It's all analytics - part II: designing an integrated AI, analytics and data science architectures for your organization
Gespeichert in:
| Beteiligte Personen: | , , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Boca Raton ; London ; New York
Routledge, Taylor & Francis Group
2022
|
| Ausgabe: | First published |
| Schlagwörter: | |
| Links: | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033219583&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | XXXV, 265 Seiten Diagramme |
| ISBN: | 9781032066813 |
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| adam_text | Contents Foreword and Tribute to the Authors....................................................... xv Preface..............................................................................................................xvii Authors..............................................................................................................xxi SECTION I 1 DESIGNING FOR ORGANIZATIONAL SUCCESS Some Say It Starts with Data—It Doesn’t.......................................... 3 Introduction....................................................................................................3 Organizational Alignment.............................................................................4 Start with the End in Mind...................................................................... 4 Remove the Cultural Divide and Establish a Center of Excellence...... 9 Innovation-Oriented Cultures.................................................................12 CoE Team Structure................................................................................ 13 Full Service Team Members................................................................13 Functionally Oriented Team Members.............................................. 14 Data and Analytic Project Team Roles.................................................. 14 Data and Analytics Literacy........................................................................ 15 What Is Data Literacy? Data Literacy vs Analytics Literacy...................15 Designing the Organization for Program Success.................................16
Analytics Success Involves More than Technology...................................18 People and Process - Not Merely Technology......................................18 Ethics........................................................................................................ 19 Governance............................................................................................. 20 Technology...................................................................................................20 Data and Analytics Platform Service Areas...........................................20 Data and Analytics Architecture............................................................ 20 Summary......................................................................................................22 References....................................................................................................22 Additional Resources................................................................................... 23
vi ■ Contents 2 The Anatomy of a Business Decision............................................25 The Anatomy of a Business Decision........................................................25 What Is a Business Decision?.................................................................27 The Value of a Decision Which Uses Data and Analytics?................. 28 Before Analytics.................................................................................. 29 After Analytics..................................................................................... 29 Types of Decisions.................................................................................. 30 Strategic Decisions............................................................................... 31 Tactical Decisions................................................................................ 31 Operational Decisions......................................................................... 31 Human vs. Automated Decisions...........................................................32 Speed Is Everything................................................................................ 34 Well Why Does It Matter?....................................................................... 35 Summary......................................................................................................37 References....................................................................................................37 3 Trustworthy AI................................................................................. 39
Introduction..................................................................................................39 Don’t Be Creepy - Be Fair, Unbiased, Explainable, and Transparent.......................................................................................... 40 Creepiness................................................................................................40 Fairness and Bias.................................................................................... 41 Explainable and Transparent..................................................................42 Ethics............................................................................................................42 Framework for Trustworthy Analytics....................................................... 44 Ethical Foundations for Trustworthy AI................................................ 45 Key Requirements for Trustworthy AI.................................................. 48 Other AI Ethical Frameworks..................................................................... 50 Summary...................................................................................................... 51 References.................................................................................................... 51 Additional Resources....................................................................................52 SECTION II DESIGNING FOR DATA SUCCESS 4 Data Design for Success...................................................................55
Introduction.................................................................................................. 55 Why Is Data So Important?......................................................................... 58 Data Is the Cornerstone of Improvement............................................. 58 Processes Are Everywhere...................................................................... 59 The Problem - Issues with Data Continue to Persist.......................... 59
Contents ■ vii Firms Are Failing to Be Data Driven..................................................... 60 Data and Analytics Explosion.................................................................61 On a Personal Note..................................................................................... 62 The Potential of Data=Analytics............................................................... 63 Framework for Data and Analytics - Some Fundamentals..................... 64 The Typical Story of Data Growth, Data Complexity, and Data Needs.......................................................................................64 Data Volume........................................................................................ 68 Data Variety......................................................................................... 68 Data Velocity....................................................................................... 69 Data Value........................................................................................... 69 Data Veracity....................................................................................... 69 The Pieces Are Interdependent and Circular - Keep Looking Forward for Next Generation Data........................................................ 69 The Value of Data and Analytics............................................................... 70 Data and Analytics Literacy Are Requirements to Successful Programs......................................................................................................72
