{"product_id":"data-science-strategy-for-dummies-isbn-9781119566250","title":"Data Science Strategy For Dummies","description":"\u003cp\u003e\u003cb\u003eAll the answers to your data science questions\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eOver half of all businesses are using data science to generate insights and value from big data. How are they doing it? \u003ci\u003eData Science Strategy For Dummies \u003c\/i\u003eanswers 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.\u003c\/p\u003e \u003cp\u003eWith 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.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eLearn exactly what data science is and why it’s important\u003c\/li\u003e \u003cli\u003eAdopt a data-driven mindset as the foundation to success\u003c\/li\u003e \u003cli\u003eUnderstand the processes and common roadblocks behind data science\u003c\/li\u003e \u003cli\u003eKeep your data science program focused on generating business value\u003c\/li\u003e \u003cli\u003eNurture a top-quality data science team\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eIn non-technical language, \u003ci\u003eData Science Strategy For Dummies \u003c\/i\u003eoutlines new perspectives and strategies to effectively lead analytics and data science functions to create real value.\u003c\/p\u003e \u003cp\u003eForeword xv\u003c\/p\u003e \u003cp\u003e\u003cb\u003eIntroduction\u003c\/b\u003e\u003cb\u003e 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eAbout This Book 2\u003c\/p\u003e \u003cp\u003eFoolish Assumptions 3\u003c\/p\u003e \u003cp\u003eHow This Book is Organized 3\u003c\/p\u003e \u003cp\u003eIcons Used In This Book 4\u003c\/p\u003e \u003cp\u003eBeyond The Book 4\u003c\/p\u003e \u003cp\u003eWhere To Go From Here 5\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 1: Optimizing Your Data Science Investment\u003c\/b\u003e\u003cb\u003e 7\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1: Framing Data Science Strategy\u003c\/b\u003e\u003cb\u003e 9\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEstablishing the Data Science Narrative 10\u003c\/p\u003e \u003cp\u003eCapture 11\u003c\/p\u003e \u003cp\u003eMaintain 12\u003c\/p\u003e \u003cp\u003eProcess 13\u003c\/p\u003e \u003cp\u003eAnalyze 14\u003c\/p\u003e \u003cp\u003eCommunicate 16\u003c\/p\u003e \u003cp\u003eActuate 17\u003c\/p\u003e \u003cp\u003eSorting Out the Concept of a Data-driven Organization 19\u003c\/p\u003e \u003cp\u003eApproaching data-driven 20\u003c\/p\u003e \u003cp\u003eBeing data obsessed 21\u003c\/p\u003e \u003cp\u003eSorting Out the Concept of Machine Learning 22\u003c\/p\u003e \u003cp\u003eDefining and Scoping a Data Science Strategy 26\u003c\/p\u003e \u003cp\u003eObjectives 26\u003c\/p\u003e \u003cp\u003eApproach 27\u003c\/p\u003e \u003cp\u003eChoices 27\u003c\/p\u003e \u003cp\u003eData 27\u003c\/p\u003e \u003cp\u003eLegal 28\u003c\/p\u003e \u003cp\u003eEthics 28\u003c\/p\u003e \u003cp\u003eCompetence 28\u003c\/p\u003e \u003cp\u003eInfrastructure 29\u003c\/p\u003e \u003cp\u003eGovernance and security 29\u003c\/p\u003e \u003cp\u003eCommercial\/business models 30\u003c\/p\u003e \u003cp\u003eMeasurements 30\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2: Considering the Inherent Complexity in Data Science\u003c\/b\u003e\u003cb\u003e 31\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDiagnosing Complexity in Data Science 32\u003c\/p\u003e \u003cp\u003eRecognizing Complexity as a Potential 33\u003c\/p\u003e \u003cp\u003eEnrolling in Data Science Pitfalls 101 34\u003c\/p\u003e \u003cp\u003eBelieving