PRAISE FOR DATA AND ANALYTICS STRATEGY FOR BUSINESS In my experience, taking people somewhere they’ve not been before requires leadership and trust, whether that’s climbing Everest or making a success of your business through the use of data and analytics. In Data and Analytics Strategy for Business, Simon Asplen- Taylor cuts through the jargon and provides a clear route for success. Kenton Cool, 15 successful Everest summits, one of the world’s greatest high-altitude mountaineers and leaders, expedition leader behind Sir Ranulph Fiennes’s north face of the Eiger ascent and Everest summit One of the benefits of going digital is that organizations can collect, review and analyse enormous quantities of data. Correctly interpreting this data provides the intelligence which enables a business to understand the consumer and marketplace in a completely new way.
Successful organizations require a clear data strategy and a disciplined set of operational processes. Simon Asplen-Taylor shows in practical detail how to make this happen in the real world. He demonstrates that data is key but reveals that an effective data officer never loses sight of the commercial application and human element of the intelligence created. Kevin Gaskell, serial entrepreneur, and former MD, Porsche GB and BMW GB Brilliant book! Genuinely the best and most readable book for existing and aspiring CDOs.
Every CEO should read the first chapter. Simon Asplen-Taylor has shared his significant expertise to create the go-to data guide for business and data leaders. Data and Analytics Strategy for Business uses examples from a wide range of organizations to explain why data can revolutionize a business. A genuinely good read, the book’s structure superbly guides the reader through all aspects of delivering a data and analytics strategy with vital tools and tips.
Whether your organization is struggling with trust in its reports or ready to launch the bots, this book is for you. Nina Monckton, Head of Data, Just Group plc Businesses operate in an increasingly complex and fast-moving environment, where making the right decision at the right time can mean the difference between winning and losing. Key to this is the successful management and use of data, underpinned by a robust data strategy. Data and Analytics Strategy for Business provides a structured approach to show how you can succeed – whether you are just embarking on your journey, part way through or just fine tuning.
The book is full of practical advice, anecdotes and experiences to help you win and not lose. Carl Bates, former Senior Partner, Data and Analytics, and leader of the Ventures practice, Deloitte One of the ways we can encourage women into data leadership roles is to provide the advice and methods to help remove barriers, while sponsoring next-generation leaders. In Data and Analytics Strategy for Business, Simon Asplen-Taylor does just that. He has shared his experiences and strategies for success to create a level playing field for all data leaders.
He also talks specifically about how to build and leverage the strengths of a diverse team. Roisin McCarthy, Founder, Women in Data As we strive to gain more value from our data assets, we create more risk, opportunity and motivation for breaches. Simon Asplen-Taylor’s new book, Data and Analytics Strategy for Business, provides amazing insight on how you can create more value in your organization’s data while also ensuring its security! Highly recommended. Ned Finn, CISO, Currys A very interesting book in such an important and contemporary area of knowledge and skills.
Data analytics is not just an area of knowledge that you need to learn more about, but it is considered to be a crucial skill that is required for every business. Therefore, this book is a great addition to the intellectual body of knowledge that can help students, especially those studying post experience executive programmes, such as MBAs and DBAs, to gain clear insights of the key elements of business analytics and to acquire the required set of skills to compete in the changing world of practice. Amir Michael, Professor of Accounting and Associate Dean (MBA, DBA), Durham University Business School, UK The world of a chief data officer (CDO) requires a full understanding of how a business operates, the sector it works within and the people involved. Simon Asplen-Taylor’s book gives a fine insight into the approaches, decisions and specific actions a CDO can use to bring real value to an organization and make it a critical part of business strategy.
Helen Crooks, Chief Data Officer, Ofgem Beyond his remarkable expertise across all the complexities of today’s data sphere, this book clearly demonstrates Simon Asplen-Taylor’s mastery of the art of data storytelling. Yet, what makes it even more compelling is that it is so helpfully structured in waves that align to the variety of levels across the entire data maturity spectrum – making it instantly transformative regardless of where an organization currently finds itself in its data journey. This book is an absolute essential for anyone who wants to successfully leverage the abundant value that can be derived from data and analytics. Edosa Odaro, Chief Data and Analytics Officer, Tawuniya, and author of Making Data Work Data and Analytics Strategy for Business Unlock data assets and increase innovation with a results-driven data strategy Simon Asplen-Taylor CONTENTS About the author Preface Acknowledgements PART ONE How data and analytics can help you grow your business 01 How can this book help you? Introduction CDOs hold the future of their organizations in their hands Task: Ten questions to ask about your business Are you worried? References 02 The business case for data Introduction The cost of doing nothing The value of data is only as good as the value of your business case for it Identifying the most pressing data problems Keep it simple Aligning the business case with the business References 03 Your data and analytics strategy Introduction What is a ‘data and analytics strategy’? Defining the CDO role Task: Setting priorities using the data periodic table Task: Using the data periodic table to design projects Five waves of transformation Value, build and improve Task: share the story References 04 A team game Introduction Cracking the code