Descripciones mejoradas de Syndetics:

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.

Tabla de contenidos provista por Syndetics

• About the author(p. x)
• Acknowledgements(p. xi)
• 01 Introduction: Why every business is now a data business(p. 1)
• The astonishing growth of data, artificial intelligence and the Internet of Things(p. 1)
• A brave new (data-driven) world(p. 3)
• Are we nearing true artificial intelligence?(p. 7)
• The fourth industrial revolution - or Industry 4.0(p. 8)
• Other world-changing technologies(p. 10)
• Why every business must become a data business(p. 11)
• Notes(p. 13)
• 02 Use cases for data(p. 15)
• The six key use cases(p. 16)
• Key data use cases in practice(p. 19)
• Some industry-specific use cases(p. 21)
• How data is revolutionizing the world of business(p. 22)
• 03 Using data to improve your business decisions(p. 23)
• Setting out your key business questions(p. 23)
• Understanding and interpreting your data(p. 26)
• Curated data dashboards - the fine dining experience(p. 27)
• Self-service data exploration dashboards - the raclette grill experience(p. 29)
• Raclette grill analytics in the real world(p. 30)
• Data democratization and the role of the data translator(p. 31)
• Data storytelling(p. 32)
• The future of data visualization and storytelling(p. 36)
• 04 Using data to understand your customers(p. 39)
• Understanding customer analytics(p. 40)
• Types of customer data(p. 43)
• Pioneering the 360-degree customer view(p. 46)
• Customer analytics at Netflix(p. 48)
• Real-time personalization and micro-moments(p. 49)
• Disney's Magic Bands(p. 50)
• How data enables customer-led design process(p. 51)
• The value of personal connections with customers(p. 52)
• 05 Using data to create more intelligent services(p. 55)
• Tech services(p. 56)
• New tricks for old dogs(p. 59)
• Smart services in banking, finance and insurance(p. 60)
• Smart services in healthcare, medicine and pharmaceuticals(p. 64)
• Smart services in fashion and clothing(p. 66)
• Robots as a service(p. 67)
• Smart education and training services(p. 68)
• AI itself as a service(p. 70)
• Every company is a tech company now(p. 71)
• Notes(p. 72)
• 06 Using data to make more intelligent products(p. 73)
• How smart products enable smart services(p. 74)
• Autonomous vehicles and mobility(p. 75)
• Intelligent home products(p. 78)
• Intelligent healthcare products(p. 82)
• Intelligent business, industry and manufacturing products(p. 84)
• Intelligent sports products(p. 86)
• Notes(p. 87)
• 07 Using data to improve your business processes(p. 91)
• Day-to-day processes and the digital twin(p. 92)
• Sales, marketing and customer service(p. 93)
• Distribution, warehousing and logistics(p. 96)
• Product development(p. 99)
• Manufacturing and production(p. 101)
• Support services - IT, finance and HR(p. 103)
• Notes(p. 106)
• 08 Monetizing your data(p. 109)
• Increasing the value of your organization(p. 110)
• When data itself is the core business asset(p. 111)
• When the value lies in a company's ability to work with data(p. 114)
• Selling data to customers or interested parties(p. 116)
• Understanding the value of user-generated data(p. 119)
• Notes(p. 120)
• 09 Defining your data use cases(p. 121)
• Identifying use cases(p. 122)
• How does the use case link to a strategic goal?(p. 122)
• What is the objective of the use case?(p. 123)
• How will you measure the success of the use case?(p. 124)
• Who will be the use case owner?(p. 124)
• Who will be the data customers?(p. 125)
• What data do we need?(p. 125)
• What data governance issues need to be addressed?(p. 126)
• How do we analyse the data and turn it into insights?(p. 127)
• What are the technology requirements?(p. 127)
• What skills and capabilities do we need?(p. 128)
• What are the issues around implementation we need to be aware of?(p. 129)
• Pick the most effective use cases and use them to build a data strategy(p. 130)
• Constructing your data strategy(p. 131)
• 10 Sourcing and collecting data(p. 133)
• Understanding the different types of data(p. 136)
• Taking a look at newer types of data(p. 142)
• Gathering your internal data(p. 145)
• Accessing external data(p. 147)
• When the data you want doesn't exist(p. 149)
• 11 Data governance, ethics and trust(p. 151)
• The ethics of AI(p. 151)
• Bias and the importance of 'clean' data(p. 156)
• Staying on the right side of the law(p. 159)
• Keeping your data safe(p. 160)
• Practising data governance(p. 164)
• Notes(p. 165)
• 12 Turning data into insights(p. 167)
• The evolution of analytics(p. 168)
• Advanced analytics - from science fiction to business fact(p. 170)
• Machine learning - the current cutting-edge in AI(p. 171)
• Supervised learning(p. 173)
• Unsupervised learning(p. 174)
• Reinforcement learning(p. 175)
• Deep learning and neural networks(p. 176)
• Generative adversarial networks (GANs)(p. 177)
• Advanced analytics in practice(p. 178)
• Types of analytics(p. 178)
• No-code and as-a-service AI infrastructure(p. 182)
• Notes(p. 183)
• 13 Creating a technology and data infrastructure(p. 185)
• Data, analytics and AI as a service(p. 187)
• Collecting data(p. 189)
• Storing data(p. 193)
• Public, private and hybrid cloud(p. 195)
• The importance of avoiding data silos(p. 197)
• The future of data storage(p. 197)
• Analysing and processing data(p. 198)
• Data communication(p. 200)
• Data storytelling and visualization(p. 201)
• Notes(p. 204)
• 14 Building data competencies in your organization(p. 205)
• The data skills shortage, and what it means for your business(p. 206)
• Building internal skills and competencies(p. 208)
• Outsourcing your data analytics(p. 215)
• Notes(p. 220)
• 15 Executing and revisiting your data strategy(p. 221)
• Putting data strategy into practice(p. 222)
• Why data strategies fail(p. 224)
• Creating a data culture(p. 226)
• Revisiting the data strategy(p. 228)
• Notes(p. 232)
• 16 Looking ahead(p. 233)
• The true value of AI(p. 235)
• But where will it all end?(p. 241)
• How does this relate to what I'm doing with AI?(p. 243)
• Notes(p. 244)
• Appendix 1 Data use case template(p. 247)
• Appendix 2 Data strategy template(p. 251)
• Index(p. 253)

Notas de autor provistas por Syndetics

Bernard Marr is a bestselling author, futurist and world-renowned expert in the areas of data and artificial intelligence. He is the author of 20 books, writes a regular column for Forbes and works for and coaches many of the world's best-known organizations, including Amazon, Google, Microsoft, IBM, Shell, NATO and the UN. He is also a major influencer with over 2 million social media followers and over 1 million newsletter subscribers. LinkedIn rank him as one of the top 5 business influencers in the world and number 1 influencer in the UK.