Data science and analytics strategy: an emergent design approach
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton ; London ; New York
CRC Press
2023
|
| Schriftenreihe: | Data science series
|
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | xxii, 204 Seiten |
| ISBN: | 9781032196329 9781032196336 |
Internformat
MARC
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Datensatz im Suchindex
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|---|---|
| adam_text | Contents Foreword.............................................................................................................. xiii Preface..................................................................................................................... xv Acknowledgements........................................................................................... xvii Contributors.......................................................................................................... xxi 1. Introduction...................................................................................................... 1 Data Science as a Sociotechnical Capability.................................................. 1 The Fallacy of Strategic Alignment................................................................ 1 A Tale of Two Databases.................................................................................. 3 The Wickedness of Building Data Capabilities............................................. 4 The Notion of Emergent Design..................................................................... 6 What to Expect from This Book...................................................................... 8 The Structure of the Book................................................................................ 9 Notes................................................................................................................. 10 References......................................................................................................... 10 2. What Is Data
Science?................................................................................... 13 The Data Analytics Stack............................................................................... 13 Data Ingestion.................................................................................................. 14 Storage.............................................................................................................. 16 Access................................................................................................................ 19 BI vs Analytics vs Data Science..................................................................... 20 Are You Ready for Data Science?.................................................................. 24 The Data Science Process............................................................................... 26 Doing the Thing...................................................................................... 27 Machine Learning Problem Types................................................ 28 Test Your Knowledge...................................................................... 31 What about AI?................................................................................ 33 Great Power, Narrow Focus........................................................... 34 Doing the Thing Right........................................................................... 35 Doing the Right Thing........................................................................... 38 In
Closing......................................................................................................... 39 Notes^................................................................................................................. 40 References......................................................................................................... 41 3. The Principles of Emergent Design............................................................ 43 The Origins of Emergent Design.................................................................. 43 Is There a Better Way?............................................................................ 44 Emergent Design, Evolution, and Learning................................................ 44 Uncertainty and Ambiguity........................................................................... 48
Guidelines for Emergent Design.................................................... 51 Be a Midwife Rather than an Expert............................................... 51 Use Conversations to Gain Commitment....................................... 51 Understand and Address Concerns of Stakeholders Who Are Wary of the Change......................................................... 52 Frame the Current Situation as an Enabling Constraint................. 53 Consider Long-Term and Hidden Consequences........................... 54 Create an Environment that Encourages Learning..........................55 Beware of Platitudinous Goals........................................................55 Act So as to Increase Future Choices.............................................. 56 Putting Emergent Design to Work - An Illustrative Case Study............ 56 Background......................................................................................58 The Route to Emergent Design........................................................58 A Pivotal Conversation................................................................... 59 First Steps........................................................................................ 60 Integrating the New Capability.......................................................61 The Pilot Project........................................................................... 61 Scaling Up....................................................................................... 62 The Official
OK............................................................................... 63 Lessons Learnt................................................................................. 64 Summarising........................................................................................... 65 Notes....................................................................................................... 65 References................................................................................................66 4. Charting a Course.................................................................................. 69 Introduction............................................................................................ 69 Tackling the Corporate Immune System................................................ 70 Finding Problems.................................................................................... 71 Demonstrating Value.............................................................................. 75 Additional Benefits of Problem Finding .............................................. 77 Powerful Questions................................................................................ 79 Designing for the Future......................................................................... 80 Notes....................................................................................................... 81 References................................................................................................82 5. Capability and
Culture.......................................................................... 83 Introduction............................................................................................ 83 Data Talent Archetypes........................................................................... 84 Database Administrators, Data Engineers, and Data Warehouse Architects...................................................................... 84 Key Skills.................................................................................. 85 What They Do........................................................................... 85 What They Don t Do................................................................. 86 Business Intelligence (BI) Developers and Analysts........................ 86
