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Titlebook: Data Quality Management in the Data Age; Excellence in Data Q Haiyan Yu Book 2024 The Editor(s) (if applicable) and The Author(s), under ex

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發(fā)表于 2025-3-21 17:02:17 | 只看該作者 |倒序?yàn)g覽 |閱讀模式
書目名稱Data Quality Management in the Data Age
副標(biāo)題Excellence in Data Q
編輯Haiyan Yu
視頻videohttp://file.papertrans.cn/285/284446/284446.mp4
概述Contains numerous tutorials designed to enhance readers‘ understanding.Introduces seven methods of experimental design.Includes one dedicated chapter on managing Ghost data, preparing readers to colle
叢書名稱SpringerBriefs in Service Science
圖書封面Titlebook: Data Quality Management in the Data Age; Excellence in Data Q Haiyan Yu Book 2024 The Editor(s) (if applicable) and The Author(s), under ex
描述.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 an
出版日期Book 2024
關(guān)鍵詞Quality management; Data science; Personalized service; Decision-making; Data markets; Data quality; Ghost
版次1
doihttps://doi.org/10.1007/978-3-031-71871-7
isbn_softcover978-3-031-71870-0
isbn_ebook978-3-031-71871-7Series ISSN 2731-3743 Series E-ISSN 2731-3751
issn_series 2731-3743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl
The information of publication is updating

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Tools of Data Quality Management,ata quality by comparing actual conditions against established requirements and reporting the findings to management. In this chapter, we elaborate on the concept of total data quality management, data quality control plans, and associated tools.
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High-Quality Data Collection in Data Markets,al data sellers are incentivized and are expected to enter the market. A significant challenge for data buyers in these markets is the selection of the most valuable data points from a data seller. It is imperative to control data quality to ensure that the service offerings meet the user’s requirem
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發(fā)表于 2025-3-22 20:04:16 | 只看該作者
Ghost Data in Data Quality Management,stitutes a persistent challenge for data scientists. This chapter introduces the concept of ghost data within the domains of statistics, computer science, and economics. Furthermore, we will delve into the intricacies of procuring high-quality data, which encompasses a multitude of stages, including
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發(fā)表于 2025-3-22 23:57:08 | 只看該作者
Introduction of Data Quality Management,ta sellers. It is essential to manage data quality effectively to ensure that the service offerings align with the user’s needs and expectations. This chapter offers a concise introduction to data quality management, its evolution, associated concepts, and the influence of data quality on data markets.
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發(fā)表于 2025-3-23 05:20:19 | 只看該作者
Pillars of Data Quality Management,m data sellers. Consequently, it is imperative to control data quality to ensure that service offerings meet the user’s requirements. This chapter delineates the pillars of data quality management, encompassing the dimensions of data quality, metrics, assessment, and analysis.
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