that all data is needed 34\u003c\/p\u003e \u003cp\u003eThinking that investing in a data lake will solve all your problems 35\u003c\/p\u003e \u003cp\u003eFocusing on AI when analytics is enough 36\u003c\/p\u003e \u003cp\u003eBelieving in the 1-tool approach 37\u003c\/p\u003e \u003cp\u003eInvesting only in certain areas 37\u003c\/p\u003e \u003cp\u003eLeveraging the infrastructure for reporting rather than exploration 38\u003c\/p\u003e \u003cp\u003eUnderestimating the need for skilled data scientists 39\u003c\/p\u003e \u003cp\u003e`Navigating the Complexity 40\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3: Dealing with Difficult Challenges\u003c\/b\u003e\u003cb\u003e 41\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eGetting Data from There to Here 41\u003c\/p\u003e \u003cp\u003eHandling dependencies on data owned by others 42\u003c\/p\u003e \u003cp\u003eManaging data transfer and computation across-country borders 43\u003c\/p\u003e \u003cp\u003eManaging Data Consistency Across the Data Science Environment 44\u003c\/p\u003e \u003cp\u003eSecuring Explainability in AI 45\u003c\/p\u003e \u003cp\u003eDealing with the Difference between Machine Learning and Traditional Software Programming 47\u003c\/p\u003e \u003cp\u003eManaging the Rapid AI Technology Evolution and Lack of Standardization 50\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4: Managing Change in Data Science\u003c\/b\u003e\u003cb\u003e 51\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding Change Management in Data Science 52\u003c\/p\u003e \u003cp\u003eApproaching Change in Data Science 53\u003c\/p\u003e \u003cp\u003eRecognizing what to avoid when driving change in data science 56\u003c\/p\u003e \u003cp\u003eUsing Data Science Techniques to Drive Successful Change 59\u003c\/p\u003e \u003cp\u003eUsing digital engagement tools 59\u003c\/p\u003e \u003cp\u003eApplying social media analytics to identify stakeholder sentiment 60\u003c\/p\u003e \u003cp\u003eCapturing reference data in change projects 61\u003c\/p\u003e \u003cp\u003eUsing data to select people for change roles 61\u003c\/p\u003e \u003cp\u003eAutomating change metrics 62\u003c\/p\u003e \u003cp\u003eGetting Started 62\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 2: Making Strategic Choices for Your Data\u003c\/b\u003e\u003cb\u003e 65\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5: Understanding the Past, Present, and Future of Data\u003c\/b\u003e\u003cb\u003e 67\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSorting Out the Basics of Data 68\u003c\/p\u003e \u003cp\u003eExplaining traditional data versus big data 69\u003c\/p\u003e \u003cp\u003eKnowing the value of data 71\u003c\/p\u003e \u003cp\u003eExploring Current Trends in Data 73\u003c\/p\u003e \u003cp\u003eData monetization 73\u003c\/p\u003e \u003cp\u003eResponsible AI 74\u003c\/p\u003e \u003cp\u003eCloud-based data architectures 75\u003c\/p\u003e \u003cp\u003eComputation and intelligence in the edge 75\u003c\/p\u003e \u003cp\u003eDigital twins 77\u003c\/p\u003e \u003cp\u003eBlockchain 78\u003c\/p\u003e \u003cp\u003eConversational platforms 79\u003c\/p\u003e \u003cp\u003eElaborating on Some Future Scenarios 80\u003c\/p\u003e \u003cp\u003eStandardization for data science productivity 80\u003c\/p\u003e \u003cp\u003eFrom data monetization scenarios to a data economy 82\u003c\/p\u003e \u003cp\u003eAn explosion of human\/machine hybrid systems 82\u003c\/p\u003e \u003cp\u003eQuantum computing will solve the unsolvable problems 83\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6: Knowing Your Data\u003c\/b\u003e\u003cb\u003e 85\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSelecting Your Data 85\u003c\/p\u003e \u003cp\u003eDescribing Data 87\u003c\/p\u003e \u003cp\u003eExploring Data 89\u003c\/p\u003e \u003cp\u003eAssessing Data Quality 93\u003c\/p\u003e \u003cp\u003eImproving Data Quality 95\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7: Considering the Ethical Aspects of Data Science\u003c\/b\u003e\u003cb\u003e 97\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExplaining AI Ethics 98\u003c\/p\u003e \u003cp\u003eAddressing trustworthy artificial intelligence 99\u003c\/p\u003e \u003cp\u003eIntroducing Ethics by Design 101\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8: Becoming Data-driven\u003c\/b\u003e\u003cb\u003e 103\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding Why Data-Driven is a Must 103\u003c\/p\u003e \u003cp\u003eTransitioning to a Data-Driven Model 105\u003c\/p\u003e \u003cp\u003eSecuring management buy-in and assigning a chief data officer (CDO) 106\u003c\/p\u003e \u003cp\u003eIdentifying the key business value aligned with the business maturity 107\u003c\/p\u003e \u003cp\u003eDeveloping a Data Strategy 108\u003c\/p\u003e \u003cp\u003eCaring for your data 109\u003c\/p\u003e \u003cp\u003eDemocratizing the data 109\u003c\/p\u003e \u003cp\u003eDriving data standardization 110\u003c\/p\u003e \u003cp\u003eStructuring the data strategy 110\u003c\/p\u003e \u003cp\u003eEstablishing a Data-Driven Culture and Mindset 111\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9: Evolving from Data-driven to Machine-driven\u003c\/b\u003e\u003cb\u003e 113\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDigitizing the Data 114\u003c\/p\u003e \u003cp\u003eApplying a Data-driven Approach 115\u003c\/p\u003e \u003cp\u003eAutomating Workflows 116\u003c\/p\u003e \u003cp\u003eIntroducing AI\/ML capabilities 116\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 3: Building a Successful Data Science Organization\u003c\/b\u003e\u003cb\u003e 119\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10: Building Successful Data Science Teams\u003c\/b\u003e\u003cb\u003e 121\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eStarting with the Data Science Team Leader 121\u003c\/p\u003e \u003cp\u003eAdopting different leadership approaches 122\u003c\/p\u003e \u003cp\u003eApproaching data science leadership 124\u003c\/p\u003e \u003cp\u003eFinding the right data science leader or manager 124\u003c\/p\u003e \u003cp\u003eDefining the Prerequisites for a Successful Team 125\u003c\/p\u003e \u003cp\u003eDeveloping a team structure 125\u003c\/p\u003e \u003cp\u003eEstablishing an infrastructure 126\u003c\/p\u003e \u003cp\u003eEnsuring data availability 126\u003c\/p\u003e \u003cp\u003eInsisting on interesting projects 127\u003c\/p\u003e \u003cp\u003ePromoting continuous learning 127\u003c\/p\u003e \u003cp\u003eEncouraging research studies 128\u003c\/p\u003e \u003cp\u003eBuilding the Team 128\u003c\/p\u003e \u003cp\u003eDeveloping smart hiring processes 129\u003c\/p\u003e \u003cp\u003eLetting your teams evolve organically 130\u003c\/p\u003e \u003cp\u003eConnecting the Team to the Business Purpose 131\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 11: Approaching a Data Science Organizational Setup\u003c\/b\u003e\u003cb\u003e 133\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFinding the Right Organizational Design 134\u003c\/p\u003e \u003cp\u003eDesigning the data science function 134\u003c\/p\u003e \u003cp\u003eEvaluating the benefits of a