Summary......................................................................................................73 How This Part Is Organized...............................................................74 References....................................................................................................74 Additional Resources................................................................................... 75 Data and Analytics Literacy References.................................................75 Additional Terms Related to This Chapter............................................76 Process and Data Quality References.................................................... 76 5 Data in Motion, Data Pipes, APIs, Microservices, Streaming, Events, and More..........................................................77 Introduction................................................................................................. 77 APIs and Microservices...............................................................................80 The Five Architectural Constraints of REST APIs................................ 82 Other APIs - RPC and SOAP................................................................. 83 API Benefits and Drawbacks................................................................. 84 Benefits (Primarily to Developers).....................................................84 Drawbacks........................................................................................... 84
Microservices............................................................................................... 85 Microservice Benefits and Drawbacks..................................................86 Benefits................................................................................................ 86 Drawbacks........................................................................................... 87 Events, Event-Driven Architectures and Streaming................................. 87
viii ■ Contents Some Drivers and Examples of Events, Streaming Events, and CEP (Complex Event Processing)....................................................... 90 IoT Is a Big Driver of Real-Time Events................................................ 90 Event Processing Advantages......................................................................92 How Businesses Benefit from Event Processing...................................93 Improved Customer Service................................................................94 Reduction of Costs and More Efficient Use of Resources................94 Optimized Operations........................................................................94 ETL and ELT.................................................................................................95 Summary......................................................................................................96 References....................................................................................................97 Additional Resources................................................................................... 97 Basic Terms Useful in This Chapter...................................................... 97 Additional Relevant Terms......................................................................97 6 Data Stores, Warehouses, Big Data, Lakes, and Cloud Data......99 Introduction..................................................................................................99 Why Data Is so Crucial to the Success of an Enterprise........................ 101 Data
Storage - Two Designations - Volatile and Nonvolatile Memory...................................................................................................... 103 Primer on Data Structures and Formats...................................................104 Data Stores Topology.................................................................................105 Local File Systems and Network Data Storage.................................... 106 Operational Data Stores........................................................................ 107 Data Marts and the EDWs..................................................................... 108 Benefits and Drawbacks of the EDW...................................................109 Benefits of an EDW........................................................................... 109 Drawbacks of an EDW...................................................................... 109 Cluster Computing and Big Data.............................................................. Ill What Is Big Data?................................................................................... Ill Big Data as a Concept........................................................................Ill Big Data as a Technology......................................................................112 Why the Push to Big Data? Why Is Big Data Technology Attractive for Data Science?................................................................... 113 Pivotal Changes in Big Data Technology............................................. 114 Optimized Big
Data............................................................................... 117 Cloud Data - What It Is, What You Can Do, Benefits, and Drawbacks.............................................................................................. 117 Cloud Benefits and Drawbacks............................................................ 118
Contents ■ ix Cloud Storage....................................................................................120 “Other Big Data Promises”, Data Lakes, Data Swamps, Reservoirs, Muddy Water, Analytic Sandboxes, and Whatever We Can Think to Call It Tomorrow............................................................................... 120 Summary.................................................................................................... 121 References.................................................................................................. 121 Additional Resources................................................................................. 122 Data Lakes and Architecture.................................................................... 122 Some Terms to Consider Exploring..................................................... 123 7 Data Virtualization........................................................................ 125 Introduction................................................................................................ 125 The Typical Story of Data Growth, Data Complexity, and Data Needs......................................................................................126 DV- What Is It?.........................................................................................127 A Platform Connecting to Hundreds of Data Sources.......................128 A Platform with Searchable Data and Rich Metadata........................128 A Collaboration Tool for Functional Areas and Users.......................129 A Pathway for New Systems and
System Migration............................129 An IT Tool for Rapid Prototyping.........................................................129 A System for Enhanced Security of Data............................................ 129 The Continuing Quest for the “Single Versions of the Truth” Motivation beyond the EDW................................................................ 131 What Are the Advantages of DV?........................................................ 134 A Sustainable Architecture for the Ever-Increasing Complexity of Data........................................................................... 134 Simplified User Experience...............................................................136 More Collaborative and Productive User Experience.................... 136 Data in Near Real Time..................................................................... 136 Source Data and Combine Data Easily............................................ 137 No Need to Replicate and Make Physical Copies of Data............ 137 Improved Security and Administration........................................... 137 Positive Impact on the EDW, IT, and the Business........................137 Governance and Data Quality..........................................................137 DV Is Scalable - Scales Up and Scales Out....................................138 Enabling Future Data and Even Technology...................................138 What Are the Drawbacks of DV?..........................................................140 Some of the Major Disadvantages of