as a CDO The résumé problem Discovering problem-solvers Task: Recruiting for PQ and AQ Beware of ready-made data teams Diversity Data and analytics capability model References PART TWO Wave 1: Aspire 05 A quick win Introduction Anatomy of a quick win Task: Identifying the right project Make sure it’s a quick win for everyone Quick wins are not strategic wins References 06 Repeat and learn Introduction Why build a repeat-and-learn culture? Listen to what data is telling you Task: Define a data process Task: Develop a business change process Learning and innovating through experimentation References PART THREE Wave 2: Mature 07 Data governance Introduction What is data governance? The importance of accountability Data stewards, data owners and the data executive Task: Implementing data governance References Further reading 08 Data quality Introduction The risks of low-quality data The upside of high-quality data The four principles of data quality A data quality strategy Task: Setting a baseline and a target Task: Build a data quality team Task: Improving data quality in the short term Task: Improving data quality in the long term References Further reading 09 A single customer view Introduction What is a single customer view? Benefits of the SCV Other single views How do you build an SCV? Shadow data is the enemy of the SCV Ownership of the SCV References 10 Reports and dashboards Introduction Task: A report audit From static to dynamic decision support Task: Designing your dashboard Task: Dashboard implementation From reporting to insight Task: Information architecture Avoiding short-termism References Further reading 11 Data risk management and ethics Introduction Five pillars Task: Working with a regulator References PART FOUR Wave 3: Industrialize 12 Automation, automation, automation Introduction What can we automate? How much can we automate? Task: The business case for automation Task: Manageable automation projects Will I use a tool? References 13 Scaling up and scaling out Introduction From quick wins to big wins Be dull and repetitive Task: Choosing how and when to scale Use your resource multipliers Task: Implementing your hackathon The dividend from scaling References 14 Optimizing Introduction Best intentions are not optimal Task: Plot a path to optimization Task: Overcoming resistance The limits of automation This is a tipping point References PART FIVE Wave 4: Realize 15 The voice of the customer Introduction Hearing their voice Reasons to use other sources of data Understanding competitors Customer behaviour Social listening Task: Applying insights from social listening Task: Creating a reliable Net Promoter Score Integration Hear the bad news References 16 Maximizing data science Introduction Data science, not data magic How not to do data science Task: Integrating data science Task: Sustaining data science Embrace the potential for failure References 17 Sharing data with suppliers and customers Introduction Widespread exposure Exposing data to business partners and suppliers Blockchain in the supply chain Sharing improves markets Exposing data to customers Task: Prepare to share Exposure is inevitable, so do it your way References PART SIX Wave 5: Differentiate 18 From data-driven to AI-driven Introduction What does AI do? A hierarchy of data value The AI journey Task: Detection Task: Process automation Task: Improved clustering Data bias Task: Complex analysis and prediction The limits of AI as a guide or manager Task: Creating commitment to AI for independent real-time decision-making From data-driven transformation to AI-driven business References 19 Data products Introduction What is a data product? A data and analytics centre of excellence (CoE) Task: Creating a data CoE Three functions of research A continuous improvement life cycle References 20 Right leadership, right time Introduction Leading a sustainable data culture Which leader are you? Reference Epilogue: Data success Glossary Abbreviations and acronyms Index LIST OF FIGURES FIGURE 2.1 The investment in data and analytics leverages your other investments FIGURE 3.1 Data periodic table FIGURE 3.2 The five waves to data maturity FIGURE 3.3 Value, Build, Improve (VBI) content of each wave FIGURE 4.1 Data and analytics capability model Value, Build, Improve content of this wave FIGURE 5.1 Quick wins Value, Build, Improve content of this wave FIGURE 7.1 Data governance maturity model FIGURE 8.1 Data quality dimensions FIGURE 8.2 Customer data quality examples FIGURE 8.3 Data quality maturity model FIGURE 9.1 Overview of single customer view FIGURE 9.2 Single customer view benefits FIGURE 9.3 Shadow data resource FIGURE 10.1 Example dashboard – single customer view FIGURE 10.2 Reports and dashboards maturity model Value, Build, Improve content of this wave FIGURE 12.1 Automation maturity model Value, Build, Improve content of this wave FIGURE 16.1 The data science and AI method Value, Build, Improve content of this wave FIGURE 18.1 Value from data FIGURE 18.2 Data science and AI maturity model LIST OF TABLES TABLE 3.1 Revenue increase TABLE 3.2 Decreased costs TABLE 3.3 Reduced risk TABLE 3.6 Enabling capabilities ABOUT THE AUTHOR Simon Asplen-Taylor is one of the most experienced and successful data leaders in Europe, having served as chief data officer for several FTSE firms and led some of the largest data- led transformations in Europe.
He specializes in transforming businesses through the use of data, analytics and artificial intelligence whilst delivering significant upside in revenue, customer satisfaction, organization efficiency, cost reduction and reduced risk. He has a unique depth and breadth of data experience covering more than 30 years across many industries, having led the data capabilities at Lloyd’s of London, Tesco, Rackspace, Regus, BUPA, UBS and Bank of America Merrill Lynch and been a data consulting leader at IBM and Detica. His major achievements include: For a UK FTSE 100 financial services organization, delivering a $1 billion per annum contribution to the bottom line and also generating a significant uplift in share price. For a FTSE 250 business, delivering a 64 per cent improvement in the company’s margin through the use of data.
Troubleshooting and fixing the data capabilities of two organizations under direct threat from regulators.