center of excellence for data science 136\u003c\/p\u003e \u003cp\u003eIdentifying success factors for a data science center of excellence 137\u003c\/p\u003e \u003cp\u003eApplying a Common Data Science Function 138\u003c\/p\u003e \u003cp\u003eSelecting a location 138\u003c\/p\u003e \u003cp\u003eApproaching ways of working 139\u003c\/p\u003e \u003cp\u003eManaging expectations 141\u003c\/p\u003e \u003cp\u003eSelecting an execution approach 142\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 12: Positioning the Role of the Chief Data Officer (CDO)\u003c\/b\u003e\u003cb\u003e 145\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eScoping the Role of the Chief Data Officer (CDO) 146\u003c\/p\u003e \u003cp\u003eExplaining Why a Chief Data Officer is Needed 149\u003c\/p\u003e \u003cp\u003eEstablishing the CDO Role 150\u003c\/p\u003e \u003cp\u003eThe Future of the CDO Role 152\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 13: Acquiring Resources and Competencies\u003c\/b\u003e\u003cb\u003e 155\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIdentifying the Roles in a Data Science Team 156\u003c\/p\u003e \u003cp\u003eData scientist 157\u003c\/p\u003e \u003cp\u003eData engineer 157\u003c\/p\u003e \u003cp\u003eMachine learning engineer 158\u003c\/p\u003e \u003cp\u003eData architect 159\u003c\/p\u003e \u003cp\u003eBusiness analyst 159\u003c\/p\u003e \u003cp\u003eSoftware engineer 159\u003c\/p\u003e \u003cp\u003eDomain expert 160\u003c\/p\u003e \u003cp\u003eSeeing What Makes a Great Data Scientist 160\u003c\/p\u003e \u003cp\u003eStructuring a Data Science Team 163\u003c\/p\u003e \u003cp\u003eHiring and evaluating the data science talent you need 165\u003c\/p\u003e \u003cp\u003eRetaining Competence in Data Science 167\u003c\/p\u003e \u003cp\u003eUnderstanding what makes a data scientist leave 169\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 4: Investing in the Right Infrastructure\u003c\/b\u003e\u003cb\u003e 173\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 14: Developing a Data Architecture \u003c\/b\u003e\u003cb\u003e175\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDefining What Makes Up a Data Architecture 176\u003c\/p\u003e \u003cp\u003eDescribing traditional architectural approaches 176\u003c\/p\u003e \u003cp\u003eElements of a data architecture 177\u003c\/p\u003e \u003cp\u003eExploring the Characteristics of a Modern Data Architecture 178\u003c\/p\u003e \u003cp\u003eExplaining Data Architecture Layers 181\u003c\/p\u003e \u003cp\u003eListing the Essential Technologies for a Modern Data Architecture 184\u003c\/p\u003e \u003cp\u003eNoSQL databases 184\u003c\/p\u003e \u003cp\u003eReal-time streaming platforms 185\u003c\/p\u003e \u003cp\u003eDocker and containers 185\u003c\/p\u003e \u003cp\u003eContainer repositories 186\u003c\/p\u003e \u003cp\u003eContainer orchestration 187\u003c\/p\u003e \u003cp\u003eMicroservices 187\u003c\/p\u003e \u003cp\u003eFunction as a service 188\u003c\/p\u003e \u003cp\u003eCreating a Modern Data Architecture 189\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 15: Focusing Data Governance on the Right Aspects\u003c\/b\u003e\u003cb\u003e 193\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eSorting Out Data Governance 194\u003c\/p\u003e \u003cp\u003eData governance for defense or offense 195\u003c\/p\u003e \u003cp\u003eObjectives for data governance 196\u003c\/p\u003e \u003cp\u003eExplaining Why Data Governance is Needed 197\u003c\/p\u003e \u003cp\u003eData governance saves money 197\u003c\/p\u003e \u003cp\u003eBad data governance is