Data strategy : how to profit from a world of big data, analytics and artificial intelligence

Cover
Contents
About the author
Acknowledgements
01 Introduction: Why every business is now a data business
	The astonishing growth of data, artificial intelligence and the Internet of Things
	A brave new (data-driven) world
	Are we nearing true artificial intelligence?
	The fourth industrial revolution – or Industry 4.0
	Other world-changing technologies
	Why every business must become a data business
	Notes
02 Use cases for data
	The six key use cases
	Key data use cases in practice
	Some industry-specific use cases
	How data is revolutionizing the world of business
03 Using data to improve your business decisions
	Setting out your key business questions
	Understanding and interpreting your data
	Curated data dashboards – the fine dining experience
	Self-service data exploration dashboards – the raclette grill experience
	Raclette grill analytics in the real world
	Data democratization and the role of the data translator
	Data storytelling
	The future of data visualization and storytelling
04 Using data to understand your customers
	Understanding customer analytics
	Types of customer data
	Pioneering the 360-degree customer view
	Customer analytics at Netflix
	Real-time personalization and micro-moments
	Disney’s Magic Bands
	How data enables customer-led design process
	The value of personal connections with customers
05 Using data to create more intelligent services
	Tech services
	New tricks for old dogs
	Smart services in banking, finance and insurance
	Smart services in healthcare, medicine and pharmaceuticals
	Smart services in fashion and clothing
	Robots as a service
	Smart education and training services
	AI itself as a service
	Every company is a tech company now
	Notes
06 Using data to make more intelligent products
	How smart products enable smart services
	Autonomous vehicles and mobility
	Intelligent home products
	Intelligent healthcare products
	Intelligent business, industry and manufacturing products
	Intelligent sports products
	Notes
07 Using data to improve your business processes
	Day-to-day processes and the digital twin
	Sales, marketing and customer service
	Distribution, warehousing and logistics
	Product development
	Manufacturing and production
	Support services – IT, finance and HR
	Notes
08 Monetizing your data
	Increasing the value of your organization
	When data itself is the core business asset
	When the value lies in a company’s ability to work with data
	Selling data to customers or interested parties
	Understanding the value of user-generated data
	Notes
09 Defining your data use cases
	Identifying use cases
	How does the use case link to a strategic goal?
	What is the objective of the use case?
	How will you measure the success of the use case?
	Who will be the use case owner?
	Who will be the data customers?
	What data do we need?
	What data governance issues need to be addressed?
	How do we analyse the data and turn it into insights?
	What are the technology requirements?
	What skills and capabilities do we need?
	What are the issues around implementation we need to be aware of?
	Pick the most effective use cases and use them to build a data strategy
	Constructing your data strategy
10 Sourcing and collecting data
	Understanding the different types of data
	Taking a look at newer types of data
	Gathering your internal data
	Accessing external data
	When the data you want doesn’t exist
11 Data governance, ethics and trust
	The ethics of AI
	Bias and the importance of ‘clean’ data
	Staying on the right side of the law
	Keeping your data safe
	Practising data governance
	Notes
12 Turning data into insights
	The evolution of analytics
	Advanced analytics – from science fiction to business fact
	Machine learning – the current cutting-edge in AI
	Supervised learning
	Unsupervised learning
	Reinforcement learning
	Deep learning and neural networks
	Generative adversarial networks (GANs)
	Advanced analytics in practice
	Types of analytics
	No-code and as-a-service AI infrastructure
	Notes
13 Creating a technology and data infrastructure
	Data, analytics and AI as a service
	Collecting data
	Storing data
	Public, private and hybrid cloud
	The importance of avoiding data silos
	The future of data storage
	Analysing and processing data
	Data communication
	Data storytelling and visualization
	Notes
14 Building data competencies in your organization
	The data skills shortage, and what it means for your business
	Building internal skills and competencies
	Outsourcing your data analytics
	Notes
15 Executing and revisiting your data strategy
	Putting data strategy into practice
	Why data strategies fail
	Creating a data culture
	Revisiting the data strategy
	Notes
16 Looking ahead
	The true value of AI
	But where will it all end?
	How does this relate to what I’m doing with AI?
	Notes
Appendix 1: Data use case template
Appendix 2: Data strategy template
Index