Dark Data
Why What You Don't Know Matters
1st Edition
Published on 18. February 2020
344 pages
978-0-691-19885-9 (ISBN)
Description
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More details
Language
English
Place of publication
Princeton
United States
Publishing group
De Gruyter
Target group
Professional and scholarly
Edition type
Digital original
Product notice
Reflowable
Illustrations
6 b/w illus. 6 tables.
6 b/w illus. 6 tables.
ISBN-13
978-0-691-19885-9 (9780691198859)
Schweitzer Classification
Other editions
Person
David J. Hand is emeritus professor of mathematics and senior research investigator at Imperial College London, a former president of the Royal Statistical Society, and a fellow of the British Academy. His many previous books include The Improbability Principle, Measurement: A Very Short Introduction, Statistics: A Very Short Introduction, and Principles of Data Mining.
Content
- Cover
- Contents
- Preface
- Part 1: Dark Data: Their Origins and Consequences
- Chapter 1: 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
- Chapter 2: 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
- Chapter 3: 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
- Chapter 4: Unintentional Dark Data: Saying One Thing, Doing Another
- The Big Picture
- Summarizing
- Human Error
- Instrument Limitations
- Linking Data Sets
- Chapter 5: Strategic Dark Data: Gaming, Feedback, and Information Asymmetry
- Gaming
- Feedback
- Information Asymmetry
- Adverse Selection and Algorithms
- Chapter 6: 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
- Chapter 7: 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
- Chapter 8: Dealing with Dark Data: Shining a Light
- Hope!
- Linking Observed and Missing Data
- Identifying the Missing Data Mechanism
- Working with the Data We Have
- Going Beyond the Data: What If You Die First?
- Going Beyond the Data: Imputation
- Iteration
- Wrong Number!
- Chapter 9: 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
- Chapter 10: Classifying Dark Data: A Route through the Maze
- A Taxonomy of Dark Data
- Illumination
- Notes
- Index
