| Titre : |
Dark data : why what you don't know matters |
| Type de document : |
texte imprimé |
| Auteurs : |
David J. Hand (1950-....)  |
| Editeur : |
Princeton, NJ : Princeton University Press |
| Année de publication : |
2020 |
| Importance : |
330 p. |
| Présentation : |
fig. |
| Format : |
22 cm |
| ISBN/ISSN/EAN : |
978-0-691-23446-5 |
| Note générale : |
Bibliographie pages 307-317. Index |
| Langues : |
Anglais (eng) |
| Descripteurs : |
Analyse des données , Collecte des données , Décision (Théorie) , Données massives , Jeu à information incomplète
|
| Tags : |
Missing observations Statistics Big data Données manquantes Données incomplètes Données sombres Dark data |
| Index. décimale : |
232 Analyse des données |
| Résumé : |
This book is a practical guide to making good decisions in a world of missing data. Data describe and represent the world. However, no matter how big they may be, data sets don't - indeed cannot - capture everything. Data are measurements - and, as such, they represent only what has been measured. They don't necessarily capture all the information that is relevant to the questions we may want to ask. If we do not take into account what may be missing/unknown in the data we have, we may find ourselves unwittingly asking questions that our data cannot actually address, come to mistaken conclusions, and make disastrous decisions. This book looks at the ubiquitous phenomenon of "missing data." It calls this "dark data" (making a comparison to "dark matter" - i.e., matter in the universe that we know is there, but which is invisible to direct measurement). It reveals how we can detect when data is missing, the types of settings in which missing data are likely to be found, and what to do about it. It can arise for many reasons, which themselves may not be obvious - for example, asymmetric information in wars; time delays in financial trading; dropouts in clinical trials; deliberate selection to enhance apparent performance in hospitals, policing, and schools; etc. What becomes clear is that measuring and collecting more and more data (big data) will not necessarily lead us to better understanding or to better decisions. We need to be vigilant to what is missing or unknown in our data, so that we can try to control for it. How do we do that? We can be alert to the causes of dark data, design better data-collection strategies that sidestep some of these causes - and, we can ask better questions of our data, which will lead us to deeper insights and better decisions. [D'après le résumé de l'éditeur] |
| Note de contenu : |
Hardback : ISBN = 978-0-691-18237-7 (2020) |
| En ligne : |
https://press.princeton.edu/books/paperback/9780691234465/dark-data |
| Permalink : |
https://genes.bibli.fr/index.php?lvl=notice_display&id=170976 |
Dark data : why what you don't know matters [texte imprimé] / David J. Hand (1950-....) . - Princeton, NJ : Princeton University Press, 2020 . - 330 p. : fig. ; 22 cm. ISBN : 978-0-691-23446-5 Bibliographie pages 307-317. Index Langues : Anglais ( eng)
| Descripteurs : |
Analyse des données , Collecte des données , Décision (Théorie) , Données massives , Jeu à information incomplète
|
| Tags : |
Missing observations Statistics Big data Données manquantes Données incomplètes Données sombres Dark data |
| Index. décimale : |
232 Analyse des données |
| Résumé : |
This book is a practical guide to making good decisions in a world of missing data. Data describe and represent the world. However, no matter how big they may be, data sets don't - indeed cannot - capture everything. Data are measurements - and, as such, they represent only what has been measured. They don't necessarily capture all the information that is relevant to the questions we may want to ask. If we do not take into account what may be missing/unknown in the data we have, we may find ourselves unwittingly asking questions that our data cannot actually address, come to mistaken conclusions, and make disastrous decisions. This book looks at the ubiquitous phenomenon of "missing data." It calls this "dark data" (making a comparison to "dark matter" - i.e., matter in the universe that we know is there, but which is invisible to direct measurement). It reveals how we can detect when data is missing, the types of settings in which missing data are likely to be found, and what to do about it. It can arise for many reasons, which themselves may not be obvious - for example, asymmetric information in wars; time delays in financial trading; dropouts in clinical trials; deliberate selection to enhance apparent performance in hospitals, policing, and schools; etc. What becomes clear is that measuring and collecting more and more data (big data) will not necessarily lead us to better understanding or to better decisions. We need to be vigilant to what is missing or unknown in our data, so that we can try to control for it. How do we do that? We can be alert to the causes of dark data, design better data-collection strategies that sidestep some of these causes - and, we can ask better questions of our data, which will lead us to deeper insights and better decisions. [D'après le résumé de l'éditeur] |
| Note de contenu : |
Hardback : ISBN = 978-0-691-18237-7 (2020) |
| En ligne : |
https://press.princeton.edu/books/paperback/9780691234465/dark-data |
| Permalink : |
https://genes.bibli.fr/index.php?lvl=notice_display&id=170976 |
|  |
Dark data / Princeton, NJ : Princeton University Press (2020)
