tools for data value creation FIRST EDITION EDOSA ODARO

Publisher’s note Every possible effort has been made to ensure that the information contained in this book is accurate at the time of going to press, and the publishers and authors cannot accept responsibility for any errors or omissions, however caused. No responsibility for loss or damage occasioned to any person acting, or refraining from action, as a result of the material in this publication can be accepted by the editor, the publisher or the author.

First published in Great Britain and Great Britain in 2023 by Edosa Odaro Apart from any fair dealing for the purposes of research or private study, or criticism or review, as permitted under the Copyright, Designs and Patents Act 1988, this publication may only be reproduced, stored or transmitted, in any form or by any means, with the prior permission in writing of the publishers. Enquiries concerning reproduction outside these terms should be sent to the publishers at the undermentioned email address: [email protected] www.edosaodaro.com © Edosa Odaro, 2023 The right of Edosa Odaro to be identified as the author of this work has been asserted by him in accordance with the Copyright, Designs and Patents Act 1988.

To my mother and all mothers who have prematurely left this world: your presence is forever felt in our lives and your inspiration is forever reflected in our work.

CONTENTS ENDORSEMENTS FOR VALUE DRIVEN DATA ABOUT THE AUTHOR ACKNOWLEDGEMENTS INTRODUCTION PART ONE VISION: DISCOVERING AND CAPTURING DATA VALUE OPPORTUNITIES Chapter 01 Enhancing Understanding of Data Vision Exercise 1: Defining Data Value Exercise 2: Interpreting Data Vision Exercise 3: Differentiating Data Vision Exercise 4: Macro Data Vision Exercise 5: Separating Signal from Noise Exercise 6: Signal from Noise Optimization Techniques Chapter TWO Capturing Data Visions Exercise 1: Identifying Budget Challenges Exercise 2: Reframing Budget Challenges Exercise 3: Time Horizon and Budget Challenges Exercise 4: Current State Assessments Exercise 5: First Principle Thinking Exercise 6: Vision Perspectives and Leadership Style Chapter THREE Why Data Visions of All Size Matter Exercise 1: Understanding Data Accessibility Challenges Exercise 2: Analysing Data Granularity and Timeliness Exercise 3: Identifying Data Quality Issues Exercise 4: Recognizing Foundational Data Analysis Challenges Exercise 5: Exploring Data Vision Breakdown Exercise 6: Clear Goals Analysis Exercise 7: Tangible Purpose Exploration Exercise 8: Enriching Data Vision Techniques Exercise 9: Strategic Decision Enhancement Exercise 10: Reflection and Application Chapter FOUR The Destructive Impact of Data Vision Misalignment

Exercise 1: Evaluating Current Data Capabilities Exercise 2: Identifying Challenges with Data Vision Alignment Exercise 3: Detecting and Defusing Data Vision Displacement Exercise 4: Embracing Alternative Viewpoints Exercise 5: Framework for Disruption Detection Exercise 6: Unlocking the Power of Diversity Exercise 7: Phenomenology and Alignment CHAPTER FIVE Simplifying Data Vision Misalignments Exercise 1: Understanding the Three-Step Process for Data Vision Alignment Exercise 2: Conceptualizing Data Vision Alignment Exercise 3: Analysing the Streamlined Three-Step Process Exercise 4: Identifying Obstacles to Data Vision Alignment Exercise 5: Examining Speed as a Key Factor in Data Vision Alignment Exercise 6: Uncovering Data Quality Matters in Data Vision Alignment Exercise 7: Addressing Technology and Infrastructure Concerns Exercise 8: Reflecting on Data Vision Alignment Challenges Exercise 9: Applying the Streamlined Approach to Data Vision Alignment PART TWO OBSTACLES: THE THINGS THAT STAND BETWEEN DATA VISIONS AND DATA VALUE REALIZATION Chapter SIX Obstacles of the Past Exercise 1: Reflection on Heritage and Legacy Data Platforms Exercise 2: Exploring Data Use within a Legacy System Context Exercise 3: Shifting from Obstacles to Opportunities Exercise 4: Legacy Data for Decision-Making Exercise 5: Heritage Skills and Capabilities Exercise 6: Complacencies from Past Successes Exercise 7: Data Quality Assessment Exercise 8: Measuring Data Quality Impact Exercise 9: The Value of Timeliness Exercise 10: Overcoming Resistance to Change Exercise 11: Evaluating Buy vs. Build Trade-offs Chapter SEVEN Enhancing Understanding of Obstacles of the Future Exercise 1: Reflecting on Misunderstandings and Mistaken Assumptions Exercise 2: Identifying Disconnects Resulting from Mistaken Assumptions Exercise 3: Analysing Misplaced Assumptions Driving Inappropriate Solutions

Exercise 4: Addressing Unknown Obstacles Exercise 5: Understanding Personal Data Protection Exercise 6: Reflection and Analysis Exercise 7: Case Study Analysis Exercise 8: Applying Strategies Exercise 9: Reflection and Action Plan Chapter EIGHT Obstacles of the Present Exercise 1: Skills Matrix Analysis Exercise 2: Leadership Competency Assessment Exercise 3: Task Distribution Analysis Exercise 4: Decision Leadership Assessment Exercise 5: Reflection on Data Strategy Exercise 6: Responsible Leadership for High-Performing Teams Exercise 7: Overcoming Complexity and Complications Exercise 8: Seeing Beyond the Challenges Exercise 9: Fixing a Flying Plane - Transition and Migration Exercise 10: Reflection on Growth Limiting Factors Exercise 11: Analysing Obstacles for Future Growth Exercise 12: Critical Steps for Ensuring the "Right" Speed of Execution Exercise 13: Reducing Defensiveness for Collaborative Efforts Exercise 14: Addressing Budgetary and Funding Issues Exercise 15: Utilising the VOV Model for Commercial Value Connectivity Exercise 16: Understanding Minimum and Maximum Viability PART THREE VALUE: IDENTIFYING, CAPTURING AND COMMUNICATING DATA VALUE Chapter NINE Capturing Data Value Propositions Exercise 1: Understanding Data Value Propositions Exercise 2: Bottom-Line Value (BLV) Optimization Exercise 3: Top-Line Value (TLV) Optimization Exercise 4: Cost Avoidance Value (CAV) Exercise 5: Understanding Data Costs Exercise 6: A Business Stakeholder Perspective of Data Value Capture Exercise 7: RTB and CTB Optimization Exercise 8: Reflecting on Data Value Propositions Exercise 9: Applying Data Strategies Exercise 10: Evaluating Data Analytics Initiatives Exercise 11: Case Study Analysis

Chapter TEN Measuring Data Value for Business Case and Operational Assurance Exercise 1: Macro vs. Micro Data Value Measurement Exercise 2: Understanding Business Stakeholder Perspectives Exercise 3: Assessing Data Value in a Multifaceted Operation Exercise 4: Articulating Data Value Propositions Exercise 5: Addressing Cost-Avoidance through Data Value Exercise 6: Macro-Level Data Value Measurement Exercise 7: Generating a Data Value Business Case Exercise 8: Reflection and Application Exercise 9: Macro and Micro Approaches to Data Value Measurement Exercise 10: Stakeholder Perspectives on Data Value Measurement Exercise 11: Generating a Data Value Business Case Exercise 12: Data Value for Different Departments Chapter ELEVEN Understanding the Data Value Measurement Lifecycle Exercise 1: Estimation Phase Exercise 2: Delivery Phase Exercise 3: Operations Phase Exercise 4: The Triple BAT Model for Data Value Measurement Exercise 5: The Application of the Triple BAT Model Exercise 6: Milestones of the Data Value Measurement Lifecycle Exercise 7: Challenges in Data Value Estimation Exercise 8: Challenges in Data Value Validation Exercise 9: Challenges in Data Value Monitoring Chapter TWELVE Enhancing Understanding of Data Value Profits and Losses Exercise 1: Vision and Value Proposition Exercise 2: Understanding the Impact of Returns Exercise 3: Estimating Value Returns on Investment Exercise 4: Identifying Challenges for Data Value P&L Exercise 5: Reflecting on the Challenges for a Data Value P&L Exercise 6: Simplifying Data Value Assessment Exercise 7: Increasing Resource Autonomy Exercise 8: Reducing Interdependencies Exercise 9: Overcoming Traditional Obstacles with Silos Exercise 10: Case Study Analysis Exercise 11: Essential Preconditions for a Data Value P&L Exercise 12: Reflection and Application

Exercise 13: Group Discussion Exercise 14: Action Plan Chapter THIRTEEN Presenting Data Value to Executives and the Board Exercise 1: Presentation Structure Analysis Exercise 2: Unexpected Findings Exercise 3: Identifying Obstacles Exercise 4: Focusing on Ambitious Visions and Associated Value Exercise 5: Transforming Data through Connected Organisational Silos Exercise 6: Role Analysis and Reflection Exercise 7: Technology Platforms and Data Transformation Exercise 8: People and Culture in Data Transformation Exercise 9: Decoupled Data Value Framework Exercise 10: Unpacking Data Value Presentation Slides CONCLUSION: BRINGING IT ALL TOGETHER YOUR JOURNEY CONTINUES: BUILDING ON "VALUE DRIVEN DATA" Empower Yourself Empower Others

ENDORSEMENTS FOR VALUE DRIVEN DATA “Value-Driven Data is a timely and practical guide to support us all with the challenge of unlocking and measuring the value of data. This thought-provoking book is filled with practical examples to support frameworks and theories. A must read for all executives.” Dr Johanna Hutchinson, Chief Data Officer, BAE Systems and Board Member, The Royal Statistics Society "A powerful reminder that data is not just a valuable asset, but a critical driver of business success and unlocking new value pools sitting at the intersection of technology and sustainable business." Lamé Verre, Head of Strategy, Innovation & Sustainability, SSE Energy Customer Solutions and Global Future Council Member, World Economic Forum "Edosa has masterfully stitched together a collection of great examples with a set of tangible principles to guide readers on how to enhance their potential with data. The insights that this book provides are unique, the advice practical and the success stories applicable across industry sectors." Mona Soni, Chief Technology Officer, formerly at S&P Global and Dow Jones “Value-Driven Data is an excellent book and a valuable resource for anyone looking to cut through the noise. It provides clarity on how to quantify the financial impact of data initiatives and effectively communicates with senior and non-technical audiences using clear and concise language.” Amy Shi-Nash, Chief Analytics & Data Officer, Tabcorp and Data Board Member, MIT Sloan School of Management "Value-Driven Data offers a combination of deep knowledge and practical value for leaders guiding organizations through the responsible use of data. Odaro brings together a variety of perspectives from data practitioners and consultants to executive leadership in global businesses. I hope his shared knowledge will reach data professionals around the world and contribute to their success." Simone Steel, Chief Data and Analytics Officer & CIO for Enterprise Data Platforms, Nationwide Building Society “Value Driven Data cuts through the rumours and hearsay with real-life, no-nonsense examples of creating a data vision and value in practice. This is a comprehensive guide for both data professionals and business leaders. Once you have read it you won't want to do research without it.” Graeme McDermott, Chief Data Officer, Tempcover

