{"product_id":"smarter-data-science-9781119693413","title":"Smarter Data Science","description":"\u003cb\u003eBook Synopsis\u003c\/b\u003e\u003cbr\u003e\u003cp\u003e\u003cb\u003eOrganizations can make data science a repeatable, predictable tool, which business professionals use to get more value from their data\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eEnterprise data and AI projects are often scattershot, underbaked, siloed, and not adaptable to predictable business changes. As a result, the vast majority fail. These expensive quagmires can be avoided, and this book explains precisely how.\u003c\/p\u003e \u003cp\u003eData science is emerging as a hands-on tool for not just data scientists, but business professionals as well. Managers, directors, IT leaders, and analysts must expand their use of data science capabilities for the organization to stay competitive.\u003ci\u003e Smarter Data Science\u003c\/i\u003e helps them achieve their enterprise-grade data projects and AI goals. It serves as a guide to building a robust and comprehensive information architecture program that enables sustainable and scalable AI deployments.\u003c\/p\u003e \u003cp\u003eWhen an organization manages its data effectively, its data science program becomes a fully scala\u003cbr\u003e\u003cbr\u003e\u003cb\u003eTable of Contents\u003c\/b\u003e\u003cbr\u003e\u003c\/p\u003e\u003cp\u003eForeword for \u003ci\u003eSmarter Data Science \u003c\/i\u003exix\u003c\/p\u003e \u003cp\u003eEpigraph xxi\u003c\/p\u003e \u003cp\u003ePreamble xxiii\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 1 Climbing the AI Ladder 1\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eReadying Data for AI 2\u003c\/p\u003e \u003cp\u003eTechnology Focus Areas 3\u003c\/p\u003e \u003cp\u003eTaking the Ladder Rung by Rung 4\u003c\/p\u003e \u003cp\u003eConstantly Adapt to Retain Organizational Relevance 8\u003c\/p\u003e \u003cp\u003eData-Based Reasoning is Part and Parcel in the Modern Business 10\u003c\/p\u003e \u003cp\u003eToward the AI-Centric Organization 14\u003c\/p\u003e \u003cp\u003eSummary 16\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 2 Framing Part I: Considerations for Organizations Using AI 17\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eData-Driven Decision-Making 18\u003c\/p\u003e \u003cp\u003eUsing Interrogatives to Gain Insight 19\u003c\/p\u003e \u003cp\u003eThe Trust Matrix 20\u003c\/p\u003e \u003cp\u003eThe Importance of Metrics and Human Insight 22\u003c\/p\u003e \u003cp\u003eDemocratizing Data and Data Science 23\u003c\/p\u003e \u003cp\u003eAye, a Prerequisite: Organizing Data Must Be a Forethought 26\u003c\/p\u003e \u003cp\u003ePreventing Design Pitfalls 27\u003c\/p\u003e \u003cp\u003eFacilitating the Winds of Change: How Organized Data Facilitates Reaction Time 29\u003c\/p\u003e \u003cp\u003e\u003ci\u003eQuae Quaestio \u003c\/i\u003e(Question Everything) 30\u003c\/p\u003e \u003cp\u003eSummary 32\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 3 Framing Part II: Considerations for Working with Data and AI 35\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003ePersonalizing the Data Experience for Every User 36\u003c\/p\u003e \u003cp\u003eContext Counts: Choosing the Right Way to Display Data 38\u003c\/p\u003e \u003cp\u003eEthnography: Improving Understanding Through Specialized Data 42\u003c\/p\u003e \u003cp\u003eData Governance and Data Quality 43\u003c\/p\u003e \u003cp\u003eThe Value of Decomposing Data 43\u003c\/p\u003e \u003cp\u003eProviding Structure Through Data Governance 43\u003c\/p\u003e \u003cp\u003eCurating Data for Training 45\u003c\/p\u003e \u003cp\u003eAdditional Considerations for Creating Value 45\u003c\/p\u003e \u003cp\u003eOntologies: A Means for Encapsulating Knowledge 