Dark data why what you don't know matters
"In the era of big data, it is easy to imagine that we have all the information we need to make good decisions. But in fact the data we have are never complete, and may be only the tip of the iceberg. Just as much of the universe is composed of dark matter, invisible to us but nonetheless prese...
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| 505 | 8 | |a Part I: Dark data: their origins and consequences. Dark data: what we don't see shapes our world. The ghost of data ; So you think you have all the data? ; Nothing happened, so we ignored it ; The power of dark data ; All around us -- Discovering dark data: what we collect and what we don't. Dark data on all sides ; Data exhaust, selection, and self-selection ; From the few to the many ; Experimental data ; Beware human frailties -- Definitions and dark data: what do you want to know?. Different definitions and measuring the wrong thing ; You can't measure everything ; Screening ; Selection on the basis of past performance -- Unintentional dark data: saying one thing, doing another. The big picture ; Summarizing ; Human error ; Instrument limitations ; Linking data sets -- Strategic dark data: gaming, feedback, and information asymmetry. Gaming ; Feedback ; Information Asymmetry ; Adverse selection and algorithms -- Intentional dark data: fraud and deception. Fraud ; Identity theft and internet fraud ; Personal financial fraud ; Financial market fraud and insider trading ; Insurance fraud ; And more -- Science and dark data: the nature of discovery. The nature of science ; If only I'd known that ; Tripping over dark data ; Dark data and the big picture ; Hiding the facts ; Retraction ; Provenance and trustworthiness: who told you that? -- | |
| 505 | 8 | |a Part II: Illuminating and using dark data. Dealing with dark data: shining a light. Hope! ; Linking observed and missing data ; Working with the data we have ; Going beyond the data: what if you die first? ; Going beyond the data: imputation ; Iteration ; Wrong number! -- Benefiting from dark data: reframing the question. Hiding data ; Hiding data from ourselves: randomized controlled trials ; What might have been ; Replicated data ; Imaginary data: the Bayesian Prior ; Privacy and confidentiality preservation ; Collecting data in the dark -- Classifying dark data: a route through the maze. A taxonomy of dark data ; Illumination | |
| 520 | |a "In the era of big data, it is easy to imagine that we have all the information we need to make good decisions. But in fact the data we have are never complete, and may be only the tip of the iceberg. Just as much of the universe is composed of dark matter, invisible to us but nonetheless present, the universe of information is full of dark data that we overlook at our peril. In Dark Data, data expert David Hand takes us on a fascinating and enlightening journey into the world of the data we don’t see. Dark Data explores the many ways in which we can be blind to missing data and how that can lead us to conclusions and actions that are mistaken, dangerous, or even disastrous. Examining a wealth of real-life examples, from the Challenger shuttle explosion to complex financial frauds, Hand gives us a practical taxonomy of the types of dark data that exist and the situations in which they can arise, so that we can learn to recognize and control for them. In doing so, he teaches us not only to be alert to the problems presented by the things we don’t know, but also shows how dark data can be used to our advantage, leading to greater understanding and better decisions. Today, we all make decisions using data. Dark Data shows us all how to reduce the risk of making bad ones." -- | ||
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CONTENTS Preface xi PORT 1: ОШ ORTO : THEIR ORIGINS RNĐ CONSEQUENCES chapter 1: Dark Data: What We Don’t See Shapes Our World 3 3 The Ghost ofData So You Think You Have All the Data ? 12 Nothing Happened, So We Ignored It 17 The Power of Dark Data гг All around Us 24 Discovering Dark Data: What We Collect and What We Don’t chapter 2: 28 Dark Data on All Sides 28 Data Exhaust, Selection, and Self-Selection 31 From the Few to the Many 43 Experimental Data 56 Beware Human Frailties 67 Definitions and Dark Data: What Do You Want to Know? 72 Different Definitions and Measuring the Wrong Thing 72 chapters: vii