“Provides insightful frameworks and considerations for every organization that wants to get more value out of data and analytics.” Gero Martin Gunkel, Data Science Leader & Chief Operating Officer (ZCAM), Zurich Insurance "Value-Driven Data provides a comprehensive framework for developing a data vision that aligns with the overall strategy of an organisation. One of the most impressive aspects of the book is how it breaks down complex concepts into easy-to-understand language, making it an enjoyable read for anyone interested in data strategy, regardless of their level of expertise.” Rowland Agidee, Head of Data Management, UK Intellectual Property Office "A masterclass in how to unlock the true value of data for your organization. Value-Driven Data is a must read for all data leaders." Hartnell Ndungi, Chief Data Officer, Absa Group “Edosa brings his experience and expertise together to remind us all of how expressing data value is fundamental to data driven transformation.” JC Lionti, Managing Director & Chief Data Officer, Mizuho Americas “Edosa has done terrific work in producing this masterpiece! I like the way he has used data visions as the starting point and has linked all chapters to it by creating a practical and actionable book to help organizations realize their full potential.” Ram Kumar, Chief Data & Analytics Officer, Cigna “This book is an incredible resource, full of frameworks and tools to help navigate the elusive topic of data value in an easy to digest format and with stories drawn from Edosa’s long professional career. An especially valuable instrument in an era of cost optimization, providing knowledge to the reader to aid in directing and articulating vision, value and creating the pathways to overcome obstacles.” Stylianos Taxidis, Head of Data Science & AI, Costain Group “Finally, a book that makes delivering value through data the number one priority. Business Leaders, whilst interested, do not really care how we as data professionals do it. Influencing Top line, Cost Avoidance and Bottom line are central to 99.9% of business strategies and so should also be the main focus when creating data strategies. Using real-world and highly relatable examples, Edosa has delivered an essential read for both data and business professionals.” Sam Richmond, Group Head of Data, The Go-Ahead Group

ABOUT THE AUTHOR

Edosa Odaro is an AI and data transformation leader who has helped countless organizations deliver significant value through advanced data analytics, recombinant innovations, and valuebased intelligent interventions. Currently serving as a Chief Data Analytics and Privacy Officer, Edosa also serves as a strategic adviser, and as a non-executive director on selective corporate boards. Beyond his academic backgrounds in economics, computer science, business, technology, and innovation, Edosa’s ability to solve problems is enhanced by his significant focus on the importance of people and of culture – as well as his passion for a brand of inclusion that is underpinned by the empowerment of diverse minds. He is a multi-published author, a guest lecturer, and a regular speaker. Edosa is also a multiaward winner, including being named a Financial Times Top 100 Most Influential Leader, a Global 100 Data Activator, and one of the 30 Most Influential Leaders in FinTech & Data. Besides the world of work, Edosa enjoys spending time with his wife, his two sons, and his family – experiencing a wide variety of activities, including football, cycling, running, and skiing.

ACKNOWLEDGEMENTS After successfully writing my first book (Making data work), this second book was meant to be easy. Instead, it came at a challenging time of change, not just for global geopolitics but also for me personally. The largest outbreak of war in Europe in recent times sent shockwaves through global food and energy markets, driving the deepest hyperinflation for decades, which ultimately resulted in a cost-of-living crisis; I had a change of job and home to a different country and faraway continent. In spite of these challenges, I have enjoyed revisiting the insights that inspired this book. They come from a wide range of sources and experiences gained through interactions with thousands of individuals across hundreds of companies – and over three decades of continuous learning in data, AI and technology. I am therefore grateful to all my colleagues, including those from AXA, Allianz, TaP (Theory & Practice), Barclays Group, Flutter Entertainment, Betfair, British Sky Broadcasting, JLL, the European Commission, Lloyds, Defra, Lloyds Banking Group, Natural England, HMRC, Severn Trent Water, Marsh, Citigroup, Egg Bank, Channel 4, Liverpool Victoria’s LV, AIG and The Company for Cooperative Insurance (Tawuniya). I am also fortunate to have met incredible minds by sitting on multiple boards, including those of Harper Adams University and the UK’s National Institute for Health Data Science (HDR UK). I must also acknowledge my academic alumni to whom I largely owe my invaluable background in economics, computer science, business, technology and innovation. I am immensely grateful to my endorsers and beta readers, who not only took precious time out of their challenging schedules to review my unfinished work but also went many steps beyond this – to provide the invaluable tough love needed to refine this book to the best it could possibly be. My sincerest appreciation goes to my dearest friends and closest family. To my friends, I say thank you for being an ever-present source of essential encouragement and for helping me step away from it all in moments of intense challenge or at the point when a different perspective was beneficial. To my father and siblings: without your critique, support and encourage- ment, this would never have happened. To my young nieces and nephews, who provided a powerful motivation for actively modelling the kind of world we hope to build for the future, I send my deepest appreciation. To my beloved Efe and Azuwa, I do not think I can ever thank you enough for your love, patience and understanding. You know you are the best. Yet my deepest debt of gratitude is to my wife, who is both my greatest critic and my biggest supporter. Anja, I could never have done this without you. From the first page of the first book to the last page of this one, you have been the invisible force propelling me through the greatest of challenges. Thank you for being there when I needed a shoulder to lean on but also for creating the space required for enabling this work to progress. By making significant sacrifices in family time and by taking on far more than your fair share, you enabled the continuation of those essential things that we too often take for granted.

INTRODUCTION Welcome to the Value Driven Data Workbook – your ultimate guide to unlocking the full potential of data for value creation. This workbook is designed to complement the main book, providing you with practical exercises, templates, and tools to help you apply the concepts and strategies discussed in the book to your own data-driven initiatives. Whether you're a company executive or a data scientist, a business leader or a technologist, a project leader or simply someone who wants to understand how to derive value from data, this workbook will empower you to take action and drive real value from your data. Through a series of interactive exercises and case studies, you'll learn how to identify and capture data visions, overcome obstacles, and measure and communicate data value to stakeholders. The workbook is available in both printed paperback and interactive eBook formats, making it easy to access and use wherever you are. Whether you prefer to work through exercises on paper or on your tablet, this workbook has you covered. So, get ready to dive deeper into the world of data and discover the amazing opportunities that lie ahead. With this Value Driven Data Workbook, you'll have everything you need to supercharge your journey into the future and unlock the full potential of data for value creation.

PART ONE VISION: DISCOVERING AND CAPTURING DATA VALUE OPPORTUNITIES

Workbook

Chapter 01 Enhancing Understanding of Data Vision

Exercise 1: Defining Data Value Instructions: Answer the following questions to enhance your understanding of data value. 1. What is data value? 2. Why is it challenging to capture, define, and quantify data value? 3. How can data value be made tangible for decision makers? 4. Does data value have to be described in financial terms? 5. What are the potential outcomes when a data-powered AI system delivers negative value?

Exercise 2: Interpreting Data Vision Instructions: Match each interpretation of data vision with its corresponding description (consider using boxes similar to those illustrated below). Interpretations

Descriptions

a) Data vision as insights

1. Refers to any intelligence that can be gained from a collection of data.

b) Data vision as perspectives

2. Involves hypothesising what set of questions a data collection can help address and what use cases it can deliver against.

c) Data vision as envisioning answers

3. Focuses on exploring the collection of divergent deductions that can be harvested from a single assembly of data attributes.

Exercise 3: Differentiating Data Vision Instructions: Answer the following questions to differentiate between data vision and data-enabled vision. 1. How does data-enabled vision differ from data vision? 2. Why is starting with objectives important for driving value from data? 3. Explain the significance of considering the "to what end" before the "how to get there."

Exercise 4: Macro Data Vision Instructions: Read the CEO's statement about the organisation's macro data vision and answer the following questions. 1. What are the two main issues the CEO identified regarding data platforms and customer data? 2. How does the CEO envision solving these issues and improving data accessibility? 3. What are the CEO's expectations regarding the project's outcome?

Exercise 5: Separating Signal from Noise Instructions: Answer the following questions to enhance your understanding of separating signal from noise. 1. What does it mean to separate signal from noise in data science? 2. Provide real-world examples of signal separation in different industries. 3. Explain the four categories of analytics used in data science: descriptive, diagnostic, predictive, and prescriptive.

Exercise 6: Signal from Noise Optimization Techniques Instructions: Match each signal from noise optimization technique with its description (consider using boxes similar to those illustrated below). Techniques

Descriptions

a) Consolidation

1. Pulling disparate components together to enhance organisation and understanding within a single context.

b) Triangulation

2. Comparing and combining data from multiple sources to validate and strengthen signals.

c) Context switching

3. Shifting focus and perspective to gain new insights

and identify additional signals. Note: These exercises are designed to enhance your understanding of the concepts discussed in Chapter 01. Take your time to reflect on each question and provide thoughtful responses.

Chapter TWO Capturing Data Visions

Exercise 1: Identifying Budget Challenges Instructions: Identify the budget challenges mentioned in the chapter and answer the following questions. 1. What was the CEO's frustration regarding the budget? 2. What are the key objectives in the data vision related to budget challenges? 3. How can shifting investments from report creation benefit the data vision?

Exercise 2: Reframing Budget Challenges Instructions: Apply the reframing techniques discussed in the chapter to the budget challenges mentioned. Answer the following questions. 1. How can you reframe the data vision to focus on ultimate outcomes instead of intermediate products? 2. How can you enable clearer visibility between investments and their beneficial returns in the data vision? 3. How can you shift the focus from cost reduction to increasing the marginal rates of returns in the data vision?

Exercise 3: Time Horizon and Budget Challenges Instructions: Analyse the importance of time horizon in addressing budget challenges. Answer the following questions.

1. Why is being clear about the time horizon essential for addressing budget challenges? 2. How can an awareness of the underlying time horizon help in analysing budget frustrations? 3. How can reframing and repositioning the analysis within the context of a constraint-free future benefit the data vision?

Exercise 4: Current State Assessments Instructions: Compare the traditional approach of current state assessments with the fresh-eyed approach in this chapter and then answer the following questions. 1. What are the main differences between the traditional approach and the fresh-eyed approach to current state assessments? 2. How does the fresh-eyed approach lead to outcomes more closely aligned with radical innovation? 3. How does starting with the target state ambition influence the current state assessment in the fresh-eyed approach?

Exercise 5: First Principle Thinking Instructions: Apply first principle thinking to the tactical delivery of the Easy to Reach - Real Time - Vision Extraction framework. Answer the following questions. 1. What are the key principles of first principle thinking? 2. How can starting from the end and employing active listening playback support the application of first principle thinking? 3. How can first principle thinking contribute to the successful delivery of the data vision?

Exercise 6: Vision Perspectives and Leadership Style Instructions: Reflect on the importance of vision perspectives and leadership styles in capturing data visions. Answer the following questions (consider using a table similar to the one illustrated below). 1. Why is it important to seek 2. How can the "conqueror" diverse perspectives in mindset of certain team capturing the data vision? members contribute to the data vision?

