All the answers to your data science questions Over half of all businesses are using data science to generate insights and value from big data. How are they doing it? Data Science Strategy For Dummies answers all your questions about how to build a data science capability from scratch, starting with the “what” and the “why” of data science and covering what it takes to lead and nurture a top-notch team of data scientists. With this book, you’ll learn how to incorporate data science as a strategic function into any business, large or small. Find solutions to your real-life challenges as you uncover the stories and value hidden within data. Learn exactly what data science is and why it’s important Adopt a data-driven mindset as the foundation to success Understand the processes and common roadblocks behind data science Keep your data science program focused on generating business value Nurture a top-quality data science team In non-technical language, Data Science Strategy For Dummies outlines new perspectives and strategies to effectively lead analytics and data science functions to create real value.

Table of Contents:
Foreword xv Introduction 1 About This Book 2 Foolish Assumptions 3 How This Book is Organized 3 Icons Used In This Book 4 Beyond The Book 4 Where To Go From Here 5 Part 1: Optimizing Your Data Science Investment 7 Chapter 1: Framing Data Science Strategy 9 Establishing the Data Science Narrative 10 Capture 11 Maintain 12 Process 13 Analyze 14 Communicate 16 Actuate 17 Sorting Out the Concept of a Data-driven Organization 19 Approaching data-driven 20 Being data obsessed 21 Sorting Out the Concept of Machine Learning 22 Defining and Scoping a Data Science Strategy 26 Objectives 26 Approach 27 Choices 27 Data 27 Legal 28 Ethics 28 Competence 28 Infrastructure 29 Governance and security 29 Commercial/business models 30 Measurements 30 Chapter 2: Considering the Inherent Complexity in Data Science 31 Diagnosing Complexity in Data Science 32 Recognizing Complexity as a Potential 33 Enrolling in Data Science Pitfalls 101 34 Believing that all data is needed 34 Thinking that investing in a data lake will solve all your problems 35 Focusing on AI when analytics is enough 36 Believing in the 1-tool approach 37 Investing only in certain areas 37 Leveraging the infrastructure for reporting rather than exploration 38 Underestimating the need for skilled data scientists 39 `Navigating the Complexity 40 Chapter 3: Dealing with Difficult Challenges 41 Getting Data from There to Here 41 Handling dependencies on data owned by others 42 Managing data transfer and computation across-country borders 43 Managing Data Consistency Across the Data Science Environment 44 Securing Explainability in AI 45 Dealing with the Difference between Machine Learning and Traditional Software Programming 47 Managing the Rapid AI Technology Evolution and Lack of Standardization 50 Chapter 4: Managing Change in Data Science 51 Understanding Change Management in Data Science 52 Approaching Change in Data Science 53 Recognizing what to avoid when driving change in data science 56 Using Data Science Techniques to Drive Successful Change 59 Using digital engagement tools 59 Applying social media analytics to identify stakeholder sentiment 60 Capturing reference data in change projects 61 Using data to select people for change roles 61 Automating change metrics 62 Getting Started 62 Part 2: Making Strategic Choices for Your Data 65 Chapter 5: Understanding the Past, Present, and Future of Data 67 Sorting Out the Basics of Data 68 Explaining traditional data versus big data 69 Knowing the value of data 71 Exploring Current Trends in Data 73 Data monetization 73 Responsible AI 74 Cloud-based data architectures 75 Computation and intelligence in the edge 75 Digital twins 77 Blockchain 78 Conversational platforms 79 Elaborating on Some Future Scenarios 80 Standardization for data science productivity 80 From data monetization scenarios to a data economy 82 An explosion of human/machine hybrid systems 82 Quantum computing will solve the unsolvable problems 83 Chapter 6: Knowing Your Data 85 Selecting Your Data 85 Describing Data 87 Exploring Data 89 Assessing Data Quality 93 Improving Data Quality 95 