viii CONTENTS You Can't Measure Everything 8o Screening 90 Selection on the Basis ofPast Performance 94 Unintentional Dark Data: Saying One Thing, Doing Another CHAPTER4: The Big Picture 98 98 Summarizing 102 Human Error 103 Instrument Limitations 108 Linking Data Sets ա Strategic Dark Data: Gaming, Feedback, and Information Asymmetry 114 chapters: Gaming 114 Feedback 122 Information Asymmetry 128 Adverse Selection and Algorithms 130 CHAPTERS: Intentional Dark Data: Fraud and Deception 140 Fraud 140 Identity Theft and Internet Fraud 144 Personal Financial Fraud 149 Financial Market Fraud and Insider Trading 153 Insurance Fraud 158 And More 163
CONTENTS ІХ Science and Dark Data: The Nature of Discovery chapter?: 167 The Nature of Science 167 If Only I'd Known That 172 Tripping over Dark Data 181 Dark Data and the Big Picture 184 Hiding the Facts 199 Retraction 215 Provenance and Trustworthiness: Who Told You That? 217 PART II: ÍLLUMINATING AND USING 0AAH DATA chapter 8: Dealing with Dark Data: Shining a Light 223 Hope! 223 Linking Observed and Missing Data 224 Identifying the Missing Data Mechanism 233 Working with the Data We Have 236 Going Beyond the Data: What If You Die First? 241 Going Beyond the Data: Imputation 245 Iteration 252 Wrong Number! 256 Benefiting from Dark Data: Reframing the Question 262 chapter9; Hiding Data 262
X CONTENTS Hiding Data from Ourselves: Randomized Controlled Trials 263 What Might Have Been 265 Replicated Data 269 Imaginary Data: The Bayesian Prior 276 Privacy and Confidentiality Preservation 278 Collecting Data in the Dark 287 chapter 10: Classifying Dark Data: A Route through the Maze 291 A Taxonomy of Dark Data 291 $ 298 Notes 307 Index 319 |
| adam_txt |
CONTENTS Preface xi PORT 1: ОШ ORTO : THEIR ORIGINS RNĐ CONSEQUENCES chapter 1: Dark Data: What We Don’t See Shapes Our World 3 3 The Ghost ofData So You Think You Have All the Data ? 12 Nothing Happened, So We Ignored It 17 The Power of Dark Data гг All around Us 24 Discovering Dark Data: What We Collect and What We Don’t chapter 2: 28 Dark Data on All Sides 28 Data Exhaust, Selection, and Self-Selection 31 From the Few to the Many 43 Experimental Data 56 Beware Human Frailties 67 Definitions and Dark Data: What Do You Want to Know? 72 Different Definitions and Measuring the Wrong Thing 72 chapters: vii
viii CONTENTS You Can't Measure Everything 8o Screening 90 Selection on the Basis ofPast Performance 94 Unintentional Dark Data: Saying One Thing, Doing Another CHAPTER4: The Big Picture 98 98 Summarizing 102 Human Error 103 Instrument Limitations 108 Linking Data Sets ա Strategic Dark Data: Gaming, Feedback, and Information Asymmetry 114 chapters: Gaming 114 Feedback 122 Information Asymmetry 128 Adverse Selection and Algorithms 130 CHAPTERS: Intentional Dark Data: Fraud and Deception 140 Fraud 140 Identity Theft and Internet Fraud 144 Personal Financial Fraud 149 Financial Market Fraud and Insider Trading 153 Insurance Fraud 158 And More 163
CONTENTS ІХ Science and Dark Data: The Nature of Discovery chapter?: 167 The Nature of Science 167 If Only I'd Known That 172 Tripping over Dark Data 181 Dark Data and the Big Picture 184 Hiding the Facts 199 Retraction 215 Provenance and Trustworthiness: Who Told You That? 217 PART II: ÍLLUMINATING AND USING 0AAH DATA chapter 8: Dealing with Dark Data: Shining a Light 223 Hope! 223 Linking Observed and Missing Data 224 Identifying the Missing Data Mechanism 233 Working with the Data We Have 236 Going Beyond the Data: What If You Die First? 241 Going Beyond the Data: Imputation 245 Iteration 252 Wrong Number! 256 Benefiting from Dark Data: Reframing the Question 262 chapter9; Hiding Data 262
X CONTENTS Hiding Data from Ourselves: Randomized Controlled Trials 263 What Might Have Been 265 Replicated Data 269 Imaginary Data: The Bayesian Prior 276 Privacy and Confidentiality Preservation 278 Collecting Data in the Dark 287 chapter 10: Classifying Dark Data: A Route through the Maze 291 A Taxonomy of Dark Data 291 $ 298 Notes 307 Index 319 |
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| author | Hand, D. J. 1950- |
