Mind+Machine : A Decision Model for Optimizing and Implementing Analytics

**Cut through information overload to make better decisions faster** Success relies on making the correct decisions at the appropriate time, which is only possible if the decision maker has the necessary insights in a suitable format. __Mind+Machine__ is the guide to getting the right insights in the right format at the right time to the right person. Designed to show decision makers how to get the most out of every level of data analytics, this book explores the extraordinary potential to be found in a model where human ingenuity and skill are supported with cutting-edge tools, including automations. The marriage of the perceptive power of the human brain with the benefits of automation is essential because mind or machine alone cannot handle the complexities of modern analytics. Only when the two come together with structure and purpose to solve a problem are goals achieved. With various stakeholders in data analytics having their own take on what is important, it can be challenging for a business leader to create such a structure. This book provides a blueprint for decision makers, helping them ask the right questions, understand the answers, and ensure an approach to analytics that properly supports organizational growth. Discover how to: * Harness the power of insightful minds and the speed of analytics technology * Understand the demands and claims of various analytics stakeholders * Focus on the right data and automate the right processes · Navigate decisions with confidence in a fast-paced world The Mind+Machine model streamlines analytics workflows and refines the never-ending flood of incoming data into useful insights. Thus, __Mind+Machine__ equips you to take on the big decisions and win. Mind+Machine: A Decision Model for Optimizing and Implementing Analytics -- Contents -- Preface -- Acknowledgments -- List of Use Cases -- Part I: The Top 12 Fallacies about Mind+Machine -- Fallacy #1: Big Data Solves Everything -- Fallacy #2: More Data Means More Insight -- Fallacy #3: First, We Need a Data Lake and Tools -- Fallacy #4: Analytics Is Just an Analytics Challenge: Part I: The Last Mile -- Fallacy #5: Analytics Is Just an Analytics Challenge: Part II: The Organization -- Fallacy #6: Reorganizations Won't Hurt Analytics -- Fallacy #7: Knowledge Management Is Easy-we Just Need Some Wikis -- Fallacy #8: Intelligent Machines Can Solve Any Analytic Problem -- Fallacy #9: Everything Must Be Done In-House! -- Fallacy #10: We Need More, Larger, and Fancier Reports -- Fallacy #11: Analytics Investment Means Great ROI -- Fallacy #12: Analytics Is a Rational Process -- Part I: Conclusion -- Part II: 13 Trends Creating Massive Opportunities for Mind+Machine -- Trend #1: The Asteroid Impact of Cloud and Mobile -- Trend #2: The Yin and Yang of the Internet of Things -- Trend #3: One-to-One Marketing -- Trend #4: Regulatory Flooding of the Ring of Knowledge -- The European Union and Privacy Rules: The General Data Protection Regulation and the EU-US Privacy Shield -- The Teeth of the General Data Protection Regulation -- Privacy Impacting the Ring of Knowledge -- The Nine Questions You Need to Ask Your CIO Regarding Personal Data -- Trend #5: The Seismic Shift to Payas-You-Go or Outputbased Commercial Models -- Trend #6: The Hidden Treasures of Multiple-Client Utilities -- Trend #7: The Race for Data Assets, Alternative Data, and Smart Data -- Trend #8: Marketplaces and the Sharing Economy Finally Arriving in Data and Analytics -- Trend #9: Knowledge Management 2.0-Still an Elusive Vision? Big Data And Artificial Intelligence Dominate Business News Coverage, But These Are Far From Singular Solutions To Any Business Challenge. At The Core Of Successful Decision-making Is Human Instinct Driven By Experience And Information. Mind+machine Is The Groundbreaking Guide To Combining The Optimally Potent Mix Of Human Expertise And State-of-the-art Digital Machines To Enhance A Variety Of Business Functions. Marc Vollenweider. Includes Bibliographical References (pages 283-286) And Index.