Foreword 9Acknowledgments 13Introduction 171The importance and benefits of data science 351.1 Introduction 351.2 Fundamental developments 371.3 The Data-Decision gap 391.4 Twenty-five advantages of effective data science 452Data science processes and the intelligent organization 672.1 Introduction 672.2 The four maturity levels of data science 692.3 The three stages of the intelligent organization 732.4 The contours of the intelligent organization 752.5 Four key concepts 803The senses of the intelligent organization 853.1 Introduction 853.2 Developing sensors: four approaches 863.3 Key Performance Indicators: KPIs 1013.4 Machine learning models & big data 1073.5 Valuable insights 1123.6 The context of sensors and indicators 1143.7 Criteria of information and indicators 1203.8 Setting norms and targets for indicators 1244The brain of the intelligent organization 1354.1 Introduction 1354.2 Processing: turning data into knowledge & action 1364.3 AI, algorithms, and machine learning 1474.4 The processors: stakeholders 1704.5 Requirements of the processing cycle 1784.6 Business Intelligence frameworks 1815The limbs of the intelligent organization 1895.1 Introduction 1895.2 Research the necessity of an agile organization 1925.3 Expertise 1945.4 The structure of the agile organization 2015.5 Social infrastructure 2156The architecture of the intelligent organization 2296.1 Introduction 2296.2 Principles dictate the architecture 2326.3 The data warehouse and ETL 2366.4 The data lake 2596.5 Portals, mobile data science, and reflex BI 2666.6 Metadata: the glue of the intelligent organization 2717Data science instruments and applications 2797.1 Introduction 2797.2 Data science instruments 2797.3 Data science applications 3008Data science projects 3198.1 Introduction 3198.2 How to approach data science projects 3218.3 The data science project cycle: approach & steps 3228.4 Top ten data science risks and obstacles 3418.5 Data science roles and competencies 3479Data science strategy and governance 3639.1 Introduction 3639.2 Data science strategy and maturity: the map 3659.3 Governance of the BI cycle: the compass 3739.4 The BI Competency Center (BICC) 3769.5 The data science roadmap 3809.6 Privacy legislation and ethics 38310The future of organizations, society, and data science 39110.1 Introduction 39110.2 The future of data science 39110.3 The social effects of AI and data science 394Epilogue 401Reference List 405Index 421This unique Data Science book is for anyone who wants to achieve greater returns from BI, (big) data, artificial intelligence, data science, and machine learning (ML) models.Data-driven working & data science are the new normal. The tech giants are leading the way, using big data analytics and robots in their everyday activities. For most organizations, managers, decision-makers, and data professionals that’s still a bridge too far, but that future is coming ever closer. This book is for them. In ten clear steps, the author describes the path to creating a mature, intelligent, and data-driven organization.The author shares his novel, refreshing perspective on (big) data, KPIs, dashboards, Artificial Intelligence (AI), and decision-making. He reveals how managers and data professionals can make better and faster decisions using AI and Business Intelligence, leaving their mark on the digital transformation while securing the organization’s future. Embed decision-making based on facts and data into your daily routine and significantly increase the quality of your decisions. Use machine learning to identify patterns and develop your own “8 million dollar insights”.Most AI books read like technical manuals. This book approaches AI from a different angle and with a much wider scope. Under the data science umbrella, the author covers the principles of data-driven working, many kinds of algorithms, and the most important aspects of architecture. He sketches a holistic profile of the intelligent, data-driven organization, a proven success formula applied by tens of thousands of organizations worldwide. Get started now, using a simple report, a cool dashboard, big data, or experiment with a decision tree or neural network.Daan van Beek studied Information Science and attained his Master of Science in AI. He is the founder of Passionned Group, a 100% independent international consulting agency and training institute. Daan hosts AI and BI masterclasses around the world and teaches at various universities.This brand-new Data Science book offers a tantalizing new perspective for anyone who wants want to let the data do the talking and successfully implement AI and Data Science. This unique handbook combines Business Intelligence (BI), Artificial Intelligence (AI), big data analytics, machine learning, and data science in an easy-to-understand package. Make your organization data-driven in 10 steps by logically connecting these concepts. Learn how to use this complete Data Science book to make your organization intelligent and data-driven. Strengthen your organization’s immune system using an AI, BI, and Big Data analytics cocktail.