Data Quality, Data Quality Management, Data Warehouse Systems 1
Markus Helfert, Ch- St. Gallen, Gregor Zellner et al. · 2008
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From data quality to data products quality
Li CAI, Yangyong ZHU · SHILAP Revista de lepidopterología · 2022
For a long time, the purpose of data quality research is to fulfill requirements of the normal operation of the organization’s own information system.With the construction and development of data market, the requirements on data quality have changed from “self-use” to “other use” and “need for supervision”.The data products quality in the data market is the focus of data users (buyers) and market regulators.The demands of users and regulators for data product quality were analyzed, and a framework of data product quality was proposed innovatively.On this basis, taking BoxedData as an example, the corresponding quality dimensions, quality indicators and quality assessment models were construct from three aspects of time, space and content integrity.The quality framework was suitable for detecting and assessing resource data products, and could provide effective detection methods and standards for data product buyers and market regulators.
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Data Quality
Tamraparni Dasu, Theodore Johnson · Wiley series in probability and statistics · 2003
In this chapter, we give a brief overview of four complementary approaches to data quality derived from the following areas: process; statistical; database; and metadata and domain expertise. The process and metadata/domain expertise aspects are integral to the design of data gathering, data flows, data specification, data storage, data retrieval, data analysis and data monitoring. We consider these to be pre-emptive approaches to data quality as revealed by our discussion. Wherever possible, we integrate case studies into the narrative.
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Data quality
2023
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Data Quality
Tamar Lasky · Oxford University Press eBooks · 2007
Abstract Interdisciplinary collaborations between epidemiologists and risk assessors have been slow to make use of traditional epidemiologic approaches to data quality and data synthesis. This chapter describes methods for evaluating data quality, and methods for combining point estimates using systematic literature reviews and meta-analysis.
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Data quality
2024
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Data quality
2023
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Data quality
2018
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Data Quality
Encyclopedia of Medical Decision Making · 2009
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Data quality
2026
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Data quality
2023
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Data Quality
2023
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Data quality
2023
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Data quality
2025
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Data quality
2023
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Data quality
2023
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Data quality
2021
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Data quality
2023
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Data Quality
Martin Treder · Apress eBooks · 2020
“Data Quality” sounds important. And hardly anybody would question the importance of Data Quality. That is why it has the potential to become another hollow catchphrase. You appear really professional if you expect “high Data Quality.”
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Data quality
Araceli Busby · Oxford University Press eBooks · 2013
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