3. How do leadership styles influence the construction of the data vision?

Note: These exercises are designed to enhance understanding and promote critical thinking about the concepts discussed in Chapter 2. Feel free to provide detailed answers and additional insights based on your interpretation of the chapter.

Chapter THREE Why Data Visions of All Size Matter

Exercise 1: Understanding Data Accessibility Challenges Instructions: Read the following scenario and answer the questions based on the information provided. Scenario: A senior leader in a multinational services company expressed their frustration with the lack of data accessibility. They needed access to sales activity data at a granular level but faced challenges due to the data being available only after a few months and only in large Excel sheets. They believed that resolving data accessibility issues would significantly improve their overall data capabilities. Questions: 1. Why was data accessibility important for the senior leader? 2. What challenges did the senior leader face in accessing the required data? 3. How did the senior leader believe resolving data accessibility issues would impact their data capabilities?

Exercise 2: Analysing Data Granularity and Timeliness Instructions: Read the following statements and classify each statement as related to data granularity or data timeliness. Statements: 1. "The further back you go, the less granular the data needs to be because we're not comparing day-by-day activities." 2. "For new events, we need data close to real-time." 3. "Data granularity refers to the amount of detail captured within a data set." 4. "Data timeliness relates to the freshness of the data." 5. "For older events, requirements for data timeliness are more forgiving."

Exercise 3: Identifying Data Quality Issues Instructions: Read the descriptions of data quality issues and match each description to its corresponding data quality issue (consider using boxes similar to those illustrated below). Data Quality Issues

Descriptions

1. Completeness

a. This data quality issue concerns the comprehensive inclusion of all relevant information in a dataset.

2. Timeliness

b. This data quality issue relates to the freshness and accessibility of data, capturing how quickly data becomes available after an event.

3. Correctness

c. This data quality issue focuses on the accuracy and reliability of the data, ensuring alignment with trusted sources.

Exercise 4: Recognizing Foundational Data Analysis Challenges Instructions: Read the statements about the challenges faced by the team member responsible for compiling reports and indicate whether each statement is related to data accuracy or data accessibility. Statements:

1. "Reports often need to be done again and again due to data accuracy issues." 2. "Data is not accurate and often needs manual correction." 3. "The team member needs to test every report to ensure its accuracy." 4. "Reports are not easily accessible, requiring checking between different platforms."

Exercise 5: Exploring Data Vision Breakdown Instructions: Answer the following questions based on the information provided in the chapter. Questions: 1. Why is it important to break down data visions into smaller tangible parts? 2. How can you determine the smallest measurable parts of a data vision? 3. What are the two fundamental questions that must precede any data vision efforts? 4. How are the questions "Why do we do what we do?" and "Why do we want what we want?" different? 5. What is the desired outcome of articulating a data vision?

Exercise 6: Clear Goals Analysis 1. Identify the clear goals mentioned by the senior executive at the leisure and entertainment business. 2. Describe the importance of a strong feedback loop to local operations and development. 3. Explain how empowering local teams to resolve issues can contribute to success. 4. Discuss the significance of having a clear understanding of data to inform business decisions.

Exercise 7: Tangible Purpose Exploration 1. Define tangible purpose and its role in data vision mining. 2. Analyse the impact of issues, such as lack of data and fragmented information, on long-term planning. 3. Discuss the challenges faced by the chief data analytics officer in accessing and verifying data. 4. Explain the importance of real-time data and consistent data across departments.

Exercise 8: Enriching Data Vision Techniques 1. Describe the challenges in capturing data vision effectively. 2. Explain the three key questions that should underpin the quest for driving data value. 3. Apply the simplifying framework of capturing, labelling, and filtering data vision elements. 4. Discuss the case of the experienced executive struggling with business growth and their realisation of the importance of data.

Exercise 9: Strategic Decision Enhancement 1. Analyse the potential benefits of robust data in exploring partnerships with major brands. 2. Explain how access to cross-sectional data can contribute to broader touchpoints in the target customers' value journeys. 3. Discuss the advantages of becoming a significant data repository in target markets. 4. Evaluate the potential of marketing capabilities to challenge larger competitors.

Exercise 10: Reflection and Application 1. Reflect on a real-life scenario where clear goals and tangible purpose were lacking or misaligned in a data-related project. 2. Discuss the potential consequences of not addressing the challenges identified in the scenario.

3. Brainstorm strategies and actions to ensure clear goals and tangible purpose are incorporated into future data vision initiatives. 4. Apply the principles learned from this chapter to develop a data vision statement for a hypothetical business scenario of your choice.

Chapter FOUR The Destructive Impact of Data Vision Misalignment

Exercise 1: Evaluating Current Data Capabilities Instructions: Contemplate the questions below and reflect on your thoughts and observations regarding the current data capabilities of your organisation. 1. How would you describe the current state of data capabilities in your organisation? 2. Are there any specific challenges or barriers that hinder the effective use of data? 3. What are the different perspectives and visions regarding the use of data within your organisation? 4. How aligned are these perspectives and visions across different departments and teams? 5. In what ways do people and processes impact the effectiveness of data utilisation?

Exercise 2: Identifying Challenges with Data Vision Alignment Instructions: Read the section on common challenges with data vision alignment. Answer the following questions based on your understanding. 1. Who is typically responsible for setting the direction of data vision within an organisation? 2. What are the challenges associated with relying solely on a majority vote for decisionmaking? 3. Why does data vision alignment often, ironically, generate resistance and friction? 4. How can reducing friction benefit forward momentum in data strategies? 5. What is the relationship between fuel and friction in driving data value?

Exercise 3: Detecting and Defusing Data Vision Displacement Instructions: Consider the questions provided below and think about how you can detect and diffuse data vision displacement within your organisation. 1. How can you identify views that deviate significantly from the consensus? 2. What alternative approaches could help reduce or address concerns regarding data vision alignment? 3. What are the potential costs and benefits of displacing or incorporating concerns into the overall data vision? 4. How do the impacts of each data vision align with the interests and goals of concerned individuals or teams?

Exercise 4: Embracing Alternative Viewpoints Instructions: Reflect on the importance of gaining alternative viewpoints for driving data value and innovation. Answer the following questions based on your understanding. 1. Why is it critical to embrace diverse perspectives and alternative viewpoints in data-driven decision-making? 2. What are the downsides of failing to seek out diverse perspectives and data sources? 3. How can a lack of diversity in data-driven innovations impact their potential impact and success? 4. What steps can you take to ensure that your organisation actively seeks and incorporates alternative viewpoints into data strategies and initiatives?

Exercise 5: Framework for Disruption Detection Instructions: Apply the provided framework for disruption detection to assess the impact of potential changes and innovations in your organisation's data vision. 1. Create a process map for the current state of data operations and vision. 2. Ensure the process map includes key elements such as what, who, and when. 3. Create a process map for a data vision-driven future replacement. 4. Compare the outcomes and mapped steps between the current state and the future replacement. 5. Evaluate the tangible differences and potential impacts of each vision on the organisation.

Exercise 6: Unlocking the Power of Diversity Instructions: Reflect on the alternative strategy for unlocking the power of diversity and answer the following questions. 1. Why might a narrow focus on diversity lead to unintended consequences? 2. What is the significance of focusing on the "to what end" rather than exclusively favouring diversity? 3. How can shifting the focus from alignment to misalignment promote the inclusion of diverse ideas and approaches? 4. What steps can you take to ensure that diverse viewpoints and perspectives are valued and utilised in your organisation's data vision?

Exercise 7: Phenomenology and Alignment Instructions: Reflect on the concept of phenomenology and its application in addressing the challenge of alignment. Answer the following questions. 1. What is phenomenology, and how does it differ from other research approaches? 2. How can phenomenology be applied to understand experiences and perspectives related to data vision

CHAPTER FIVE Simplifying Data Vision Misalignments

Exercise 1: Understanding the Three-Step Process for Data Vision Alignment Instructions: Read the description of the three-step process for data vision alignment mentioned in Chapter 05. Then, answer the following questions: 1. What are the three steps in the streamlined process for data vision alignment? 2. What is the purpose of the "Capture" step? 3. How are themes clustered in the "Cluster" step? 4. What is the goal of the "Consolidate" step? 5. In what situations is the streamlined approach recommended over the purist methodology?

Exercise 2: Conceptualizing Data Vision Alignment 1. Define data vision alignment in your own words. 2. Explain the importance of data vision alignment for organisations. 3. Discuss the challenges that can arise when attempting to achieve data vision alignment.

Exercise 3: Analysing the Streamlined Three-Step Process 1. Describe the three steps of the streamlined process for data vision alignment assessment. 2. Compare and contrast the streamlined process with the purist methodology for data alignment. 3. Discuss the situations in which the streamlined process is more suitable compared to the purist methodology.

Exercise 4: Identifying Obstacles to Data Vision Alignment 1. Summarise the obstacles highlighted by the senior operational strategy executive, in achieving data vision alignment. 2. Explain the significance of an omni-channel data solution in addressing data vision alignment challenges. 3. Discuss the implications of having multiple business channels and platforms on data compilation and alignment.

Exercise 5: Examining Speed as a Key Factor in Data Vision Alignment 1. Explore the role of "speed to market" in data vision alignment. 2. Analyse the challenges faced by the data analyst in obtaining timely and granular data. 3. Discuss the benefits of real-time data access and its impact on data vision alignment.

Exercise 6: Uncovering Data Quality Matters in Data Vision Alignment 1. Evaluate the impact of data quality on data vision alignment efforts. 2. Explain the significance of having a single customer view capability in data vision alignment. 3. Discuss the challenges faced in mapping the data received from the central data warehouse.

Exercise 7: Addressing Technology and Infrastructure Concerns 1. Describe the issues related to technology and infrastructure that hinder data vision alignment. 2. Discuss the role of computational power in generating timely reports and its impact on data vision alignment. 3. Analyse the implications of delayed data availability on monitoring and managing shop performance.

Exercise 8: Reflecting on Data Vision Alignment Challenges 1. Reflect on the data vision alignment challenges discussed in the chapter. 2. Identify potential solutions or strategies to overcome these challenges. 3. Discuss the benefits that organisations can gain from achieving effective data vision alignment.

Exercise 9: Applying the Streamlined Approach to Data Vision Alignment Instructions: Imagine you are a data strategist in the organisation described in this chapter. Develop a plan for applying the streamlined three-step process for data vision alignment to address the challenges mentioned in the chapter. Include the following details: 1. Specify the data capture methods and tools you would use to implement the "Capture" step. 2. Explain how you would cluster themes vertically in the "Cluster" step and provide examples of potential themes. 3. Describe the process of horizontally conducting and organising data visions in the "Consolidate" step. 4. Outline the expected benefits and outcomes of applying the streamlined approach to address the identified challenges. 5. Propose a timeline and implementation strategy for executing the three-step process in your organisation. Note: The exercises in this workbook chapter are designed to enhance your understanding of the concepts discussed in Chapter 5 of the book. You can use these exercises to reflect on the key ideas, analyse the challenges faced by organisations, and think critically about potential solutions. The answers to this exercise will vary based on individual interpretations and insights into the organisation's specific context and requirements.