DV......................................... 140 Are You Ready for DV?......................................................................... 142
x ■ Contents Summary.................................................................................................... 142 References.................................................................................................. 142 Additional Resources..................................................................................143 8 Data Governance and Data Management........................................145 Introduction................................................................................................ 145 Data Governance - Policies, Procedure, and Process............................ 146 Goals of Data Governance....................................................................... 148 Data Integrity.......................................................................................... 150 Data Security.......................................................................................... 150 Data Consistency.................................................................................... 151 Data Confidence..................................................................................... 151 Compliance to Regulations, Data Privacy Laws...................................151 Adherence to Organizational Ethics and Standards............................ 152 Risk Management of Data Leakage...................................................... 152 Data Distribution.................................................................................... 152 Value of Good
Data...............................................................................153 Moving Data Quality Upstream Reduces Costs.................................. 153 Data Literacy Education.........................................................................154 Technology to Support Data Management and Governance.................154 Data Management..................................................................................154 Master Data............................................................................................ 155 Reference Data....................................................................................... 156 Data Quality........................................................................................... 156 Security................................................................................................... 157 Summary.................................................................................................... 158 References.................................................................................................. 158 Additional Resources..................................................................................158 Some Terms Related to This Chapter to Consider Exploring.............159 Data Quality Resource...........................................................................159 9 Miscellanea - Curated, Purchased, Nascent, and Future Data..................................................................................... 161
Introduction................................................................................................ 161 Data Outside Your Organization.............................................................. 162 Supplemental Data.................................................................................163 Meaningful Data..................................................................................... l63 Data for Free.............................................................................................. 164 Publically Available Data.......................................................................l65 Data Available from Commercial Entities and Universities....................165
Contents ■ xi Data for Sale............................................................................................... 167 Data Syndicators.....................................................................................168 Data Brokers...........................................................................................168 Data Exchange and Data Exchange Platforms.................................... 169 Data Marketplaces................................................................................. 169 Should You Monetize Your Data?............................................................. 170 Future Data................................................................................................ 170 Keep an Eye Out for Nascent Technologies and Trends in Applications of Analytics.................................................................. 171 GIS and Geo Analytics.......................................................................... 171 Graph Databases....................................................................................171 Time Series Databases.......................................................................... 172 Today Is the Time to Start Collecting Data for the Future................ 172 Data Strategy and Data Paradigms.......................................................173 Summary.................................................................................................... 174 References.................................................................................................. 174 Additional
Resources..................................................................................175 What Is DataOps?...................................................................................175 SECTION III 10 DESIGNING FOR ANALYTICS SUCCESS Technology to Create Analytics.................................................................179 Introduction................................................................................................ 179 Analytics Maturity..................................................................................... 180 Architectural Considerations for the Data Scientist................................186 Data Discovery and Acquisition.......................................................... 188 Exploratory Data Analysis..................................................................... 189 Data Preparation....................................................................................190 Feature Engineering.............................................................................. 190 Model Build and Selection....................................................................190 Model Evaluation and Testing............................................................... 191 Model Deployment.................................................................................191 Model Monitoring...................................................................................192 Legality and Ethical Use of Data..........................................................192 Automation and
ML.................................................................................. 192 The Real World is Different than University........................................... 193 Do You Know How to Bake Bread?........................................................196 Analytical Capabilities and Architectural Considerations.......................197 Data Management as a Prerequisite.................................................... 198
