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Data Strategy: How to Profit from a World of Big Data, Analytics and Artificial Intelligence 2nd Edition
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Data Strategy: How to Profit from a World of Big Data, Analytics and Artificial Intelligence 2nd Edition

$3.00

Format

PDF

Author(s)

Bernard Marr

Publisher

Kogan Page

ISBN-10

1398602604

ISBN-13

978-1398602601

Pages

272

Language

English

Edition

2nd edition | October 3, 2021

File Size

2 MB

Amazon Price

$76

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  • Description
Description

Data Strategy: How to Profit from a World of Big Data, Analytics and Artificial Intelligence 2nd Edition

Data Strategy: How to Profit from a World of Big Data Analytics and Artificial Intelligence 2nd Edition:

BRONZE RUNNER UP: Axiom Awards 2018 – Business Technology Category (1st edition)

Data is an integral strategic asset for all businesses. Learn how to leverage this data and generate valuable insights and true business value with bestselling author and data guru Bernard Marr.
Data has massive potential for all businesses when used correctly, from small organizations to tech giants and huge multinationals, but this resource is too often not fully utilized. Data Strategy is the must-read guide on how to create a robust, data-driven approach that will harness the power of data to revolutionize your business. Explaining how to collect, use and manage data, this book prepares any organization with the tools and strategies needed to thrive in the digital economy.
Now in its second edition, this bestselling title is fully updated with insights on understanding your customers and markets and how to provide them with intelligent services and products. With case studies and real-world examples throughout, Bernard Marr offers unrivalled expertise on how to gain the competitive advantage in a data-driven world.

Additional ISBNs:

∗ Print ISBN:1398602582, 978-1398602588, 9781398602588

∗ eText ISBN: 1398602590, 978-1398602595, 9781398602595

Data Strategy 1
Data Strategy2
Data Strategy3
Data Strategy4
Data Strategy5
Table of Contents

Contents

  1. Cover
  2. Halftitle
  3. Titlepage
  4. Contents
  5. About the author
  6. Acknowledgements
  7. 01 Introduction: Why every business is now a data business
  8. The astonishing growth of data, artificial intelligence and the Internet of Things
  9. A brave new (data-driven) world
  10. Are we nearing true artificial intelligence?
  11. The fourth industrial revolution – or Industry 4.0
  12. Other world-changing technologies
  13. Why every business must become a data business
  14. Notes
  15. 02 Use cases for data
  16. The six key use cases
  17. Key data use cases in practice
  18. Some industry-specific use cases
  19. How data is revolutionizing the world of business
  20. 03 Using data to improve your business decisions
  21. Setting out your key business questions
  22. Understanding and interpreting your data
  23. Curated data dashboards – the fine dining experience
  24. Self-service data exploration dashboards – the raclette grill experience
  25. Raclette grill analytics in the real world
  26. Data democratization and the role of the data translator
  27. Data storytelling
  28. The future of data visualization and storytelling
  29. 04 Using data to understand your customers
  30. Understanding customer analytics
  31. Types of customer data
  32. Pioneering the 360-degree customer view
  33. Customer analytics at Netflix
  34. Real-time personalization and micro-moments
  35. Disney’s Magic Bands
  36. How data enables customer-led design process
  37. The value of personal connections with customers
  38. 05 Using data to create more intelligent services
  39. Tech services
  40. New tricks for old dogs
  41. Smart services in banking, finance and insurance
  42. Smart services in healthcare, medicine and pharmaceuticals
  43. Smart services in fashion and clothing
  44. Robots as a service
  45. Smart education and training services
  46. AI itself as a service
  47. Every company is a tech company now
  48. Notes
  49. 06 Using data to make more intelligent products
  50. How smart products enable smart services
  51. Autonomous vehicles and mobility
  52. Intelligent home products
  53. Intelligent healthcare products
  54. Intelligent business, industry and manufacturing products
  55. Intelligent sports products
  56. Notes
  57. 07 Using data to improve your business processes
  58. Day-to-day processes and the digital twin
  59. Sales, marketing and customer service
  60. Distribution, warehousing and logistics
  61. Product development
  62. Manufacturing and production
  63. Support services – IT, finance and HR
  64. Notes
  65. 08 Monetizing your data
  66. Increasing the value of your organization
  67. When data itself is the core business asset
  68. When the value lies in a company’s ability to work with data
  69. Selling data to customers or interested parties
  70. Understanding the value of user-generated data
  71. Notes
  72. 09 Defining your data use cases
  73. Identifying use cases
  74. How does the use case link to a strategic goal?
  75. What is the objective of the use case?
  76. How will you measure the success of the use case?
  77. Who will be the use case owner?
  78. Who will be the data customers?
  79. What data do we need?
  80. What data governance issues need to be addressed?
  81. How do we analyse the data and turn it into insights?
  82. What are the technology requirements?
  83. What skills and capabilities do we need?
  84. What are the issues around implementation we need to be aware of?
  85. Pick the most effective use cases and use them to build a data strategy
  86. Constructing your data strategy
  87. 10 Sourcing and collecting data
  88. Understanding the different types of data
  89. Taking a look at newer types of data
  90. Gathering your internal data
  91. Accessing external data
  92. When the data you want doesn’t exist
  93. 11 Data governance, ethics and trust
  94. The ethics of AI
  95. Bias and the importance of ‘clean’ data
  96. Staying on the right side of the law
  97. Keeping your data safe
  98. Practising data governance
  99. Notes
  100. 12 Turning data into insights
  101. The evolution of analytics
  102. Advanced analytics – from science fiction to business fact
  103. Machine learning – the current cutting-edge in AI
  104. Supervised learning
  105. Unsupervised learning
  106. Reinforcement learning
  107. Deep learning and neural networks
  108. Generative adversarial networks (GANs)
  109. Advanced analytics in practice
  110. Types of analytics
  111. No-code and as-a-service AI infrastructure
  112. Notes
  113. 13 Creating a technology and data infrastructure
  114. Data, analytics and AI as a service
  115. Collecting data
  116. Storing data
  117. Public, private and hybrid cloud
  118. The importance of avoiding data silos
  119. The future of data storage
  120. Analysing and processing data
  121. Data communication
  122. Data storytelling and visualization
  123. Notes
  124. 14 Building data competencies in your organization
  125. The data skills shortage, and what it means for your business
  126. Building internal skills and competencies
  127. Outsourcing your data analytics
  128. Notes
  129. 15 Executing and revisiting your data strategy
  130. Putting data strategy into practice
  131. Why data strategies fail
  132. Creating a data culture
  133. Revisiting the data strategy
  134. Notes
  135. 16 Looking ahead
  136. The true value of AI
  137. But where will it all end?
  138. How does this relate to what I’m doing with AI?
  139. Notes
  140. Appendix 1: Data use case template
  141. Appendix 2: Data strategy template
  142. Index
  143. Copyright

About the Author

Bernard Marr is a bestselling author, keynote speaker, strategic performance consultant, and analytics, KPI, AI & Big Data guru. He has worked with and advised many of the world’s best-known organisations. LinkedIn has recently ranked Bernard as the number one LinkedIn Top Voice among UK influncers. He writes on the topic of data and analytics for various publications including Forbes, HuffPost, and LinkedIn Pulse. His blogs and SlideShare presentation of the topic have millions of readers.

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SKU: EB1335 Categories: Computer Science, Engineering Tags: AI, Artificial Intelligence, Big Data, Computer Science, Data Strategy, Engineering
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