PART TWO OBSTACLES: THE THINGS THAT STAND BETWEEN DATA VISIONS AND DATA VALUE REALIZATION

Workbook

Chapter SIX Obstacles of the Past

Exercise 1: Reflection on Heritage and Legacy Data Platforms 1. Take a moment to reflect on your own experiences with heritage and legacy data platforms. Have you encountered any challenges or limitations with such systems? What impact did they have on your work or organisation? 2. Identify specific examples of monolithic infrastructure within your organisation or industry. How does this type of technology landscape affect the ability to deliver outstanding results? What trade-offs or dilemmas might arise from such systems? 3. Consider the approaches for addressing legacy systems challenges mentioned in the chapter (building new infrastructure, creating hybrid landscapes, retiring old systems). Which approach do you think would be most suitable for your organisation? Why?

Exercise 2: Exploring Data Use within a Legacy System Context 1. Think of a specific use case within your organisation where data plays a crucial role. What specific data is required to satisfy the minimum requirements for this use case? 2. Reflect on the challenges of working within a legacy environment. Are the minimum data requirements available within your current infrastructure? If not, how challenging would it be to access or collect this data? 3. Brainstorm potential solutions or workarounds for accessing the required data within a legacy system context. Consider the feasibility and potential impact of each solution.

Exercise 3: Shifting from Obstacles to Opportunities 1. Reflect on a situation where you or your organisation faced obstacles or challenges related to legacy systems. How did you approach these obstacles? Were you able to find opportunities for progress or innovation despite the limitations? 2. Consider the key insights mentioned in the chapter regarding data use objectives and real-time data systems. Can you identify any scenarios within your organisation where offline batch processing could be sufficient instead of investing in real-time data systems? Share your thoughts. 3. Imagine a hypothetical scenario where your organisation adopts real-time data systems. How do you think this shift would impact your work or the organisation as a whole? What opportunities and challenges might arise from this transition?

Exercise 4: Legacy Data for Decision-Making 1. Reflect on a situation where you or your organisation faced challenges related to data storage and performance. How did these challenges impact decision-making processes? 2. Consider the key insights mentioned in the chapter regarding the optimal structure for insights from various business streams and the use of different database technologies. How might adopting alternative database technologies or restructuring data storage impact decision-making within your organisation? 3. Brainstorm potential solutions or improvements for managing and accessing large volumes of data within your organisation. Think about the key insights provided in the chapter and how they could be applied to address your specific challenges.

Exercise 5: Heritage Skills and Capabilities 1. Reflect on a time when your organisation underwent a platform transformation. How did this transformation impact the skills and capabilities of the data team or individuals involved? 2. Consider the key insights mentioned in the chapter regarding legacy capability and capability transformation. How important is it to address both platform transformations and capability transformations simultaneously? Share your thoughts and experiences. 3. Imagine you are leading a platform transformation project within your organisation. How would you approach engaging and upskilling the data team to ensure their success and alignment with the transformation goals? Brainstorm specific strategies or initiatives that could be implemented.

Exercise 6: Complacencies from Past Successes 1. Reflect on a situation where past successes became obstacles or barriers to progress within your organisation. How did complacency or rigid attitudes towards past decisions impact the organisation's ability to drive data value?

2. Consider the examples of obstacles mentioned in the chapter and reflect on their presence within your organisation. Are there any decisions or biases that might be hindering progress without being fully recognized? 3. Brainstorm strategies or approaches that could help

Exercise 7: Data Quality Assessment Instructions: 1. Take a moment to reflect on your own organisation or a hypothetical one. Identify three key data quality challenges that you believe could hinder data value creation. 2. For each challenge, describe its impact on the organisation and its potential consequences. 3. Brainstorm potential solutions or mitigation measures for each challenge. 4. Discuss your answers with a partner or a small group to gain different perspectives and insights.

Exercise 8: Measuring Data Quality Impact Instructions: 1. Imagine you are the CEO of a company facing data quality issues. Identify three specific areas or processes in your organisation that are currently impacted by data quality challenges. 2. For each area or process, estimate the potential financial losses or negative consequences caused by these data quality issues (consider using a table similar to the one illustrated below). Potential Financial Losses

Negative Consequences

3. Discuss with a partner or a small group the importance of measuring the impact of data quality issues and how it can help prioritise investments in resolving these challenges.

Exercise 9: The Value of Timeliness Instructions: 1. Consider a business scenario where the timing of data is critical for decision-making. Describe this scenario and explain why timely data is essential in this context. 2. Identify potential consequences or risks that may arise from using outdated or delayed data in the scenario you described. 3. Brainstorm strategies or solutions that can improve the timeliness of data in the given scenario. 4. Share your ideas with others and discuss the significance of data freshness and accuracy in

driving effective decision-making processes.

Exercise 10: Overcoming Resistance to Change Instructions: 1. Reflect on a situation in your organisation or a hypothetical one where there is resistance to change, particularly related to data transformation initiatives. 2. Identify the signs or behaviours that indicate resistance to change within the organisation. 3. Discuss potential strategies or approaches to address resistance to change and encourage a more open mindset towards data value strategies. 4. Share your insights and recommendations with a partner or a small group to exchange ideas and learn from different experiences.

Exercise 11: Evaluating Buy vs. Build Trade-offs Instructions: 1. Imagine you are a decision-maker in an organisation considering whether to buy or build a data management platform. Outline the specific needs and challenges your organisation faces that influence this decision. 2. Evaluate the five key criteria for evaluating buy versus build trade-offs: current speed, cost, skills, adaptability, and future speed. Assess how each criterion applies to your organisation's context (consider using a table similar to the one illustrated below). Current Speed

Cost

Skills

Adaptability

Future Speed

3. Make a pros and cons list for both buying and building a data management platform based on the evaluation of the key criteria (consider using a table similar to the one illustrated below). Pros

Cons

4. Discuss your analysis and conclusions with a partner or a small group to gain different perspectives and insights. Remember: These exercises are designed to enhance your understanding of the obstacles

related to the topics discussed throughout Chapter 6. Working through these exercises will help you apply the concepts to real-world scenarios and encourage critical thinking about potential solutions and strategies. Also remember to reflect on your own organisation's context or use hypothetical scenarios to personalise the exercises.

Chapter SEVEN Enhancing Understanding of Obstacles of the Future

Exercise 1: Reflecting on Misunderstandings and Mistaken Assumptions Instructions: Read the passage about the luxury goods retailer's returns problem and the application of the "five whys" technique. Answer the following questions to enhance your understanding. 1. What was the original assumption regarding the 10 percent final sale discount? 2. Why did the organisation want to reduce the number of returns? 3. What was the ultimate objective of reducing the cost of returns? 4. How was the assumption about the 10 percent discount evaluated? 5. What did the aggregated test reveal about the impact of applying 10 percent discounts to all predicted returns? 6. Why do misunderstanding ultimate objectives lead to suboptimal outcomes or negative returns on investments? 7. How did the intelligent intervention model help in expanding the scope of possibilities? 8. Why is it important to monitor and evaluate assumptions over time?

Exercise 2: Identifying Disconnects Resulting from Mistaken Assumptions Instructions: Read the section about the disconnect between the central data team and the rest of the organisation in the financial services company. Answer the following questions to enhance your understanding.

1. What was the concern expressed by a key stakeholder regarding the BI solution project? 2. Why did the stakeholder believe there was a disconnect between the central team and other teams? 3. How did the sessions with the central team confirm the stakeholder's concerns? 4. What does the BI project represent in terms of misalignment and mistaken assumptions? 5. Why are such disconnects and misalignment challenges common across companies and industry sectors?

Exercise 3: Analysing Misplaced Assumptions Driving Inappropriate Solutions Instructions: Read the passage about the multinational media firm and its consideration of market-specific differences. Answer the following questions to enhance your understanding. 1. What were the distinct market conditions mentioned by the marketing specialist? 2. How did technology adoption differ in some markets compared to Europe or North America? 3. What role did trust play in the adoption of tech solutions in different markets? 4. Why is it important to consider market-specific customer behaviour and avoid misplaced assumptions? 5. How can insights from practices that work well be utilised for driving data value?

Exercise 4: Addressing Unknown Obstacles Instructions: Read the section about the senior executive's challenge of addressing obstacles that are unknown. Answer the following questions to enhance your understanding. 1. What was the main concern of the senior executive regarding unknown obstacles? 2. How did the executive navigate the challenge of not having concrete market data? 3. What is the biggest challenge faced when dealing with the risk of the unknown? 4. How can the risks associated with unknown obstacles be reduced? 5. Why is it important to capture facts, verify sources, stress-test scenarios, consider risks, and apply counter-mitigation?

Exercise 5: Understanding Personal Data Protection Instructions: Read the section about personal data protection and its opportunities and obstacles. Answer the

following questions to enhance your understanding. 1. What are the key drivers of data protection? 2. Why is personal data protection important across industry sectors? 3. What were the concerns of the chief data officer at the insurance firm regarding cloud accessibility? 4. How was the insurance firm indirectly exposed to the cloud? 5. Why is it crucial to identify and mitigate obstacles related to personal data protection?

Exercise 6: Reflection and Analysis Instructions: Take a few moments to reflect on the key concepts and insights presented in this chapter. Then, answer the following questions to analyse your understanding of the chapter: 1. What are the three key consequences of going too fast in data value transformation? 2. Why is it important to ensure that data value solutions match the needs they seek to address? 3. What are the five steps suggested for addressing misalignment obstacles? 4. Why is getting the basics right crucial for successful data value projects? 5. Name the five essential dimensions to consider when getting the basics right. 6. What are the three helpful questions to ask when getting the basics right? 7. Why is ineffective communication a significant obstacle in driving data value? 8. Explain the concept of "data labels" and their role in addressing communication obstacles. 9. How can descriptive and consistent data labelling help overcome communication challenges? 10. What are the three key steps for addressing ineffective communication?

Exercise 7: Case Study Analysis Instructions: Read the case study provided below and answer the questions that follow to analyse and apply the concepts discussed in the chapter. Case Study: The Challenge of Too Much Information A healthcare organisation implemented a data-driven solution to improve patient outcomes and reduce costs. The solution generated a vast amount of data, providing detailed insights into patient health and treatment effectiveness. However, the organisation soon faced challenges in managing and utilising this abundance of information effectively. 1. What is the main challenge faced by the healthcare organisation? 2. How does this challenge relate to the concept of "too much of a good thing"?

3. Explain the three aspects of consideration for enabling desirable optimization in the case study. 4. What were the key challenges with the predictive model originally delivered? 5. What were the consequences of the increased number of "true positives" produced by the predictive model? 6. What are the three viable alternatives suggested for resolving the "confirmation feedback" situation?