dangerous 198\u003c\/p\u003e \u003cp\u003eGood data governance provides clarity 198\u003c\/p\u003e \u003cp\u003eEstablishing Data Stewardship to Enforce Data Governance Rules 198\u003c\/p\u003e \u003cp\u003eImplementing a Structured Approach to Data Governance 199\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 16: Managing Models During Development and Production\u003c\/b\u003e\u003cb\u003e 203\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnfolding the Fundamentals of Model Management 203\u003c\/p\u003e \u003cp\u003eWorking with many models 204\u003c\/p\u003e \u003cp\u003eMaking the case for efficient model management 206\u003c\/p\u003e \u003cp\u003eImplementing Model Management 207\u003c\/p\u003e \u003cp\u003ePinpointing implementation challenges 208\u003c\/p\u003e \u003cp\u003eManaging model risk 210\u003c\/p\u003e \u003cp\u003eMeasuring the risk level 211\u003c\/p\u003e \u003cp\u003eIdentifying suitable control mechanisms 211\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 17: Exploring the Importance of Open Source\u003c\/b\u003e\u003cb\u003e 213\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExploring the Role of Open Source 213\u003c\/p\u003e \u003cp\u003eUnderstanding the importance of open source in smaller companies 214\u003c\/p\u003e \u003cp\u003eUnderstanding the trend 215\u003c\/p\u003e \u003cp\u003eDescribing the Context of Data Science Programming Languages 215\u003c\/p\u003e \u003cp\u003eUnfolding Open Source Frameworks for AI\/ML Models 218\u003c\/p\u003e \u003cp\u003eTensorFlow 219\u003c\/p\u003e \u003cp\u003eTheano 219\u003c\/p\u003e \u003cp\u003eTorch 219\u003c\/p\u003e \u003cp\u003eCaffe and Caffe2 220\u003c\/p\u003e \u003cp\u003eThe Microsoft Cognitive Toolkit (previously known as Microsoft CNTK) 220\u003c\/p\u003e \u003cp\u003eKeras 220\u003c\/p\u003e \u003cp\u003eScikit-learn 221\u003c\/p\u003e \u003cp\u003eSpark MLlib 221\u003c\/p\u003e \u003cp\u003eAzure ML Studio 221\u003c\/p\u003e \u003cp\u003eAmazon Machine Learning 221\u003c\/p\u003e \u003cp\u003eChoosing Open Source or Not? 222\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 18: Realizing the Infrastructure\u003c\/b\u003e\u003cb\u003e 223\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eApproaching Infrastructure Realization 223\u003c\/p\u003e \u003cp\u003eListing Key Infrastructure Considerations for AI and ML Support 226\u003c\/p\u003e \u003cp\u003eLocation 226\u003c\/p\u003e \u003cp\u003eCapacity 227\u003c\/p\u003e \u003cp\u003eData center setup 227\u003c\/p\u003e \u003cp\u003eEnd-to-end management 227\u003c\/p\u003e \u003cp\u003eNetwork infrastructure 228\u003c\/p\u003e \u003cp\u003eSecurity and ethics 228\u003c\/p\u003e \u003cp\u003eAdvisory and supporting services 229\u003c\/p\u003e \u003cp\u003eEcosystem fit 229\u003c\/p\u003e \u003cp\u003eAutomating Workflows in Your Data Infrastructure 229\u003c\/p\u003e \u003cp\u003eEnabling an Efficient Workspace for Data Engineers and Data Scientists 230\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 5: Data as a Business\u003c\/b\u003e\u003cb\u003e 233\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 19: Investing in Data as a Business\u003c\/b\u003e\u003cb\u003e 235\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExploring How to Monetize Data 236\u003c\/p\u003e \u003cp\u003eApproaching data monetization is about treating data as an asset 237\u003c\/p\u003e \u003cp\u003eData monetization in a data economy 238\u003c\/p\u003e \u003cp\u003eLooking to the Future of the Data Economy 240\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 