46\u003c\/p\u003e \u003cp\u003eFairness, Trust, and Transparency in AI Outcomes 49\u003c\/p\u003e \u003cp\u003eAccessible, Accurate, Curated, and Organized 52\u003c\/p\u003e \u003cp\u003eSummary 54\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 4 A Look Back on Analytics: More Than One Hammer 57\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eBeen Here Before: Reviewing the Enterprise Data Warehouse 57\u003c\/p\u003e \u003cp\u003eDrawbacks of the Traditional Data Warehouse 64\u003c\/p\u003e \u003cp\u003eParadigm Shift 68\u003c\/p\u003e \u003cp\u003eModern Analytical Environments: The Data Lake 69\u003c\/p\u003e \u003cp\u003eBy Contrast 71\u003c\/p\u003e \u003cp\u003eIndigenous Data 72\u003c\/p\u003e \u003cp\u003eAttributes of Difference 73\u003c\/p\u003e \u003cp\u003eElements of the Data Lake 75\u003c\/p\u003e \u003cp\u003eThe New Normal: Big Data is Now Normal Data 77\u003c\/p\u003e \u003cp\u003eLiberation from the Rigidity of a Single Data Model 78\u003c\/p\u003e \u003cp\u003eStreaming Data 78\u003c\/p\u003e \u003cp\u003eSuitable Tools for the Task 78\u003c\/p\u003e \u003cp\u003eEasier Accessibility 79\u003c\/p\u003e \u003cp\u003eReducing Costs 79\u003c\/p\u003e \u003cp\u003eScalability 79\u003c\/p\u003e \u003cp\u003eData Management and Data Governance for AI 80\u003c\/p\u003e \u003cp\u003eSchema-on-Read vs. Schema-on-Write 81\u003c\/p\u003e \u003cp\u003eSummary 84\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 5 A Look Forward on Analytics: Not Everything Can Be a Nail 87\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA Need for Organization 87\u003c\/p\u003e \u003cp\u003eThe Staging Zone 90\u003c\/p\u003e \u003cp\u003eThe Raw Zone 91\u003c\/p\u003e \u003cp\u003eThe Discovery and Exploration Zone 92\u003c\/p\u003e \u003cp\u003eThe Aligned Zone 93\u003c\/p\u003e \u003cp\u003eThe Harmonized Zone 98\u003c\/p\u003e \u003cp\u003eThe Curated Zone 100\u003c\/p\u003e \u003cp\u003eData Topologies 100\u003c\/p\u003e \u003cp\u003eZone Map 103\u003c\/p\u003e \u003cp\u003eData Pipelines 104\u003c\/p\u003e \u003cp\u003eData Topography 105\u003c\/p\u003e \u003cp\u003eExpanding, Adding, Moving, and Removing Zones 107\u003c\/p\u003e \u003cp\u003eEnabling the Zones 108\u003c\/p\u003e \u003cp\u003eIngestion 108\u003c\/p\u003e \u003cp\u003eData Governance 111\u003c\/p\u003e \u003cp\u003eData Storage and Retention 112\u003c\/p\u003e \u003cp\u003eData Processing 114\u003c\/p\u003e \u003cp\u003eData Access 116\u003c\/p\u003e \u003cp\u003eManagement and Monitoring 117\u003c\/p\u003e \u003cp\u003eMetadata 118\u003c\/p\u003e \u003cp\u003eSummary 119\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 6 Addressing Operational Disciplines on the AI Ladder 121\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eA Passage of Time 122\u003c\/p\u003e \u003cp\u003eCreate 128\u003c\/p\u003e \u003cp\u003eStability 128\u003c\/p\u003e \u003cp\u003eBarriers 129\u003c\/p\u003e \u003cp\u003eComplexity 129\u003c\/p\u003e \u003cp\u003eExecute 130\u003c\/p\u003e \u003cp\u003eIngestion 131\u003c\/p\u003e \u003cp\u003eVisibility 132\u003c\/p\u003e \u003cp\u003eCompliance 132\u003c\/p\u003e \u003cp\u003eOperate 133\u003c\/p\u003e \u003cp\u003eQuality 134\u003c\/p\u003e \u003cp\u003eReliance 135\u003c\/p\u003e \u003cp\u003eReusability 135\u003c\/p\u003e \u003cp\u003eThe xOps Trifecta: DevOps\/MLOps, DataOps, and AIOps 136\u003c\/p\u003e \u003cp\u003eDevOps\/MLOps 137\u003c\/p\u003e \u003cp\u003eDataOps 139\u003c\/p\u003e \u003cp\u003eAIOps 142\u003c\/p\u003e \u003cp\u003eSummary 144\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 7 Maximizing the Use of Your Data: Being Value Driven 147\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eToward a Value Chain 148\u003c\/p\u003e \u003cp\u003eChaining