Data and Analytics Strategy for Business: Unlock Data Assets and Increase Innovation with a ResultsDriven Data Strategy

  • 5h 20m
  • Simon Asplen-Taylor
  • Kogan Page
  • 2022

For many organizations data is a by-product, but for the smarter ones it is the heartbeat of their business. Most businesses have a wealth of data buried in their systems which, if used effectively, could increase revenue, reduce costs and risk and improve customer satisfaction and employee experience.

Beginning with how to choose projects which reflect your organization's goals and how to make the business case for investing in data, this book then takes the reader through the five 'waves' of organizational data maturity. It takes the reader from getting started on the data journey with some quick wins, to how data can help your business become a leading innovator which systematically outperforms competitors.

Data and Analytics Strategy for Business outlines how to build consistent, high-quality sources of data which will create business value and explores how automation, AI and machine learning can improve performance and decision making. Filled with real-world examples and case studies, this book is a stage-by-stage guide to designing and implementing a results-driven data strategy.

About the Author

Simon Asplen-Taylor is an experienced and successful data and analytics leader based in London, UK, having served as Chief Data Officer for multiple FTSE firms and led some of the largest data led transformations in Europe. He specialises in transforming business through the use of data, analytics and artificial intelligence and is currently leading the data transformation at Lloyd's of London. He was included in the dataIQ 100 Most Influential People in Data in both 2020 and 2021.

In this Book

  • How Can This Book Help You?
  • The Business Case for Data
  • Your Data and Analytics Strategy
  • A Team Game
  • A Quick Win
  • Repeat and Learn
  • Data Governance
  • Data Quality
  • A Single Customer View
  • Reports and Dashboards
  • Data Risk Management and Ethics
  • Automation, Automation, Automation
  • Scaling up and Scaling Out
  • Optimizing
  • The Voice of the Customer
  • Maximizing Data Science
  • Sharing Data with Suppliers and Customers
  • From Data-Driven to AI-Driven
  • Data Products
  • Right Leadership, Right Time
  • Epilogue—Data Success
  • Glossary
  • Abbreviations and Acronyms
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