20: Using Data for Insights or Commercial Opportunities\u003c\/b\u003e\u003cb\u003e 243\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eFocusing Your Data Science Investment 243\u003c\/p\u003e \u003cp\u003eDetermining the Drivers for Internal Business Insights 244\u003c\/p\u003e \u003cp\u003eRecognizing data science categories for practical implementation 245\u003c\/p\u003e \u003cp\u003eApplying data-science-driven internal business insights 247\u003c\/p\u003e \u003cp\u003eUsing Data for Commercial Opportunities 248\u003c\/p\u003e \u003cp\u003eDefining a data product 249\u003c\/p\u003e \u003cp\u003eDistinguishing between categories of data products 250\u003c\/p\u003e \u003cp\u003eBalancing Strategic Objectives 252\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 21: Engaging Differently with Your Customers\u003c\/b\u003e\u003cb\u003e 255\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eUnderstanding Your Customers 255\u003c\/p\u003e \u003cp\u003eStep 1: Engage your customers 256\u003c\/p\u003e \u003cp\u003eStep 2: Identify what drives your customers 257\u003c\/p\u003e \u003cp\u003eStep 3: Apply analytics and machine learning to customer actions 258\u003c\/p\u003e \u003cp\u003eStep 4: Predict and prepare for the next step 259\u003c\/p\u003e \u003cp\u003eStep 5: Imagine your customer’s future 260\u003c\/p\u003e \u003cp\u003eKeeping Your Customers Happy 261\u003c\/p\u003e \u003cp\u003eServing Customers More Efficiently 263\u003c\/p\u003e \u003cp\u003ePredicting demand 263\u003c\/p\u003e \u003cp\u003eAutomating tasks 264\u003c\/p\u003e \u003cp\u003eMaking company applications predictive 264\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 22: Introducing Data-driven Business Models\u003c\/b\u003e\u003cb\u003e 265\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDefining Business Models 265\u003c\/p\u003e \u003cp\u003eExploring Data-driven Business Models 267\u003c\/p\u003e \u003cp\u003eCreating data-centric businesses 268\u003c\/p\u003e \u003cp\u003eInvestigating different types of data-driven business models 268\u003c\/p\u003e \u003cp\u003eUsing a Framework for Data-driven Business Models 275\u003c\/p\u003e \u003cp\u003eCreating a data-driven business model using a framework 276\u003c\/p\u003e \u003cp\u003eKey resources 277\u003c\/p\u003e \u003cp\u003eKey activities 277\u003c\/p\u003e \u003cp\u003eOffering\/value proposition 278\u003c\/p\u003e \u003cp\u003eCustomer segment 278\u003c\/p\u003e \u003cp\u003eRevenue model 279\u003c\/p\u003e \u003cp\u003eCost structure 280\u003c\/p\u003e \u003cp\u003ePutting it all together 280\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 23: Handling New Delivery Models\u003c\/b\u003e\u003cb\u003e 281\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDefining Delivery Models for Data Products and Services 282\u003c\/p\u003e \u003cp\u003eUnderstanding and Adapting to New Delivery Models 282\u003c\/p\u003e \u003cp\u003eIntroducing New Ways to Deliver Data Products 284\u003c\/p\u003e \u003cp\u003eSelf-service analytics environments as a delivery model 285\u003c\/p\u003e \u003cp\u003eApplications, websites, and product\/service interfaces as delivery models 287\u003c\/p\u003e \u003cp\u003eExisting products and services 289\u003c\/p\u003e \u003cp\u003eDownloadable files 290\u003c\/p\u003e \u003cp\u003eAPIs 290\u003c\/p\u003e \u003cp\u003eCloud services 291\u003c\/p\u003e \u003cp\u003eOnline market places 291\u003c\/p\u003e \u003cp\u003eDownloadable licenses 292\u003c\/p\u003e \u003cp\u003eOnline services 293\u003c\/p\u003e \u003cp\u003eOnsite