Chapter 7: Considering the Ethical Aspects of Data Science 97 Explaining AI Ethics 98 Addressing trustworthy artificial intelligence 99 Introducing Ethics by Design 101 Chapter 8: Becoming Data-driven 103 Understanding Why Data-Driven is a Must 103 Transitioning to a Data-Driven Model 105 Securing management buy-in and assigning a chief data officer (CDO) 106 Identifying the key business value aligned with the business maturity 107 Developing a Data Strategy 108 Caring for your data 109 Democratizing the data 109 Driving data standardization 110 Structuring the data strategy 110 Establishing a Data-Driven Culture and Mindset 111 Chapter 9: Evolving from Data-driven to Machine-driven 113 Digitizing the Data 114 Applying a Data-driven Approach 115 Automating Workflows 116 Introducing AI/ML capabilities 116 Part 3: Building a Successful Data Science Organization 119 Chapter 10: Building Successful Data Science Teams 121 Starting with the Data Science Team Leader 121 Adopting different leadership approaches 122 Approaching data science leadership 124 Finding the right data science leader or manager 124 Defining the Prerequisites for a Successful Team 125 Developing a team structure 125 Establishing an infrastructure 126 Ensuring data availability 126 Insisting on interesting projects 127 Promoting continuous learning 127 Encouraging research studies 128 Building the Team 128 Developing smart hiring processes 129 Letting your teams evolve organically 130 Connecting the Team to the Business Purpose 131 Chapter 11: Approaching a Data Science Organizational Setup 133 Finding the Right Organizational Design 134 Designing the data science function 134 Evaluating the benefits of a center of excellence for data science 136 Identifying success factors for a data science center of excellence 137 Applying a Common Data Science Function 138 Selecting a location 138 Approaching ways of working 139 Managing expectations 141 Selecting an execution approach 142 Chapter 12: Positioning the Role of the Chief Data Officer (CDO) 145 Scoping the Role of the Chief Data Officer (CDO) 146 Explaining Why a Chief Data Officer is Needed 149 Establishing the CDO Role 150 The Future of the CDO Role 152 Chapter 13: Acquiring Resources and Competencies 155 Identifying the Roles in a Data Science Team 156 Data scientist 157 Data engineer 157 Machine learning engineer 158 Data architect 159 Business analyst 159 Software engineer 159 Domain expert 160 Seeing What Makes a Great Data Scientist 160 Structuring a Data Science Team 163 Hiring and evaluating the data science talent you need 165 Retaining Competence in Data Science 167 Understanding what makes a data scientist leave 169 Part 4: Investing in the Right Infrastructure 173 Chapter 14: Developing a Data Architecture 175 Defining What Makes Up a Data Architecture 176 Describing traditional architectural approaches 176 Elements of a data architecture 177 Exploring the Characteristics of a Modern Data Architecture 178 Explaining Data Architecture Layers 181 Listing the Essential Technologies for a Modern Data Architecture 184 NoSQL databases 184 Real-time streaming platforms 185 Docker and containers 185 Container repositories 186 Container orchestration 187 Microservices 187 Function as a service 188 Creating a Modern Data Architecture 189 Chapter 15: Focusing Data Governance on the Right Aspects 193 Sorting Out Data Governance 194 Data governance for defense or offense 195 Objectives for data governance 196 Explaining Why Data Governance is Needed 197 Data governance saves money 197 Bad data governance is dangerous 198 Good data governance provides clarity 198 Establishing Data Stewardship to Enforce Data Governance Rules 198 Implementing a Structured Approach to Data Governance 199 Chapter 16: Managing Models During Development and Production 203 Unfolding the Fundamentals of Model Management 203 Working with many models 204 Making the case for efficient model management 206 Implementing Model Management 207 Pinpointing implementation challenges 208 Managing model risk 210 Measuring the risk level 211 Identifying suitable control mechanisms 211 Chapter 17: Exploring the Importance of Open Source 213 Exploring the Role of Open Source 213 Understanding the