Key Skills.......................................................................................... 86 What They Do.................................................................................. 86 What They Don t Do....................................................................... 87 From Analyst to Data Scientist............................................................. 87 Key Skills.......................................................................................... 87 What They Can Do for You............................................................ 88 What They Don t Do....................................................................... 88 Newer Data Roles................................................................................... 89 Other Roles.............................................................................................. 90 The Right Timing............................................................................................. 90 Building Capability......................................................................................... 92 Identifying and Growing Talent........................................................... 93 Designing Training Programmes.................................................. 94 Immersion and Other Approaches............................................... 95 Ongoing Development; Building a Culture ............................................ 98 Communities of Practice........................................................................ 99 Data
Literacy......................................................................................... 101 Critical Thinking............................................................................ 102 Problems, Hypotheses, and the Scientific Method................... 103 Measuring Culture............................................................................... 105 Some Principles for Developing a Data Culture.............................. 106 Buying Talent................................................................................................. 108 Find the Right Mix of Skills................................................................. 108 Value Problem-Solving........................................................................ 109 Assessing Broader Skills....................................................................... Ill Get the Candidate to Work on a Real Data Problem................. Ill Get Them to Do a Presentation.....................................................112 Match Expectations...............................................................................113 Consider a Broader Talent Pool...........................................................113 Conditions over Causes............................................................................... 114 Closing Remarks........................................................................................... 116 Notes................................................................................................................117
References....................................................................................................... 117 6. Technical Choices......................................................................................... 119 Introduction................................................................................................... 119 Cloud and the Future (Is Now)................................................................... 119 Cost Savings.......................................................................................... 120 Simplicity of Setup............................................................................... 121 Security and Governance..................................................................... 121 Backups.................................................................................................. 122 The Main Players........................................................................................... 122 API Services........................................................................................... 124 Code vs User-Centricity....................................................................... 125
Guiding Principles............................................................................... ՚........ 128 Reducing Complexity.......................................................................... 128 Blocking Innovation............................................................................. 129 Interoperability..................................................................................... 129 Usability................................................................................................. 130 The Effort to Keep Things Running................................................... 131 Transparency......................................................................................... 132 Working with Vendors................................................................................. 132 Initial Discussions................................................................................. 133 Making the Choice................................................................................ 133 Taking Root...and Growing (the Wrong and Right Way)............... 136 Concluding Remarks.................................................................................... 137 Notes............................................................................................................... 137 References....................................................................................................... 138 7. Doing Data Science: From Planning to Production............................... 139
Introduction................................................................................................... 139 The Machine Learning Workflow............................................................... 140 The MĽWF in Depth..................................................................................... 141 Problem Formulation and Context Understanding......................... 142 Data Engineering.................................................................................. 149 Model Development and Explainability........................................... 153 Deployment, Monitoring, and Maintenance.................................... 155 Dialling It Down............................................................................................ 159 Concluding Thoughts................................................................................... 160 Notes............................................................................................................... 161 References....................................................................................................... 162 8. Doing the Right Thing................................................................................ 163 Does Data Speak for Itself?.......................................................................... 163 Might Do Now, Must Do Later?................................................................. 165 Responsible AI............................................................................................... 168 Data and AI
Governance.............................................................................. 170 Data Governance and Data Management......................................... 170 Getting Started with Data Governance............................................. 171 How Do You Figure Out a Direction?................................................ 172 Data Stewards and Councils............................................................... 173 What about AI Governance?............................................................... 174 Trust........................................................................................................ 174 Explainability and Transparency................................................. 175 Bias and Fairness........................................................................... 176 Acting Ethically............................................................................................. 180 Find What Matters................................................................................ 182 Build in Diversity.................................................................................. 183 Establish Meaningful Rituals.............................................................. 184
