Dark Data: Why What You Don’t Know Matters

Hand, David J.

ISBN 10: 069118237X / ISBN 13: 9780691182377

Verlag: Princeton University Press, 2020

Beschreibung

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Bestandsnummer des Verkäufers SHAK7199

Bibliografische Details

• Titel: Dark Data: Why What You Don?t Know Matters
• Verlag: Princeton University Press
• Erscheinungsjahr: 2020
• Sprache: Englisch
• ISBN-10: 069118237X
• ISBN-13: 9780691182377
• Einband: Hardcover
• Zustand: Neu

Über diesen Titel

Inhaltsangabe

A practical guide to making good decisions in a world of missing data

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.

Über die Autorin bzw. den Autor

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.