It's all analytics - part II: designing an integrated AI, analytics and data science architectures for your organization
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| Main Authors: | , , |
|---|---|
| Format: | Book |
| Language: | English |
| Published: |
Boca Raton ; London ; New York
Routledge, Taylor & Francis Group
2022
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| Edition: | First published |
| Subjects: | |
| Online Access: | Inhaltsverzeichnis |
| Physical Description: | XXXV, 265 Seiten Diagramme |
| ISBN: | 9781032066813 |
Staff View
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| 245 | 1 | 0 | |a It's all analytics - part II |b designing an integrated AI, analytics and data science architectures for your organization |c Scott Burk, Ph.D., David E. Sweenor, Gary D. Miner, Ph.D. |
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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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| adam_txt |
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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| id | DE-604.BV047836419 |
| illustrated | Not Illustrated |
| index_date | 2024-07-03T19:10:37Z |
| indexdate | 2024-07-10T09:22:41Z |
| institution | BVB |
| isbn | 9781032066813 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-033219583 |
| oclc_num | 1298743654 |
| open_access_boolean | |
| owner | DE-739 |
| owner_facet | DE-739 |
| physical | XXXV, 265 Seiten Diagramme |
| publishDate | 2022 |
| publishDateSearch | 2022 |
| publishDateSort | 2022 |
| publisher | Routledge, Taylor & Francis Group |
| record_format | marc |
| spelling | Burk, Scott ca. 20./21. Jh. Verfasser (DE-588)1251791255 aut 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. First published Boca Raton ; London ; New York Routledge, Taylor & Francis Group 2022 XXXV, 265 Seiten Diagramme txt rdacontent n rdamedia nc rdacarrier Maschinelles Lernen (DE-588)4193754-5 gnd rswk-swf Data Science (DE-588)1140936166 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 gnd rswk-swf Künstliche Intelligenz (DE-588)4033447-8 s Maschinelles Lernen (DE-588)4193754-5 s Data Science (DE-588)1140936166 s DE-604 Sweenor, David E. ca. 20./21. Jh. Verfasser (DE-588)1251791751 aut Miner, Gary 1942- Verfasser (DE-588)140491813 aut Erscheint auch als Online-Ausgabe, EPUB 978-0-429-34395-7 Digitalisierung UB Passau - ADAM Catalogue Enrichment application/pdf 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 Inhaltsverzeichnis |
| 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 |
| subject_GND | (DE-588)4193754-5 (DE-588)1140936166 (DE-588)4033447-8 |
| 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_exact_search_txtP | 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 |
| url | 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 |
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