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| contents | Part I: Dark data: their origins and consequences. Dark data: what we don't see shapes our world. The ghost of data ; So you think you have all the data? ; Nothing happened, so we ignored it ; The power of dark data ; All around us -- Discovering dark data: what we collect and what we don't. Dark data on all sides ; Data exhaust, selection, and self-selection ; From the few to the many ; Experimental data ; Beware human frailties -- Definitions and dark data: what do you want to know?. Different definitions and measuring the wrong thing ; You can't measure everything ; Screening ; Selection on the basis of past performance -- Unintentional dark data: saying one thing, doing another. The big picture ; Summarizing ; Human error ; Instrument limitations ; Linking data sets -- Strategic dark data: gaming, feedback, and information asymmetry. Gaming ; Feedback ; Information Asymmetry ; Adverse selection and algorithms -- Intentional dark data: fraud and deception. Fraud ; Identity theft and internet fraud ; Personal financial fraud ; Financial market fraud and insider trading ; Insurance fraud ; And more -- Science and dark data: the nature of discovery. The nature of science ; If only I'd known that ; Tripping over dark data ; Dark data and the big picture ; Hiding the facts ; Retraction ; Provenance and trustworthiness: who told you that? -- Part II: Illuminating and using dark data. Dealing with dark data: shining a light. Hope! ; Linking observed and missing data ; Working with the data we have ; Going beyond the data: what if you die first? ; Going beyond the data: imputation ; Iteration ; Wrong number! -- Benefiting from dark data: reframing the question. Hiding data ; Hiding data from ourselves: randomized controlled trials ; What might have been ; Replicated data ; Imaginary data: the Bayesian Prior ; Privacy and confidentiality preservation ; Collecting data in the dark -- Classifying dark data: a route through the maze. A taxonomy of dark data ; Illumination |
| ctrlnum | (OCoLC)1357529834 (DE-599)BVBBV048551280 |
| discipline | Informatik |
| discipline_str_mv | Informatik |
| format | Book |
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| spelling | Hand, D. J. 1950- Verfasser (DE-588)123535492 aut Dark data why what you don't know matters David J. Hand Princeton, New Jersey Princeton University Press [2022] xii, 330 pages Illustrationen 22 cm txt rdacontent n rdamedia nc rdacarrier Part I: Dark data: their origins and consequences. Dark data: what we don't see shapes our world. The ghost of data ; So you think you have all the data? ; Nothing happened, so we ignored it ; The power of dark data ; All around us -- Discovering dark data: what we collect and what we don't. Dark data on all sides ; Data exhaust, selection, and self-selection ; From the few to the many ; Experimental data ; Beware human frailties -- Definitions and dark data: what do you want to know?. Different definitions and measuring the wrong thing ; You can't measure everything ; Screening ; Selection on the basis of past performance -- Unintentional dark data: saying one thing, doing another. The big picture ; Summarizing ; Human error ; Instrument limitations ; Linking data sets -- Strategic dark data: gaming, feedback, and information asymmetry. Gaming ; Feedback ; Information Asymmetry ; Adverse selection and algorithms -- Intentional dark data: fraud and deception. Fraud ; Identity theft and internet fraud ; Personal financial fraud ; Financial market fraud and insider trading ; Insurance fraud ; And more -- Science and dark data: the nature of discovery. The nature of science ; If only I'd known that ; Tripping over dark data ; Dark data and the big picture ; Hiding the facts ; Retraction ; Provenance and trustworthiness: who told you that? -- Part II: Illuminating and using dark data. Dealing with dark data: shining a light. Hope! ; Linking observed and missing data ; Working with the data we have ; Going beyond the data: what if you die first? ; Going beyond the data: imputation ; Iteration ; Wrong number! -- Benefiting from dark data: reframing the question. Hiding data ; Hiding data from ourselves: randomized controlled trials ; What might have been ; Replicated data ; Imaginary data: the Bayesian Prior ; Privacy and confidentiality preservation ; Collecting data in the dark -- Classifying dark data: a route through the maze. A taxonomy of dark data ; Illumination "In the era of big data, it is easy to imagine that we have all the information we need to make good decisions. But in fact the data we have are never complete, and may be only the tip of the iceberg. Just as much of the universe is composed of dark matter, invisible to us but nonetheless present, the universe of information is full of dark data that we overlook at our peril. In Dark Data, data expert David Hand takes us on a fascinating and enlightening journey into the world of the data we don’t see. Dark Data explores the many ways in which we can be blind to missing data and how that can lead us to conclusions and actions that are mistaken, dangerous, or even