services 293\u003c\/p\u003e \u003cp\u003e\u003cb\u003ePart 6: The Part of Tens\u003c\/b\u003e\u003cb\u003e 295\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 24: Ten Reasons to Develop a Data Science Strategy\u003c\/b\u003e\u003cb\u003e 297\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eExpanding Your View on Data Science 297\u003c\/p\u003e \u003cp\u003eAligning the Company View 298\u003c\/p\u003e \u003cp\u003eCreating a Solid Base for Execution 299\u003c\/p\u003e \u003cp\u003eRealizing Priorities Early 299\u003c\/p\u003e \u003cp\u003ePutting the Objective into Perspective 300\u003c\/p\u003e \u003cp\u003eCreating an Excellent Base for Communication 300\u003c\/p\u003e \u003cp\u003eUnderstanding Why Choices Matter 301\u003c\/p\u003e \u003cp\u003eIdentifying the Risks Early 301\u003c\/p\u003e \u003cp\u003eThoroughly Considering Your Data Need 302\u003c\/p\u003e \u003cp\u003eUnderstanding the Change Impact 303\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 25: Ten Mistakes to Avoid When Investing in Data Science\u003c\/b\u003e\u003cb\u003e 305\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDon’t Tolerate Top Management’s Ignorance of Data Science 305\u003c\/p\u003e \u003cp\u003eDon’t Believe That AI is Magic 306\u003c\/p\u003e \u003cp\u003eDon’t Approach Data Science as a Race to the Death between Man and Machine 307\u003c\/p\u003e \u003cp\u003eDon’t Underestimate the Potential of AI 308\u003c\/p\u003e \u003cp\u003eDon’t Underestimate the Needed Data Science Skill Set 308\u003c\/p\u003e \u003cp\u003eDon’t Think That a Dashboard is the End Objective 309\u003c\/p\u003e \u003cp\u003eDon’t Forget about the Ethical Aspects of AI 310\u003c\/p\u003e \u003cp\u003eDon’t Forget to Consider the Legal Rights to the Data 311\u003c\/p\u003e \u003cp\u003eDon’t Ignore the Scale of Change Needed 312\u003c\/p\u003e \u003cp\u003eDon’t Forget the Measurements Needed to Prove Value 313\u003c\/p\u003e \u003cp\u003eIndex 315\u003c\/p\u003e  \u003cp\u003e\u003cb\u003eUlrika Jägare\u003c\/b\u003e 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\u0026amp;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.   \u003c\/p\u003e\u003cul\u003e \u003cli\u003eAdopt a data-driven mindset for business success\u003c\/li\u003e \u003cli\u003eKeep your data science program focused on generating value\u003c\/li\u003e \u003cli\u003eNurture a top-quality data science team\u003c\/li\u003e\t \u003c\/ul\u003e \u003cp\u003e\u003cb\u003eWho's afraid of data science? Not you!\u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eUsing 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. \u003c\/p\u003e\u003cp\u003e\u003cb\u003eInside...\u003c\/b\u003e \u003c\/p\u003e\u003cul\u003e \u003cli\u003eData science basics\u003c\/li\u003e \u003cli\u003eFraming a strategy\u003c\/li\u003e \u003cli\u003eData-driven business models\u003c\/li\u003e \u003cli\u003eChoosing your data\u003c\/li\u003e \u003cli\u003eEthical aspects of data usage\u003c\/li\u003e \u003cli\u003eBuilding a successful team\u003c\/li\u003e \u003cli\u003eExplaining the role of the Chief Data Officer\u003c\/li\u003e \u003cli\u003eDealing with effective data architectures\u003c\/li\u003e \u003c\/ul\u003e","brand":"For Dummies","offers":[{"title":"Default Title","offer_id":47989026029797,"sku":"NP9781119566250","price":29.99,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9781119566250.jpg?v=1761782490","url":"https:\/\/k12savings.com\/es\/products\/data-science-strategy-for-dummies-isbn-9781119566250","provider":"K12savings","version":"1.0","type":"link"}