Exercise 8: Applying Strategies Instructions: Imagine you are a data leader in an organisation facing obstacles similar to those discussed in the chapter. Answer the following questions to apply the strategies presented in the chapter to your specific situation. 1. Identify one obstacle related to data value in your organisation. 2. How does this obstacle impact your organisation's goals and objectives? 3. Choose one strategy discussed in the chapter that could help address this obstacle. 4. Describe how you would implement this strategy in your organisation. 5. What potential benefits or improvements do you expect to achieve by implementing this strategy? 6. Are there any potential challenges or risks associated with implementing this strategy, and how would you mitigate them? (consider using tables similar to those illustrated below) Scenario

Challenge

Mitigation

Scenario

Challenge

Mitigation

Scenario

Challenge

Mitigation

Exercise 9: Reflection and Action Plan Instructions: Reflect on your learnings from this chapter and create an action plan to address obstacles and enhance data value in your organisation. 1. Summarise the key insights and strategies you have learned from this chapter.

2. Identify the top three obstacles or challenges your organisation is currently facing related to data value (consider using a table similar to the one illustrated below). 3. Choose one strategy or approach discussed 4. Outline the specific steps and actions you in this chapter that you believe would be most will take to implement this strategy in your effective in addressing these obstacles. organisation.

5. Define measurable goals and milestones to track the progress and success of your action plan. 6. Assign responsibilities and designate a timeline for each action step (consider using a table similar to the one illustrated below). Action Step

Assigned Responsibility

Designated Timeline

7. List potential risks or challenges that may arise during the implementation and develop contingency plans. 8. Reflect on how this action plan aligns with your organisation's overall goals and vision for data value. Note: This workbook chapter is designed to enhance your understanding of the chapter and encourage active engagement with the concepts and strategies discussed. Remember that the exercises aim to deepen your understanding of the concepts and examples presented, so, take your time to reflect on the questions and provide thoughtful responses.

Chapter EIGHT Obstacles of the Present

Exercise 1: Skills Matrix Analysis Objective: Analyse the skills matrix of your organisation or a specific team and identify areas of disconnect between the skills currently available and the skills required to achieve data value. Instructions: 1. Obtain or create a skills matrix (consider using a table similar to the one illustrated below) for a team or department in your organisation. Skills Employee

Skill One

Skill Two

Skill Two

Skill Four

Skill Five

Skill Six

Skill Seven

Team Member A Team Member B Team Member C Team Member D Team Member E Total

2. Identify the skills listed in the matrix and their corresponding proficiency levels for each team member. 3. Compare the skills listed with the skills needed to drive data value in your organisation. 4. Identify any gaps or disconnects between the current skills and the skills required for data value initiatives (consider using a table similar to the one illustrated below). Initiative

Current Skills

Required Skills

5. Reflect on the implications of these gaps and consider potential strategies for bridging them. 6. Discuss your findings with relevant stakeholders and propose actions to address the skill gaps.

Exercise 2: Leadership Competency Assessment Objective: Evaluate the essential competencies required for leadership in driving data value and assess the current leadership capabilities within your organisation. Instructions: 1. Identify the essential competencies for leadership in driving data value based on the insights provided in the chapter. 2. Assess the current leadership capabilities within your organisation or a specific team. 3. Evaluate each leader's proficiency in the identified competencies using a scale (e.g., 1-5, where 1 is low proficiency and 5 is high proficiency). 4. Reflect on the strengths and weaknesses of the current leadership capabilities in relation to driving data value (consider using a table similar to the one illustrated below). Strengths

Weaknesses

5. Identify areas where leadership development or support is needed to enhance the competencies required for driving data value. 6. Develop a plan for leadership development or support, such as coaching, mentoring, or training, tailored to address the identified gaps.

Exercise 3: Task Distribution Analysis Objective: Analyse the distribution of tasks and responsibilities within your organisation to identify any imbalances and potential obstacles to driving data value. Instructions: 1. Identify a team or department within your organisation that is involved in data-related initiatives. 2. Review the distribution of tasks and responsibilities among team members (consider using a table similar to the one illustrated below). Tasks

Responsibilities

3. Assess whether there is an uneven distribution of tasks, with some individuals shouldering a disproportionate amount of work (consider using a table similar to the one illustrated below). Tasks

Responsibilities

4. Consider the implications of this imbalance on driving data value, such as potential burnout, decreased productivity, or limited capacity for strategic initiatives. 5. Explore strategies to empower other team members and distribute tasks more evenly, ensuring that capable individuals are not overloaded. 6. Develop a plan to implement changes in task distribution, including delegating responsibilities, providing additional support or resources, or implementing a more structured approach to workload management.

Exercise 4: Decision Leadership Assessment Objective: Evaluate the effectiveness of decision leadership within your organisation and identify any obstacles that hinder the decision-making process. Instructions: 1. Assess the decision-making process within your organisation, focusing on data-related initiatives. 2. Evaluate the efficiency and effectiveness of decision-making in driving data value, considering factors such as timeliness, alignment with strategic goals, and stakeholder involvement. 3. Identify any obstacles or challenges that hamper the decision-making process, such as lack of ownership, accountability, or clear guidelines. 4. Reflect on the impact of these obstacles on driving data value and consider potential solutions or improvements. 5. Propose strategies to enhance decision leadership, such as establishing clear roles and responsibilities, improving communication and collaboration among stakeholders, or implementing decision-making frameworks or protocols. 6. Discuss your assessment findings and proposed solutions with relevant stakeholders to gain their input and support for implementing changes.

Exercise 5: Reflection on Data Strategy

Instructions: Reflect on the importance of having a clear data strategy for driving data value transformations. Consider the challenges faced by organisations that lack a defined data strategy. Answer the following questions: 1. Why is it important for organisations to have a clear data strategy? 2. What are the practical challenges that organisations face when implementing data strategies? 3. How can a clear data strategy help in overcoming these challenges? 4. Reflect on a specific organisation or industry you are familiar with. How do you think a clear data strategy could benefit them?

Exercise 6: Responsible Leadership for High-Performing Teams Instructions: Review the list of 44 strategies for leading high-performing teams provided in the chapter. Select five strategies that resonate with you the most and explain why. Consider how these strategies can enhance teamwork, reduce stress, and promote a culture of responsible leadership (consider using a table similar to the one illustrated below). Strategy

Resonance

Exercise 7: Overcoming Complexity and Complications Instructions: Consider the impact of complexity and complications on driving data value transformations. Answer the following questions: 1. What are some common challenges organisations face when dealing with complexity and complications in data-related initiatives? 2. Review the suggested solutions for addressing complexity, such as federated learning, federated enrichment, and federated monitoring. Explain how these approaches can help overcome complexity and achieve data value transformations. 3. How can organisations ensure a direct line of sight between their actions and the value created? Why is this important for data science and advanced analytics teams?

Exercise 8: Seeing Beyond the Challenges Instructions: Reflect on the concept of looking beyond immediate challenges and focusing on long-term goals. Answer the following questions: 1. Why is it important for organisations to look beyond immediate challenges when driving data value transformations? 2. Consider the three dimensions for exploring complexities and complications: people, design, and life-cycle. Select one dimension and explain how it can be assessed and aligned with organisational goals. 3. Provide an example of a situation where an organisation's focus on immediate challenges hindered its ability to achieve long-term data value. How could they have approached the situation differently?

Exercise 9: Fixing a Flying Plane - Transition and Migration Instructions: Think about the challenges involved in implementing data transformation projects while keeping the business operations running smoothly. Answer the following questions: 1. Why is it important to implement data transformation projects without disrupting day-to-day operations? 2. Explain the concept of "fixing a plane while it's flying" and how it relates to data transformation projects. 3. Consider the suggested approach of advancing online objectives while delivering quality service to other customers. How can organisations achieve this balance effectively?

4. Reflect on a situation where you have experienced or observed a data transformation project. How well was the transition and migration handled? What could have been done differently to ensure a smoother process?

Exercise 10: Reflection on Growth Limiting Factors Take a few moments to reflect on the two main limiting factors that can hinder data value when growth outpaces capabilities: growth delivery and operational growth. Consider the following questions: 1. How can growth delivery affect the ability to drive data value? 2. What challenges might arise when trying to develop multiple "change" assets in parallel? 3. How can operational growth impact the activation of changes? 4. What limitations might arise when trying to activate multiple changes simultaneously? 5. What potential consequences can unsupported growth have on a company?

Exercise 11: Analysing Obstacles for Future Growth Review the important considerations outlined in the box section to prevent unsupported growth from becoming an obstacle. Answer the following questions based on your understanding: 1. Why is it crucial to assess the implications of growth on existing processes and jobs? 2. What risks might arise when growth outpaces capabilities, and how can they be addressed? 3. How can having adequate mitigation strategies help prevent unsupported growth from becoming an obstacle? 4. What role does understanding the potential consequences play in managing growth effectively?

Exercise 12: Critical Steps for Ensuring the "Right" Speed of Execution Carefully read the five critical steps provided for addressing challenges related to speed in data transformation projects. Then, complete the following tasks: 1. Identify examples of data value engagement expertise, both internal and external, that can contribute to achieving the "right" speed of execution. 2. Explain the importance of creating a roadmap that reflects cross-organizational data collection in ensuring efficient execution. 3. Discuss why it is crucial to test the set ambition against existing capacity and capabilities. 4. Reflect on the significance of delivering optimal options and recommendations in driving data value.

Exercise 13: Reducing Defensiveness for Collaborative Efforts Consider the three steps outlined for reducing defensiveness in data value transformations. Then, complete the following exercises: 1. Identify how independence can help overcome defensiveness in teams. 2. Discuss the importance of firm-wide data collection in fostering collaboration. 3. Explain why making methodologies and outcomes visible to stakeholders can help reduce defensiveness.

Exercise 14: Addressing Budgetary and Funding Issues Reflect on the challenges associated with budgetary and funding issues in data transformation projects. Answer the following questions: 1. Why is it essential to balance the need to invest with the need to demonstrate tangible positive returns? 2. How can focusing on the benefits of a data value strategy help resolve budgetary challenges? 3. Discuss the role of open-mindedness and bringing together the best ideas in overcoming funding obstacles.

Exercise 15: Utilising the VOV Model for Commercial Value Connectivity Review the VOV model and its components: Vision, Obstacles, and Value. Complete the following tasks: 1. Explain how the VOV model can help ensure commercial value connectivity in data value transformations. 2. Discuss the importance of setting clear goals and value-centric ideation (Vision) in driving successful outcomes. 3. Identify potential obstacles that may impede data value realisation and how they can be addressed. 4. Reflect on the significance of associating tangible value with a vision or other components (Value).

Exercise 16: Understanding Minimum and Maximum Viability Consider the concepts of minimum viability and maximum viability in data value propositions.

