Details

Data Science Strategy For Dummies


Data Science Strategy For Dummies


1. Aufl.

von: Ulrika Jägare

22,99 €

Verlag: Wiley
Format: EPUB
Veröffentl.: 12.06.2019
ISBN/EAN: 9781119566274
Sprache: englisch
Anzahl Seiten: 352

DRM-geschütztes eBook, Sie benötigen z.B. Adobe Digital Editions und eine Adobe ID zum Lesen.

Beschreibungen

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

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