Through Correlation 152\u003c\/p\u003e \u003cp\u003eEnabling Action 154\u003c\/p\u003e \u003cp\u003eExpanding the Means to Act 155\u003c\/p\u003e \u003cp\u003eCuration 156\u003c\/p\u003e \u003cp\u003eData Governance 159\u003c\/p\u003e \u003cp\u003eIntegrated Data Management 162\u003c\/p\u003e \u003cp\u003eOnboarding 163\u003c\/p\u003e \u003cp\u003eOrganizing 164\u003c\/p\u003e \u003cp\u003eCataloging 166\u003c\/p\u003e \u003cp\u003eMetadata 167\u003c\/p\u003e \u003cp\u003ePreparing 168\u003c\/p\u003e \u003cp\u003eProvisioning 169\u003c\/p\u003e \u003cp\u003eMulti-Tenancy 170\u003c\/p\u003e \u003cp\u003eSummary 173\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 8 Valuing Data with Statistical Analysis and Enabling Meaningful Access 175\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDeriving Value: Managing Data as an Asset 175\u003c\/p\u003e \u003cp\u003eAn Inexact Science 180\u003c\/p\u003e \u003cp\u003eAccessibility to Data: Not All Users are Equal 183\u003c\/p\u003e \u003cp\u003eProviding Self-Service to Data 184\u003c\/p\u003e \u003cp\u003eAccess: The Importance of Adding Controls 186\u003c\/p\u003e \u003cp\u003eRanking Datasets Using a Bottom-Up Approach for Data Governance 187\u003c\/p\u003e \u003cp\u003eHow Various Industries Use Data and AI 188\u003c\/p\u003e \u003cp\u003eBenefi ting from Statistics 189\u003c\/p\u003e \u003cp\u003eSummary 198\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 9 Constructing for the Long-Term 199\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eThe Need to Change Habits: Avoiding Hard-Coding 200\u003c\/p\u003e \u003cp\u003eOverloading 201\u003c\/p\u003e \u003cp\u003eLocked In 202\u003c\/p\u003e \u003cp\u003eOwnership and Decomposition 204\u003c\/p\u003e \u003cp\u003eDesign to Avoid Change 204\u003c\/p\u003e \u003cp\u003eExtending the Value of Data Through AI 206\u003c\/p\u003e \u003cp\u003ePolyglot Persistence 208\u003c\/p\u003e \u003cp\u003eBenefi ting from Data Literacy 213\u003c\/p\u003e \u003cp\u003eUnderstanding a Topic 215\u003c\/p\u003e \u003cp\u003eSkillsets 216\u003c\/p\u003e \u003cp\u003eIt’s All Metadata 218\u003c\/p\u003e \u003cp\u003eThe Right Data, in the Right Context, with the Right Interface 219\u003c\/p\u003e \u003cp\u003eSummary 221\u003c\/p\u003e \u003cp\u003e\u003cb\u003eChapter 10 A Journey’s End: An IA for AI 223\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eDevelopment Efforts for AI 224\u003c\/p\u003e \u003cp\u003eEssential Elements: Cloud-Based Computing, Data, and Analytics 228\u003c\/p\u003e \u003cp\u003eIntersections: Compute Capacity and Storage Capacity 234\u003c\/p\u003e \u003cp\u003eAnalytic Intensity 237\u003c\/p\u003e \u003cp\u003eInteroperability Across the Elements 238\u003c\/p\u003e \u003cp\u003eData Pipeline Flight Paths: Preflight, Inflight, Postflight 242\u003c\/p\u003e \u003cp\u003eData Management for the Data Puddle, Data Pond, and Data Lake 243\u003c\/p\u003e \u003cp\u003eDriving Action: Context, Content, and Decision-Makers 245\u003c\/p\u003e \u003cp\u003eKeep It Simple 248\u003c\/p\u003e \u003cp\u003eThe Silo is Dead; Long Live the Silo 250\u003c\/p\u003e \u003cp\u003eTaxonomy: Organizing Data Zones 252\u003c\/p\u003e \u003cp\u003eCapabilities for an Open Platform 256\u003c\/p\u003e \u003cp\u003eSummary 260\u003c\/p\u003e \u003cp\u003eAppendix Glossary of Terms 263\u003c\/p\u003e \u003cp\u003eIndex 269\u003c\/p\u003e","brand":"John Wiley \u0026 Sons Inc","offers":[{"title":"Default Title","offer_id":48866411348311,"sku":"9781119693413","price":30.39,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0817\/1739\/5799\/files\/9781119693413.jpg?v=1722278512","url":"https:\/\/bookcurl.com\/products\/smarter-data-science-9781119693413","provider":"Book Curl","version":"1.0","type":"link"}