Answer the following questions: 1. Define the point of minimum viability and its importance in data value realisation. 2. Explain the concept of the law of increasing returns and its relevance to the point of minimum viability. 3. Discuss the significance of the point of maximum viability and how it differs from the point of minimum viability. 4. Define the law of decreasing returns and its relationship to the point of maximum viability. Note: The exercises provided are designed to enhance your understanding of the obstacles discussed in this chapter. Take your time to reflect on each exercise and provide thoughtful responses. You should additionally find it helpful to adapt the exercises as needed to suit the specific context and needs of your organisation.

PART THREE VALUE: IDENTIFYING, CAPTURING AND COMMUNICATING DATA VALUE

Workbook

Chapter NINE Capturing Data Value Propositions

Exercise 1: Understanding Data Value Propositions 1. Define the VOV model and its three core features: capture, case, and communicate. 2. Explain the significance of understanding data as a business-value-optimization tool. 3. Discuss the importance of change management in realising tangible value from data. 4. Describe the key inputs for capturing data value: sources and phases. 5. Identify and explain the three fundamental sources of data value: cost reduction, business development, and risk mitigation.

Exercise 2: Bottom-Line Value (BLV) Optimization 1. Define the term "bottom line" and its significance across organisations. 2. Explain the relationship between bottom line, revenue, and costs (consider using a table similar to the one illustrated below). Bottom Line

Revenue

Costs

3. Discuss the inverse relationship between costs and bottom-line performance (consider using a table similar to the one illustrated below). Costs

Bottom-Line

4. Explain the direct relationship between revenue and bottom-line performance (consider using a table similar to the one illustrated below). Revenue

Bottom-Line

5. Identify and describe the three categories of bottom-line optimization: waste, abuse, and fraud (WAF).

Exercise 3: Top-Line Value (TLV) Optimization 1. Differentiate between top line and bottom line in terms of their representation on an organisation's income statement. 2. Explain the importance of revenue and costs in determining the success of a company. 3. Identify key avenues for enhancing data value through business development optimization.

Exercise 4: Cost Avoidance Value (CAV) 1. Discuss the significance of cost avoidance as a source of data value. 2. Provide examples of initiatives and interventions that can help mitigate the risk of incurring unnecessary costs. 3. Explain how effective regulatory compliance can contribute to cost avoidance value.

Exercise 5: Understanding Data Costs 1. Differentiate between "run the business" (RTB) operations and "change the business" (CTB) delivery in the context of data costs (consider using a table similar to the one illustrated below). RTB

CTB

2. Discuss the importance of considering RTB costs in the overall data value equation.

Exercise 6: A Business Stakeholder Perspective of Data

Value Capture 1. Analyse the perspectives of different team members on data value in a given scenario. 2. Discuss the challenges and priorities identified by the CEO, technology team member, and chief of staff. 3. Identify the key areas of concern related to data value, such as scalability, data accessibility, and data quality. 4. Propose potential solutions or strategies to address the identified challenges and enhance data value capture.

Exercise 7: RTB and CTB Optimization 1. Discuss the impact of explosive growth on an organisation's ability to capture data value. 2. Identify the limitations and challenges associated with data access, reporting, and analysis in a high-growth environment. 3. Explain the importance of data quality and automation in improving outcomes and efficiency. 4. Propose strategies or initiatives for optimising RTB and CTB operations to enhance data value capture.

Exercise 8: Reflecting on Data Value Propositions Instructions: Read the chapter carefully and reflect on the different data value propositions discussed by various stakeholders. Consider the potential benefits and challenges associated with each proposition. Write a short paragraph for each of the following questions: 1. What are the potential benefits of having live data for fraud prevention? 2. How can data help in future-proofing the business? 3. What are the challenges in assigning dollar values to improvements in data capabilities? 4. Why is it important to prioritise spending and adjust to changing conditions when investing in data? 5. What obstacles can arise when there is a lack of cooperation and communication between teams working on data solutions?

Exercise 9: Applying Data Strategies Instructions: Imagine you are part of the organisation discussed in the chapter and you have been tasked with developing a data strategy. Answer the following questions based on the information provided in the chapter:

1. What outcomes and value creation do you think are desirable for your team, your business stream, and the whole company? 2. How would you prioritise spending and adjust to changing conditions when investing in data? 3. How would you address the lack of cooperation and communication between teams working on data solutions?

Exercise 10: Evaluating Data Analytics Initiatives Instructions: Consider the challenges faced by the organisation in implementing data analytics capabilities. Answer the following questions: 1. What challenges did the data team face in developing the BI solution? 2. How can the organisation improve cooperation and communication between the data team and other departments? 3. What steps can be taken to ensure successful implementation and utilisation of data analytics initiatives?

Exercise 11: Case Study Analysis Instructions: Research and analyse a case study of an organisation that successfully implemented a data analytics strategy to achieve positive outcomes. Answer the following questions based on the case study: 1. What were the specific data value propositions identified by the organisation? 2. How did the organisation overcome challenges in assigning value to data capabilities? 3. What strategies did the organisation use to prioritise spending and adjust to changing conditions? 4. How did the organisation ensure cooperation and communication between teams working on data solutions? 5. What lessons can be learned from this case study and applied to the organisation discussed in the chapter? Note: The exercises provided above are meant to enhance understanding and stimulate critical thinking about the concepts and scenarios presented in the chapter. The answers to the exercises will vary based on individual interpretations and analysis.

Chapter TEN Measuring Data Value for Business Case and Operational Assurance

Exercise 1: Macro vs. Micro Data Value Measurement 1. Write a short paragraph discussing the challenges associated with macro-level data value measurements and their implications for businesses and data professionals. 2. Identify two tangible research studies or reports that have estimated the value of data at the macro level. Summarise their findings and discuss the potential impact on the global economy. 3. Consider the limitations of using sophisticated mathematical formulas to measure the intrinsic value of data at the micro level. Discuss why the utility of data attributes and their specific use cases should be taken into account when assigning value.

Exercise 2: Understanding Business Stakeholder Perspectives 1. Imagine you are a data professional working with a large organisation. Identify three different stakeholders within the organisation who might have varying perspectives on data value measurement. Describe each stakeholder's role and explain why understanding their perspective is crucial for developing a comprehensive data value strategy (consider using a table similar to the one illustrated below). Stakeholder

Role Description

Criticality Explanation

2. Conduct a short interview with a fictional stakeholder from one of the identified roles. Ask them about their challenges, priorities, and expectations related to data value measurement. Summarise their responses and discuss the potential impact on the organisation's business case (consider using a table similar to the one illustrated below).

Stakeholder Description

Response Summary

Potential Impact

Exercise 3: Assessing Data Value in a Multifaceted Operation 1. Analyse the case study of the marketing manager in a retail business operating across multiple countries and channels. Identify three key challenges they face in measuring data value for marketing strategies. 2. Develop a list of potential data sources and metrics that could help address their challenges. Explain how each data source or metric could contribute to enhancing marketing outcomes and customer understanding. 3. Consider the implications of capturing customer data from in-person interactions (e.g., street vendors) and offline activities. Discuss the benefits and limitations of incorporating this data into the organisation's overall data value strategy.

Exercise 4: Articulating Data Value Propositions 1. Identify three potential business value propositions that could be derived from better understanding customer data in the organisation. Explain how each proposition aligns with the organisation's goals and could contribute to revenue growth or cost optimization (consider using a table similar to the one illustrated below). Value Proposition

Aligned Goals

Value Contribution

2. Develop a persuasive pitch to convince stakeholders (e.g., senior management, finance team) of the value of investing in data capture and analysis. Include key arguments, supporting evidence, and potential financial impacts to strengthen your case.

Exercise 5: Addressing Cost-Avoidance through Data Value 1. Analyse the challenges faced by the finance team in terms of data accuracy and timeliness. Discuss the potential consequences of these challenges on financial analysis and decisionmaking. 2. Identify two specific areas where data value measurement can help address cybersecurity and data privacy challenges within the organisation. Explain how improving data quality, accessibility, or governance can contribute to cost-avoidance and risk mitigation (consider using a table similar to the one illustrated below). Initiative

Contribution

3. Develop a roadmap or action plan outlining steps to improve data consolidation, accuracy, and availability for financial analysis purposes. Include recommendations for implementing data quality controls, enhancing reporting processes, and fostering collaboration between different departments.

Exercise 6: Macro-Level Data Value Measurement Instructions: Read the following statements and determine whether they are true or false. 1. Measuring data value at the macro level involves considering the value of data on a global scale. 2. Some researchers estimate that the value of data is as high as 5% of the global gross domestic product (GDP). 3. Big-picture proclamations about the value of data are often frustrating for data professionals and stakeholders. 4. Sophisticated mathematical formulae can accurately determine the intrinsic value of data. 5. Micro-focused approaches to data value measurement take into account the utility of data attributes.

Exercise 7: Generating a Data Value Business Case Instructions: Fill in the blanks with the appropriate words or phrases from the chapter to complete the sentences. 1. The organisation's marketing department operates in multiple countries and uses a hybrid platform that combines __________ and __________ agents. 2. The organisation's marketing team works closely with __________ and __________ teams. 3. The organisation's CSR initiatives are gaining traction, especially in __________. 4. The marketing team has a global approach but also allows __________ teams to determine their own marketing activities. 5. A senior stakeholder wants to increase revenue by __________ through the ability to capture and analyse customer data. 6. Understanding the customer journey is important for __________ and __________ strategies.

Exercise 8: Reflection and Application Instructions: Reflect on the chapter and answer the following questions. 1. What are the main challenges and considerations when measuring data value for business cases and operational assurance? 2. How can understanding the organisation's vision and value proposition help in generating a data value business case? 3. What potential benefits can come from enhancing marketing outcomes through data value measurement? 4. How can data value measurement help address cybersecurity and data privacy challenges? 5. How would you apply the concepts discussed in this chapter to a real-life business scenario? Provide an example.

Exercise 9: Macro and Micro Approaches to Data Value Measurement Instructions: 1. Research and identify at least three different macro approaches to measuring data value. 2. Summarise each approach in a paragraph, highlighting its key principles and implications (consider using a table similar to the one illustrated below). Approaches

Principles

Implications

3. Reflect on the limitations and challenges associated with macro approaches to data value measurement. 4. Research and identify at least three different micro approaches to measuring data value. 5. Summarise each approach in a paragraph, emphasising its focus and potential benefits (consider using a table similar to the one illustrated below). Approach

Focus

Benefits

6. Discuss the challenges and limitations of micro approaches to data value measurement. 7. Compare and contrast macro and micro approaches, highlighting their differences and similarities.

8. Reflect on the implications of choosing either a macro or micro approach for measuring data value in a business case (consider using a table similar to the one illustrated below). Macro

Micro

Exercise 10: Stakeholder Perspectives on Data Value Measurement Instructions: 1. Imagine you are a stakeholder in a company and have been asked to provide your perspective on data value measurement. 2. Identify and describe your role in the organisation (e.g., marketing manager, finance director, operations supervisor). 3. Reflect on the specific challenges and obstacles you face in measuring and communicating data value in your role. 4. Consider the implications of data value measurement for your department's goals and objectives. 5. Outline the key factors that you believe should be considered when measuring data value in your department. 6. Discuss the potential benefits and risks associated with accurate data value measurement in your role. 7. Reflect on the importance of collaboration and communication with other stakeholders in ensuring effective data value measurement and realisation. 8. Share your perspective on how data value measurement can contribute to the overall success and growth of the organisation.

