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Data quality management in the data age = excellence in data quality for enhanced digital economic growth /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Data quality management in the data age/ by Haiyan Yu.
Reminder of title:	excellence in data quality for enhanced digital economic growth /
Author:	Yu, Haiyan.
Published:	Cham :Springer Nature Switzerland : : 2024.,
Description:	xix, 90 p. :ill. (some col.), digital ;24 cm.
[NT 15003449]:	Introduction of data quality management -- Quality management in Data Science -- Pillars of data quality management -- Tools of data quality management -- Experimental designs for data quality control -- High-quality data collection in data markets -- Ghost data in data quality management -- Summary.
Contained By:	Springer Nature eBook
Subject:	Electronic data processing - Quality control. -
Online resource:	https://doi.org/10.1007/978-3-031-71871-7
ISBN:	9783031718717

Data quality management in the data age = excellence in data quality for enhanced digital economic growth /
Yu, Haiyan.

Data quality management in the data ageexcellence in data quality for enhanced digital economic growth /[electronic resource] :by Haiyan Yu. - Cham :Springer Nature Switzerland :2024. - xix, 90 p. :ill. (some col.), digital ;24 cm. - SpringerBriefs in service science,2731-3751. - SpringerBriefs in service science..

Introduction of data quality management -- Quality management in Data Science -- Pillars of data quality management -- Tools of data quality management -- Experimental designs for data quality control -- High-quality data collection in data markets -- Ghost data in data quality management -- Summary.

This book addresses data quality management for data markets, including foundational quality issues in modern data science. By clarifying the concept of data quality, its impact on real-world applications, and the challenges stemming from poor data quality, it will equip data scientists and engineers with advanced skills in data quality management, with a particular focus on applications within data markets. This will help them create an environment that encourages potential data sellers with high-quality data to join the market, ultimately leading to an improvement in overall data quality. High-quality data, as a novel factor of production, has assumed a pivotal role in driving digital economic development. The acquisition of such data is particularly important for contemporary decision-making models. Data markets facilitate the procurement of high-quality data and thereby enhance the data supply. Consequently, potential data sellers with high-quality data are incentivized to enter the market, an aspect that is particularly relevant in data-scarce domains such as personalized medicine and services. Data scientists have a pivotal role to play in both the intellectual vitality and the practical utility of high-quality data. Moreover, data quality control presents opportunities for data scientists to engage with less structured or ambiguous problems. The book will foster fruitful discussions on the contributions that various scientists and engineers can make to data quality and the further evolution of data markets.

ISBN: 9783031718717

Standard No.: 10.1007/978-3-031-71871-7doiSubjects--Topical Terms:

577938
Electronic data processing
--Quality control.

LC Class. No.: QA76.9.E95

Dewey Class. No.: 004

Data quality management in the data age = excellence in data quality for enhanced digital economic growth /
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505 0 $a Introduction of data quality management -- Quality management in Data Science -- Pillars of data quality management -- Tools of data quality management -- Experimental designs for data quality control -- High-quality data collection in data markets -- Ghost data in data quality management -- Summary.
520 $a This book addresses data quality management for data markets, including foundational quality issues in modern data science. By clarifying the concept of data quality, its impact on real-world applications, and the challenges stemming from poor data quality, it will equip data scientists and engineers with advanced skills in data quality management, with a particular focus on applications within data markets. This will help them create an environment that encourages potential data sellers with high-quality data to join the market, ultimately leading to an improvement in overall data quality. High-quality data, as a novel factor of production, has assumed a pivotal role in driving digital economic development. The acquisition of such data is particularly important for contemporary decision-making models. Data markets facilitate the procurement of high-quality data and thereby enhance the data supply. Consequently, potential data sellers with high-quality data are incentivized to enter the market, an aspect that is particularly relevant in data-scarce domains such as personalized medicine and services. Data scientists have a pivotal role to play in both the intellectual vitality and the practical utility of high-quality data. Moreover, data quality control presents opportunities for data scientists to engage with less structured or ambiguous problems. The book will foster fruitful discussions on the contributions that various scientists and engineers can make to data quality and the further evolution of data markets.
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