importance of open source in smaller companies 214 Understanding the trend 215 Describing the Context of Data Science Programming Languages 215 Unfolding Open Source Frameworks for AI/ML Models 218 TensorFlow 219 Theano 219 Torch 219 Caffe and Caffe2 220 The Microsoft Cognitive Toolkit (previously known as Microsoft CNTK) 220 Keras 220 Scikit-learn 221 Spark MLlib 221 Azure ML Studio 221 Amazon Machine Learning 221 Choosing Open Source or Not? 222 Chapter 18: Realizing the Infrastructure 223 Approaching Infrastructure Realization 223 Listing Key Infrastructure Considerations for AI and ML Support 226 Location 226 Capacity 227 Data center setup 227 End-to-end management 227 Network infrastructure 228 Security and ethics 228 Advisory and supporting services 229 Ecosystem fit 229 Automating Workflows in Your Data Infrastructure 229 Enabling an Efficient Workspace for Data Engineers and Data Scientists 230 Part 5: Data as a Business 233 Chapter 19: Investing in Data as a Business 235 Exploring How to Monetize Data 236 Approaching data monetization is about treating data as an asset 237 Data monetization in a data economy 238 Looking to the Future of the Data Economy 240 Chapter 20: Using Data for Insights or Commercial Opportunities 243 Focusing Your Data Science Investment 243 Determining the Drivers for Internal Business Insights 244 Recognizing data science categories for practical implementation 245 Applying data-science-driven internal business insights 247 Using Data for Commercial Opportunities 248 Defining a data product 249 Distinguishing between categories of data products 250 Balancing Strategic Objectives 252 Chapter 21: Engaging Differently with Your Customers 255 Understanding Your Customers 255 Step 1: Engage your customers 256 Step 2: Identify what drives your customers 257 Step 3: Apply analytics and machine learning to customer actions 258 Step 4: Predict and prepare for the next step 259 Step 5: Imagine your customer’s future 260 Keeping Your Customers Happy 261 Serving Customers More Efficiently 263 Predicting demand 263 Automating tasks 264 Making company applications predictive 264 Chapter 22: Introducing Data-driven Business Models 265 Defining Business Models 265 Exploring Data-driven Business Models 267 Creating data-centric businesses 268 Investigating different types of data-driven business models 268 Using a Framework for Data-driven Business Models 275 Creating a data-driven business model using a framework 276 Key resources 277 Key activities 277 Offering/value proposition 278 Customer segment 278 Revenue model 279 Cost structure 280 Putting it all together 280 Chapter 23: Handling New Delivery Models 281 Defining Delivery Models for Data Products and Services 282 Understanding and Adapting to New Delivery Models 282 Introducing New Ways to Deliver Data Products 284 Self-service analytics environments as a delivery model 285 Applications, websites, and product/service interfaces as delivery models 287 Existing products and services 289 Downloadable files 290 APIs 290 Cloud services 291 Online market places 291 Downloadable licenses 292 Online services 293 Onsite services 293 Part 6: The Part of Tens 295 Chapter 24: Ten Reasons to Develop a Data Science Strategy 297 Expanding Your View on Data Science 297 Aligning the Company View 298 Creating a Solid Base for Execution 299 Realizing Priorities Early 299 Putting the Objective into Perspective 300 Creating an Excellent Base for Communication 300 Understanding Why Choices Matter 301 Identifying the Risks Early 301 Thoroughly Considering Your Data Need 302 Understanding the Change Impact 303 Chapter 25: Ten Mistakes to Avoid When Investing in Data Science 305 Don’t Tolerate Top Management’s Ignorance of Data Science 305 Don’t Believe That AI is Magic 306 Don’t Approach Data Science as a Race to the Death between Man and Machine 307 Don’t Underestimate the Potential of AI 308 Don’t Underestimate the Needed Data Science Skill Set 308 Don’t Think That a Dashboard is the End Objective 309 Don’t Forget about the Ethical Aspects of AI 310 Don’t Forget to Consider the Legal Rights to the Data 311 Don’t Ignore the Scale of Change Needed 312 Don’t Forget the Measurements Needed to Prove Value 313 Index 315