Exercise 11: Generating a Data Value Business Case Instructions: 1. Choose a specific business scenario or project in which data value needs to be measured and justified. 2. Identify the key stakeholders involved in the project and their respective roles (consider using a table similar to the one illustrated below). Stakeholders

Roles

3. Outline the organisation's vision and value proposition and discuss how data value measurement aligns with them. 4. Analyse the obstacles and challenges that need to be addressed in order to make a compelling business case for investing in data. 5. Propose suitable interventions and strategic recommendations based on your understanding of the organisation's past and current positions (consider using a table similar to the one illustrated below). Past

Current

6. Discuss the potential impact of data value measurement on the organisation's scalability, costjustification, and overall success. 7. Outline the steps and methodologies that can be employed to measure data value in the specific business scenario or project. 8. Assess the potential benefits, risks, and limitations associated with data value measurement in the context of the chosen scenario. 9. Present your data value business case, including the estimated value and potential return on investment, to the stakeholders and justify your recommendations.

Exercise 12: Data Value for Different Departments Instructions: 1. Choose three different departments or functions within an organisation (e.g., marketing, finance, operations). 2. Identify the unique challenges and priorities each department faces in measuring and leveraging data value. 3. Discuss the specific data sources, metrics, and methodologies relevant to each department's goals and objectives (consider using a table similar to the one illustrated below). Department or Function

Relevant Specifics

Goals and Objectives

4. Analyse the potential benefits and implications of data value measurement for each department's decision-making processes (consider using a table similar to the one illustrated below). Department

Process

Benefits and Implications

5. Identify the key stakeholders involved in data value measurement within each department and discuss their roles and responsibilities (consider using a table similar to the one illustrated below). Department

Stakeholders

Responsibilities

6. Explore potential collaboration opportunities and synergies between departments in order to enhance data value realisation. 7. Discuss the importance of data privacy, cybersecurity, and compliance considerations for each department when measuring data value. 8. Develop recommendations for each department on how to effectively measure, communicate, and leverage data value to achieve their respective goals. Note: The exercises provided are designed to enhance understanding and promote critical thinking about the concepts discussed in Chapter 10. They can be completed individually or in groups, and the level of detail and analysis can be adjusted based on your particular context and on your prior familiarity with the topics discussed.

Chapter ELEVEN Understanding the Data Value Measurement Lifecycle

Introduction: The data value measurement lifecycle is essential to understand the value that can be derived from any data-driven initiative. The measurement process involves three critical milestones: estimation, delivery, and operations. The challenges associated with each of these milestones can make it difficult to capture the full value of data. The triple BAT model is a useful framework for measuring data value across three dimensions: Can it be achieved, has it been achieved, and does it continue to be achieved? This workbook chapter includes exercises to help understand the key concepts presented in the chapter.

Exercise 1: Estimation Phase The estimation phase of the data value measurement lifecycle aims to provide credible projections of the potential value that can be derived from a specific data-driven initiative. The challenges associated with this phase include ascribing value to raw data items, value variability, and separating required inputs for delivering data value. To understand these challenges better, complete the following exercise: 1. Pick a data-driven initiative that your organisation has undertaken or is planning to undertake. 2. List the raw data items involved in the initiative. 3. Ascribe value to each data item, taking into consideration the variables that may impact its value (consider using a table similar to the one illustrated below). Data Item

Ascribed Value

Impact Variables

4. List the variables that may affect the value of the data items (consider using a table similar to

the one illustrated below). Data Item

Impact Variables

Value Impact

5. Identify the costs and other required inputs for delivering data value.

Exercise 2: Delivery Phase The delivery phase involves testing the hypothesis proposed during the estimation phase. The challenges associated with this phase include defining a universal set of contextual test cases, determining a credible outcome from testing, separating required inputs and observed outcomes, and modelling complex actions and reactions. To understand these challenges better, complete the following exercise: 1. Think of a data-driven initiative that your organisation has delivered recently. 2. Identify the universal set of contextual test cases that were used to test the initiative. 3. Determine whether the test cases covered the full range of proofs required to quantify data value. 4. Consider the variables that may impact the value of the initiative over time. 5. Identify the actions and reactions that the initiative could have initiated, and discuss how they were modelled during the testing phase.

Exercise 3: Operations Phase The operations phase is the most significant stage of the data value lifecycle. It commences at the point of deployment and continues throughout the life of the data value solution. The challenges associated with this phase include isolating factors that influence value, determining the contribution of data to the overall value proposition, evaluating marginal value, and the lack of awareness of continuous tracking. To understand these challenges better, complete the following exercise: 1. Identify a data-driven initiative that has been in operation for more than six months. 2. List the factors that influence the value of the initiative. 3. Determine the contribution of data to the overall value proposition. 4. Evaluate the marginal value of the data-driven initiative. 5. Discuss the importance of continuous tracking of data value over time and ways to achieve it.

Exercise 4: The Triple BAT Model for Data Value Measurement Instructions: 1. Describe the Triple BAT Model and its significance in evaluating data value. 2. Compare and contrast the three phases of the model: Baseline, Alternative, and Theory, Testing, and Tracking (consider using tables similar to those illustrated below). Baseline Theory

Testing

Tracking

Alternative Theory

Testing

Tracking

3. Develop a hypothetical scenario where the Triple BAT Model could be applied, and outline the steps involved in each phase.

Exercise 5: The Application of the Triple BAT Model The triple BAT model is a framework that helps assess the value of a data-driven initiative across three dimensions: Can it be achieved, has it been achieved, and does it continue to be achieved? To understand this model better, complete the following exercise: 1. Pick a data-driven initiative that your organisation has undertaken. 2. Assess whether the initiative can be achieved, has been achieved, and continues to be achieved. 3. Determine the factors that affect the achievement of each dimension (consider using a table similar to the one illustrated below). Dimension

Factor

Description

4. Discuss ways to enhance the achievement of each dimension. 5. Consider the triple BAT model when planning future data-driven initiatives.

Exercise 6: Milestones of the Data Value Measurement Lifecycle Instructions: 1. List and briefly describe the three essential milestones of the Data Value Measurement Lifecycle. 2. Reflect on each milestone and provide an example scenario that illustrates its relevance (consider using a table similar to the one illustrated below). Milestone

Relevant Scenario

3. Discuss the challenges associated with each milestone and brainstorm potential solutions.

Exercise 7: Challenges in Data Value Estimation Instructions: 1. Identify and explain the challenges associated with data value estimation during the theory phase. 2. Analyse each challenge and propose strategies or techniques to overcome them (consider using a table similar to the one illustrated below). Challenges

Strategies or Techniques

3. Provide real-life examples or case studies that demonstrate the challenges and potential solutions.

Exercise 8: Challenges in Data Value Validation Instructions: 1. Outline the challenges faced during data value validation in the testing phase. 2. Explore the implications of these challenges and their impact on the accuracy of data value measurement (consider using a table similar to the one illustrated below).

Challenges

Implications and Impacts

3. Develop strategies or approaches to address these challenges effectively.

Exercise 9: Challenges in Data Value Monitoring Instructions: 1. Examine the challenges related to data value monitoring in the tracking phase. 2. Discuss the potential consequences of these challenges on the long-term assessment of data value (consider using a table similar to the one illustrated below). Challenges

Consequences

3. Suggest practical methodologies or tools that can aid in overcoming the challenges. Conclusion: In this workbook chapter, we have delved into the Data Value Measurement Lifecycle, exploring its milestones and the challenges associated with each phase. By completing the exercises, you have gained a deeper understanding of the concepts discussed in Chapter 11. The Triple BAT Model has been introduced as a framework for data value measurement, and you have applied it to a hypothetical scenario. Use this knowledge to enhance your comprehension of data value measurement and its practical implementation in real-world scenarios.

Chapter TWELVE Enhancing Understanding of Data Value Profits and Losses

Exercise 1: Vision and Value Proposition Instructions: Reflect on your organisation or a hypothetical business scenario. Identify a tangible use case or idea related to data that the organisation needs to achieve. Then, define the broader ambitions of the business that this use case or idea can enable. Finally, explain how this use case aligns with the organisation's value proposition (consider using tables similar to those illustrated below). Use Case

Broader Ambition

Value Proposition

Use Case

Broader Ambition

Value Proposition

Use Case

Broader Ambition

Value Proposition

Exercise 2: Understanding the Impact of Returns Instructions: Imagine you work for a retail company that is facing significant costs due to product returns. Identify the various costs associated with returns, such as shipping and restocking costs, damages, merchandise carrying cost, store associate time, non-congruent inventory, and store transfer costs. Reflect on the challenges faced by the company and the potential negative impact on its profitability. Discuss possible strategies to address these challenges and mitigate the negative impact (consider using a table similar to the one illustrated below).

Challenges

Impacts

Strategies and Mitigations

Exercise 3: Estimating Value Returns on Investment Instructions: Consider the retail company mentioned in Exercise 2. Suppose you are tasked with establishing a predictive range of potential value returns on investment for a proposed AI solution. Collect relevant data, including internally available experience data and externally accessible base rate information. Use this data to approximate the cost of returns and triangulate a set of targeted intervention strategies. Finally, present a predictive range of potential value returns on investment based on the analysis.

Exercise 4: Identifying Challenges for Data Value P&L Review the section on challenges for establishing a data value P&L and answer the following questions: 1. What is the first challenge for establishing a data value P&L? 2. How can costs be isolated in data value measures? 3. What is the second challenge for establishing a data value P&L? 4. Explain the challenge with resource ring-fencing for teams and technologies. 5. What is the third challenge for establishing a data value P&L? 6. Provide an example of opposing data value perspectives.

Exercise 5: Reflecting on the Challenges for a Data Value P&L Instructions: Analyse the challenges discussed in the chapter for establishing a data value profit and loss (P&L) account. Focus on the challenge of isolating costs, achieving resource ring-fencing, and understanding the variety of value perspectives. Reflect on real-life examples or hypothetical scenarios to illustrate these challenges. Discuss potential strategies or approaches to overcome these challenges.