disastrous. Examining a wealth of real-life examples, from the Challenger shuttle explosion to complex financial frauds, Hand gives us a practical taxonomy of the types of dark data that exist and the situations in which they can arise, so that we can learn to recognize and control for them. In doing so, he teaches us not only to be alert to the problems presented by the things we don’t know, but also shows how dark data can be used to our advantage, leading to greater understanding and better decisions. Today, we all make decisions using data. Dark Data shows us all how to reduce the risk of making bad ones." -- Missing observations (Statistics) Big data Observations manquantes (Statistique) Données volumineuses Big data fast Missing observations (Statistics) fast Entscheidung (DE-588)4014904-3 gnd rswk-swf Fehlende Daten (DE-588)4264715-0 gnd rswk-swf Fehlende Daten (DE-588)4264715-0 s Entscheidung (DE-588)4014904-3 s DE-604 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=033927647&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Hand, D. J. 1950- Dark data why what you don't know matters Part I: Dark data: their origins and consequences. Dark data: what we don't see shapes our world. The ghost of data ; So you think you have all the data? ; Nothing happened, so we ignored it ; The power of dark data ; All around us -- Discovering dark data: what we collect and what we don't. Dark data on all sides ; Data exhaust, selection, and self-selection ; From the few to the many ; Experimental data ; Beware human frailties -- Definitions and dark data: what do you want to know?. Different definitions and measuring the wrong thing ; You can't measure everything ; Screening ; Selection on the basis of past performance -- Unintentional dark data: saying one thing, doing another. The big picture ; Summarizing ; Human error ; Instrument limitations ; Linking data sets -- Strategic dark data: gaming, feedback, and information asymmetry. Gaming ; Feedback ; Information Asymmetry ; Adverse selection and algorithms -- Intentional dark data: fraud and deception. Fraud ; Identity theft and internet fraud ; Personal financial fraud ; Financial market fraud and insider trading ; Insurance fraud ; And more -- Science and dark data: the nature of discovery. The nature of science ; If only I'd known that ; Tripping over dark data ; Dark data and the big picture ; Hiding the facts ; Retraction ; Provenance and trustworthiness: who told you that? -- Part II: Illuminating and using dark data. Dealing with dark data: shining a light. Hope! ; Linking observed and missing data ; Working with the data we have ; Going beyond the data: what if you die first? ; Going beyond the data: imputation ; Iteration ; Wrong number! -- Benefiting from dark data: reframing the question. Hiding data ; Hiding data from ourselves: randomized controlled trials ; What might have been ; Replicated data ; Imaginary data: the Bayesian Prior ; Privacy and confidentiality preservation ; Collecting data in the dark -- Classifying dark data: a route through the maze. A taxonomy of dark data ; Illumination Missing observations (Statistics) Big data Observations manquantes (Statistique) Données volumineuses Big data fast Missing observations (Statistics) fast Entscheidung (DE-588)4014904-3 gnd Fehlende Daten (DE-588)4264715-0 gnd |
| subject_GND | (DE-588)4014904-3 (DE-588)4264715-0 |
| title | Dark data why what you don't know matters |
| title_auth | Dark data why what you don't know matters |
| title_exact_search | Dark data why what you don't know matters |
| title_exact_search_txtP | Dark data why what you don't know matters |
| title_full | Dark data why what you don't know matters David J. Hand |
| title_fullStr | Dark data why what you don't know matters David J. Hand |
| title_full_unstemmed | Dark data why what you don't know matters David J. Hand |
| title_short | Dark data |
| title_sort | dark data why what you don t know matters |
| title_sub | why what you don't know matters |
| topic | Missing observations (Statistics) Big data Observations manquantes (Statistique) Données volumineuses Big data fast Missing observations (Statistics) fast Entscheidung (DE-588)4014904-3 gnd Fehlende Daten (DE-588)4264715-0 gnd |
| topic_facet | Missing observations (Statistics) Big data Observations manquantes (Statistique) Données volumineuses Entscheidung Fehlende Daten |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=033927647&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
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