Continual Awareness........................................................................... 185 Leverage Existing Tools (to a Point!).................................................. 186 In Closing....................................................................................................... 186 Notes............................................................................................................... 187 References....................................................................................................... 188 9. Coda................................................................................................................ 191 The Principles of Emergent Design Redux................................................ 191 Selling the Strategy; Choosing Your Adventure....................................... 192 Example 1: As a Means to Enable Data-Supported Decision-Making................................................................................... 192 Example 2: As a Means to Support the Organisation s Strategy.......193 Quo Vadis?..................................................................................................... 194 Note................................................................................................................. 195 Index..................................................................................................................... 197
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| adam_txt |
Contents Foreword. xiii Preface. xv Acknowledgements. xvii Contributors. xxi 1. Introduction. 1 Data Science as a Sociotechnical Capability. 1 The Fallacy of Strategic Alignment. 1 A Tale of Two Databases. 3 The Wickedness of Building Data Capabilities. 4 The Notion of Emergent Design. 6 What to Expect from This Book. 8 The Structure of the Book. 9 Notes. 10 References. 10 2. What Is Data
Science?. 13 The Data Analytics Stack. 13 Data Ingestion. 14 Storage. 16 Access. 19 BI vs Analytics vs Data Science. 20 Are You Ready for Data Science?. 24 The Data Science Process. 26 Doing the Thing. 27 Machine Learning Problem Types. 28 Test Your Knowledge. 31 What about AI?. 33 Great Power, Narrow Focus. 34 Doing the Thing Right. 35 Doing the Right Thing. 38 In
Closing. 39 Notes^. 40 References. 41 3. The Principles of Emergent Design. 43 The Origins of Emergent Design. 43 Is There a Better Way?. 44 Emergent Design, Evolution, and Learning. 44 Uncertainty and Ambiguity. 48
Guidelines for Emergent Design. 51 Be a Midwife Rather than an Expert. 51 Use Conversations to Gain Commitment. 51 Understand and Address Concerns of Stakeholders Who Are Wary of the Change. 52 Frame the Current Situation as an Enabling Constraint. 53 Consider Long-Term and Hidden Consequences. 54 Create an Environment that Encourages Learning.55 Beware of Platitudinous Goals.55 Act So as to Increase Future Choices. 56 Putting Emergent Design to Work - An Illustrative Case Study. 56 Background.58 The Route to Emergent Design.58 A Pivotal Conversation. 59 First Steps. 60 Integrating the New Capability.61 The "Pilot" Project. 61 Scaling Up. 62 The Official
OK. 63 Lessons Learnt. 64 Summarising. 65 Notes. 65 References.66 4. Charting a Course. 69 Introduction. 69 Tackling the Corporate Immune System. 70 Finding Problems. 71 Demonstrating Value. 75 Additional Benefits of "Problem Finding". 77 Powerful Questions. 79 Designing for the Future. 80 Notes. 81 References.82 5. Capability and
Culture. 83 Introduction. 83 Data Talent Archetypes. 84 Database Administrators, Data Engineers, and Data Warehouse Architects. 84 Key Skills. 85 What They Do. 85 What They Don't Do. 86 Business Intelligence (BI) Developers and Analysts. 86
Key Skills. 86 What They Do. 86 What They Don't Do. 87 From Analyst to Data Scientist. 87 Key Skills. 87 What They Can Do for You. 88 What They Don't Do. 88 Newer Data Roles. 89 Other Roles. 90 The Right Timing. 90 Building Capability. 92 Identifying and Growing Talent. 93 Designing Training Programmes. 94 Immersion and Other Approaches. 95 Ongoing Development; "Building a Culture". 98 Communities of Practice. 99 Data
Literacy. 101 Critical Thinking. 102 Problems, Hypotheses, and the Scientific Method. 103 Measuring Culture. 105 Some Principles for Developing a Data Culture. 106 Buying Talent. 108 Find the Right Mix of Skills. 108 Value Problem-Solving. 109 Assessing Broader Skills. Ill Get the Candidate to Work on a Real Data Problem. Ill Get Them to Do a Presentation.112 Match Expectations.113 Consider a Broader Talent Pool.113 Conditions over Causes. 114 Closing Remarks. 116 Notes.117
References. 117 6. Technical Choices. 119 Introduction. 119 Cloud and the Future (Is Now). 119 Cost Savings. 120 Simplicity of Setup. 121 Security and Governance. 121 Backups. 122 The Main Players. 122 API Services. 124 Code vs User-Centricity. 125
Guiding Principles. ՚. 128 Reducing Complexity. 128 Blocking Innovation. 129 Interoperability. 129 Usability. 130 The Effort to Keep Things Running. 131 Transparency. 132 Working with Vendors. 132 Initial Discussions. 133 Making the Choice. 133 Taking Root.and Growing (the Wrong and Right Way). 136 Concluding Remarks. 137 Notes. 137 References. 138 7. Doing Data Science: From Planning to Production. 139
Introduction. 139 The Machine Learning Workflow. 140 The MĽWF in Depth. 141 Problem Formulation and Context Understanding. 142 Data Engineering. 149 Model Development and Explainability. 153 Deployment, Monitoring, and Maintenance. 155 Dialling It Down. 159 Concluding Thoughts. 160 Notes. 161 References. 162 8. Doing the Right Thing. 163 Does Data Speak for Itself?. 163 Might Do Now, Must Do Later?. 165 Responsible AI. 168 Data and AI
Governance. 170 Data Governance and Data Management. 170 Getting Started with Data Governance. 171 How Do You Figure Out a Direction?. 172 Data Stewards and Councils. 173 What about AI Governance?. 174 Trust. 174 Explainability and Transparency. 175 Bias and Fairness. 176 Acting Ethically. 180 Find What Matters. 182 Build in Diversity. 183 Establish Meaningful Rituals. 184
Continual Awareness. 185 Leverage Existing Tools (to a Point!). 186 In Closing. 186 Notes. 187 References. 188 9. Coda. 191 The Principles of Emergent Design Redux. 191 Selling the Strategy; Choosing Your Adventure. 192 Example 1: As a Means to Enable Data-Supported Decision-Making. 192 Example 2: As a Means to Support the Organisation's Strategy.193 Quo Vadis?. 194 Note. 195 Index. 197 |
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| index_date | 2024-07-03T21:55:03Z |
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| spelling | Awati, Kailash Verfasser (DE-588)1020393092 aut Data science and analytics strategy an emergent design approach Kailash Awati, Alexander Scriven Boca Raton ; London ; New York CRC Press 2023 xxii, 204 Seiten txt rdacontent n rdamedia nc rdacarrier Data science series Scriven, Alexander Verfasser aut Erscheint auch als Online-Ausgabe 978-1-003-26015-8 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=034181757&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Awati, Kailash Scriven, Alexander Data science and analytics strategy an emergent design approach |
| title | Data science and analytics strategy an emergent design approach |
| title_auth | Data science and analytics strategy an emergent design approach |
| title_exact_search | Data science and analytics strategy an emergent design approach |
| title_exact_search_txtP | Data science and analytics strategy an emergent design approach |
| title_full | Data science and analytics strategy an emergent design approach Kailash Awati, Alexander Scriven |
| title_fullStr | Data science and analytics strategy an emergent design approach Kailash Awati, Alexander Scriven |
| title_full_unstemmed | Data science and analytics strategy an emergent design approach Kailash Awati, Alexander Scriven |
| title_short | Data science and analytics strategy |
| title_sort | data science and analytics strategy an emergent design approach |
| title_sub | an emergent design approach |
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