Exercise 6: Simplifying Data Value Assessment Instructions:

Choose a data quality improvement initiative within your organisation or a hypothetical scenario. Apply the VoV (Vision, Obstacles, Value) model to define the vision, obstacles, and value associated with the initiative. Identify any challenges or difficulties in quantifying the benefits and setting tangible evaluation criteria. Propose ways to simplify the assessment of data value and establish more tangible measures for the initiative (consider using a table similar to the one illustrated below). Vision

Obstacles

Value

Exercise 7: Increasing Resource Autonomy Instructions: Reflect on the various inputs required for driving data value within your organisation or a hypothetical scenario. Identify the skills, talents, capabilities, technologies, finances, legal contracts, and other resources involved. Discuss the importance of resource autonomy for enabling effective data value delivery. Consider any obstacles or dependencies that may limit resource autonomy and propose strategies to increase it (consider using tables similar to those illustrated below). Resources

Obstacles

Strategies

Resources

Obstacles

Strategies

Resources

Obstacles

Strategies

Exercise 8: Reducing Interdependencies Instructions: Explore the interdependencies between different processes or initiatives related to data value within your organisation or a hypothetical scenario. Discuss the challenges and potential negative implications of these interdependencies. Consider the impact on systemic flow, individual resilience, and systemic sustainability. Propose strategies or approaches to reduce interdependencies and enhance the overall effectiveness of data value initiatives (consider using tables similar to those illustrated below). Interdependencies

Impacts

Strategies

Interdependencies

Impacts

Strategies

Interdependencies

Impacts

Strategies

Exercise 9: Overcoming Traditional Obstacles with Silos Instructions: Analyse the traditional phenomena that present obstacles for an effective data value profit and loss (P&L) account, such as monolithic technologies, pooled teams, and traditional architecture and design standards. Reflect on the drawbacks and limitations of attempting to break down silos completely. Discuss the concept of microservices as a counterintuitive solution for reducing interactional friction and enabling effective data value delivery. Explain how microservices can address the identified obstacles.

Exercise 10: Case Study Analysis Read the case study presented in Chapter 12 and answer the following questions: 1. What was the problem faced by the retail company in the case study? 2. Why did the company want to build a predictive AI solution? 3. What challenges did they encounter during the project? 4. What was the ultimate solution to the problem?

Exercise 11: Essential Preconditions for a Data Value P&L Read the section on essential preconditions for a data value P&L and answer the following questions: 1. Why is simplifying data value assessment important? 2. What does resource autonomy refer to? 3. Why is reducing interdependencies crucial for establishing an effective data value P&L? 4. Identify the three traditional phenomena that can obstruct an effective data value P&L. 5. Explain why creating silos can be a solution to overcome these obstacles.

Exercise 12: Reflection and Application

Think about a data-related project or initiative you have been involved in or are currently working on. Reflect on the challenges and preconditions discussed in this chapter and apply them to your project: 1. Identify the challenges you faced or are currently facing in establishing the data value. 2. Consider how the preconditions discussed in this chapter could have helped address those challenges. 3. Brainstorm potential solutions or adjustments you could make to improve the data value assessment and establish a data value P&L.

Exercise 13: Group Discussion Form small groups and discuss the following questions: 1. Share your insights from the case study analysis exercise. How did the challenges in the case study resonate with your own experiences? 2. Discuss the importance of simplifying data value assessment and increasing resource autonomy. How can these preconditions contribute to the success of data-related initiatives? 3. Reflect on the idea of creating silos as a solution to overcome traditional obstacles. Do you agree or disagree with this approach? Discuss the potential benefits and drawbacks (consider using tables similar to those illustrated below). Benefits

Drawbacks

Benefits

Drawbacks

Benefits

Drawbacks

Exercise 14: Action Plan Develop an action plan for your own data-related project or initiative based on the challenges and preconditions discussed in this chapter: 1. Identify specific actions you can take to address the challenges you identified. 2. Determine how you can simplify the data value assessment and increase resource autonomy in your project. 3. Consider strategies for reducing interdependencies and creating a more effective data value P&L. 4. Set clear goals and milestones for implementing the action plan and monitor progress

regularly. Note: These exercises are designed to enhance your understanding of the concepts discussed in Chapter 12. You can work on them individually or as part of a group. Feel free to adapt the exercises to suit your specific needs and context.

Chapter THIRTEEN Presenting Data Value to Executives and the Board

Exercise 1: Presentation Structure Analysis Instructions: 1. Read the opening structure of the content presented in the chapter. 2. Take a blank sheet of paper and try to recreate the structure from memory. 3. Compare your recreated structure with the original one in the chapter. 4. Identify any missing or misplaced sections in your recreation. 5. Reflect on the importance of having a clear and organised structure in a presentation. 6. Write a brief paragraph describing the significance of a well-structured presentation in securing buy-in from executives and the board.

Exercise 2: Unexpected Findings Instructions: 1. Read the section about unexpected findings in the chapter. 2. Choose a recent project or initiative you have been involved in. 3. Identify one or two unexpected findings or surprises that you encountered during the project. 4. Write a short paragraph explaining the impact of these unexpected findings on the project. 5. Reflect on the importance of including unexpected surprises in a data value presentation. 6. Explain how unexpected findings can help capture the attention and interest of executives and the board.

Exercise 3: Identifying Obstacles

Instructions: 1. Read the section about capturing a range of obstacles in the chapter. 2. Think about a data value initiative or project that you are familiar with. 3. List three potential obstacles or challenges that could hinder the success of the initiative. 4. Reflect on how these obstacles align with the obstacles mentioned in the chapter. 5. Write a short paragraph explaining the significance of addressing and overcoming obstacles in driving data value. 6. Discuss strategies or approaches that can be employed to overcome the identified obstacles.

Exercise 4: Focusing on Ambitious Visions and Associated Value Instructions: 1. Read the section about focusing on ambitious visions and associated value in the chapter. 2. Choose a visionary goal or aspiration that you have encountered in your professional or personal life. 3. Using the three value dimensions (TLV, CAV, and BLV) mentioned in the chapter, analyse the potential value associated with the visionary goal (consider using a table similar to that illustrated below). TLV

CAV

BLV

4. Write a paragraph discussing the importance of aligning ambitious visions with tangible value propositions. 5. Reflect on how the understanding of value dimensions can help in effectively communicating the potential impact of a visionary goal to executives and the board.

Exercise 5: Transforming Data through Connected Organisational Silos Instructions: 1. Read the section about transforming data through connected organisational silos in the chapter. 2. Imagine a scenario where data silos exist within an organisation you are familiar with. 3. Identify three potential benefits of connecting these data silos and enabling collaboration among teams.

4. Write a short paragraph explaining the importance of breaking down data silos for driving data value. 5. Reflect on how implementing shared frameworks, architecture, and governance standards can contribute to the successful transformation of data within an organisation. 6. Discuss potential challenges or resistance that might arise when attempting to break down data silos and how they can be addressed.

Exercise 6: Role Analysis and Reflection Instructions: 1. Read the section on "The Role of a Chief Data Value Officer" in Chapter 13. 2. Take a few minutes to reflect on the concept of a Chief Data Value Officer and its potential impact on an organisation. 3. Answer the following questions: a. What are the key responsibilities of a Chief Data Value Officer? b. How can a Chief Data Value Officer drive the coordination of connected silos and add a central oversight perspective? c. Why is it important for this role to focus on generating value rather than just governance work? d. What are the potential benefits of having a Chief Data Value Officer in an organisation? e. If a Chief Data Value Officer cannot be secured, what are the alternative options to ensure the necessary capabilities are present in the organisation?

Exercise 7: Technology Platforms and Data Transformation Instructions: Consider the role of technology platforms in data transformation and their impact on driving data value. Answer the following questions: 1. Why is technology an important aspect of data transformation? 2. How can technology platforms support the work of an Enterprise Data Products organisation? 3. What is DevOps, and how can it contribute to data value transformation? 4. What challenges and limitations might arise from existing technology platforms? 5. How can organisations ensure that their technology stack is adaptable and scalable for driving data value?

Exercise 8: People and Culture in Data Transformation Instructions:

Reflect on the role of people in data transformation and the importance of addressing cultural shifts within an organization. Answer the following questions: 1. Why is the people component of data transformation often challenging? 2. How can a federated or decentralized structure support data transformation? 3. What are the challenges faced by data analysts in the organization mentioned in the chapter? 4. What are some potential strategies for defusing existing tensions and improving the atmosphere within a data-focused team? 5. How can organizations ensure that their data practices are more proactive, scalable, and integrated? 6. Why is it essential to address the people component alongside the technology aspect in data transformation? 7. What is the role of central hubs and decentralised spokes in a federated or decentralised structure? 8. How can defensiveness and tensions be defused when addressing people-related problems? 9. What is the importance of maintaining focus on the ultimate vision and ambition during data transformation?

Exercise 9: Decoupled Data Value Framework Instructions: Consider the concept of a decoupled data value framework and its benefits for organisations. Answer the following questions: 1. Why might teams or departments within an organisation compete for resources and hinder overall business performance? 2. What is the decoupled approach to data value, and how can it address separated or competing interests? 3. How can the cloud or cloud-like mindset support a decoupled data value framework? 4. What are the benefits of maintaining clear lines of sight within a decoupled data value framework? 5. How can organisations ensure that the various autonomous solutions fit into the overall data value strategy?

Exercise 10: Unpacking Data Value Presentation Slides Instructions: 1. Read the section on "Data Value Presentation Slides Unpacked" in Chapter 13. 2. Examine the recommended approach for structuring a presentation on data value. 3. Answer the following questions:

a. b. c. d.

What are the three pillars of the data value presentation? What are the key elements within each pillar, and what is their purpose? What is the purpose of the review pillar in a data value presentation? How can visualisations, quotes, and observations be utilised to convey outcomes and insights from the review phase? e. How does the transformation pillar relate to the recommended solutions for driving data value? f. What is the role of the roadmap pillar in a data value presentation, and what considerations should be addressed within it? Note: This accompanying workbook chapter aims to enhance understanding and engagement with the content for Presenting Data Value to Executives and the Board. It provides a set of exercises that encourage readers to reflect on key concepts, apply them to real-world scenarios, and reinforce their comprehension of the material.

CONCLUSION: BRINGING IT ALL TOGETHER Congratulations on completing the premium workbook for driving data value! You have taken a significant step towards unlocking the full potential of data in your organization and personal life. The tools and exercises in this workbook are designed to help you overcome the obstacles that stand between your data vision and value realization. As you reflect on your journey, remember that data is not just a commodity, but a powerful asset that can transform the way we live and work. By leveraging data, we can make better decisions, create new products and services, and drive innovation in our industries. However, we must also acknowledge that the road to data value is not always easy. It requires us to confront our biases, challenge our assumptions, and embrace new ways of thinking. It also requires us to invest in the right tools, technologies, and talent to turn data into actionable insights. But the rewards of data value are worth the effort. By unlocking the full potential of data, we can create a more prosperous, sustainable, and equitable future for ourselves and future generations. We can drive economic growth, improve public services, and tackle some of the world's most pressing challenges. So, as you continue your journey towards data value, remember that you are not alone. There is a growing community of data value advocates, champions, and experts who are committed to helping you succeed. Whether you need guidance, support, or inspiration, there are resources available to help you achieve your goals. In closing, I urge you to embrace the power of data and use it to create a brighter future for yourself, your organization, and the world. With the right mindset, tools, and support, you can turn data into a force for good and drive meaningful change in your industry and beyond.

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