xii ■ Contents Starting with the Data....................................................................... 198 Starting with the Analytics................................................................ 199 Data and Analytics Architecture........................................................... 199 Data Sources.......................................................................................199 Data Management............................................................................. 201 Analytics.................................................................................................202 Model Building.................................................................................. 202 Reporting and Dashboards...............................................................202 Data Science...................................................................................... 203 AI, ML, Deep Learning - Oh My!.................................................... 204 Model Training.................................................................................. 206 Model Inference................................................................................ 207 Model Management........................................................................... 208 Governance....................................................................................... 208 Streaming Analytics................................................................................210 IoT and Edge Analytics......................................................................... 210
Cloud Ecosystems and Frameworks.....................................................212 A Few Example Architectures.................................................................. 212 Uber........................................................................................................ 212 Facebook................................................................................................ 214 An Evolution of CRISP-DM................................................................... 214 Feature Stores............................................................................................. 214 Technology................................................................................................. 219 Cost Considerations...................................................................................219 Other Open Source Considerations......................................................221 Technical Debt in Data Scienceand ML...................................................222 Model Dependencies............................................................................ 222 Data Dependencies............................................................................... 223 Feedback................................................................................................223 Anti-patterns or Poor CodingHabits.....................................................223 Summary.................................................................................................... 224
References.................................................................................................. 225 Additional Resources................................................................................. 227 11 Technology to Communicateand Act Upon Analytics............... 229 Introduction................................................................................................229 An Analytics Confluence.......................................................................... 230 Data Storytelling.........................................................................................231 Building an Analytics Culture................................................................... 235
Contents ■ xiii Model Ops.................................................................................................. 236 How Is Analytics Different?..................................................................236 Why Does an Organization Need Model Ops?...................................238 Model Ops Capabilities......................................................................... 239 Model Visibility.................................................................................. 239 Model Repository.............................................................................. 239 Model Performance Metrics............................................................. 240 Contextualized Collaboration Framework.......................................241 Governance........................................................................................241 Summary.................................................................................................... 241 References..................................................................................................242 Additional Resources................................................................................. 243 Keywords...................................................................................................243 12 To Build, Buy, or Outsource Analytics Platform.......................245 Introduction................................................................................................243 Analytics Infrastructure Components......................................................
246 What Really Matters (In Your Business)?................................................. 248 Build vs. Buy Considerations....................................................................249 Strategy and Competitive Advantage................................................... 249 Costs....................................................................................................... 249 Scale and Complexity........................................................................... 250 Commoditization, Flexibility, and Change.......................................... 250 Time........................................................................................................ 250 In-House Expertise............................................................................... 251 Risks........................................................................................................ 251 Support Structure.................................................................................. 252 Operational Factors............................................................................... 252 Intellectual Property.............................................................................. 252 Outsourcing................................................................................................252 Build vs. Buy vs. Outsource Guidelines.................................................. 252 Summary.................................................................................................... 253
References..................................................................................................254 Additional Resources................................................................................. 254 Index 255
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| spellingShingle | Burk, Scott ca. 20./21. Jh Sweenor, David E. ca. 20./21. Jh Miner, Gary 1942- It's all analytics - part II designing an integrated AI, analytics and data science architectures for your organization Maschinelles Lernen (DE-588)4193754-5 gnd Data Science (DE-588)1140936166 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
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| title | It's all analytics - part II designing an integrated AI, analytics and data science architectures for your organization |
| title_auth | It's all analytics - part II designing an integrated AI, analytics and data science architectures for your organization |
| title_exact_search | It's all analytics - part II designing an integrated AI, analytics and data science architectures for your organization |
| title_full | It's all analytics - part II designing an integrated AI, analytics and data science architectures for your organization Scott Burk, Ph.D., David E. Sweenor, Gary D. Miner, Ph.D. |
| title_fullStr | It's all analytics - part II designing an integrated AI, analytics and data science architectures for your organization Scott Burk, Ph.D., David E. Sweenor, Gary D. Miner, Ph.D. |
| title_full_unstemmed | It's all analytics - part II designing an integrated AI, analytics and data science architectures for your organization Scott Burk, Ph.D., David E. Sweenor, Gary D. Miner, Ph.D. |
| title_short | It's all analytics - part II |
| title_sort | it s all analytics part ii designing an integrated ai analytics and data science architectures for your organization |
| title_sub | designing an integrated AI, analytics and data science architectures for your organization |
| topic | Maschinelles Lernen (DE-588)4193754-5 gnd Data Science (DE-588)1140936166 gnd Künstliche Intelligenz (DE-588)4033447-8 gnd |
| topic_facet | Maschinelles Lernen Data Science Künstliche Intelligenz |
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