We outline a call to action for promoting empiricism in data quality research. The action points result from an analysis of the landscape of data quality research. The landscape exhibits two dimensions of empiricism in data quality research relating to type of metrics and scope of method. Our study indicates the presence of a data continuum ranging from real to synthetic data, which has implications for how data quality methods are evaluated. The dimensions of empiricism and their inter-relationships provide a means of positioning data quality research, and help expose limitations, gaps and opportunities
Data Quality is, in essence, understood as the degree to which the data of interest satisfies the re...
Regardless of the field of study, sharing data is one of the most fundamental aspects of maintaining...
Attribute data quality is important object for each databases. If data quality doesn’t useful,...
We present the old-but-also-new problem of data quality from a statistical perspective, in part with...
In this commentary, I propose a framework for thinking about data quality in the context of scientif...
In this commentary, I propose a framework for thinking about data quality in the context of scientif...
In this commentary, I propose a framework for thinking about data quality in the context of scientif...
In this commentary, I propose a framework for thinking about data quality in the context of scientif...
In this commentary, I propose a framework for thinking about data quality in the context of scientif...
In this commentary, I propose a framework for thinking about data quality in the context of scientif...
The body of knowledge on data and information quality is highly diversified, primarily due to the cr...
Data quality (DQ) has been studied in significant depth over the last two decades and has received a...
Data quality is obviously a good thing and an attractive goal to pursue. But what is data quality? ...
Research and practice in data and information quality is characterized by methodological as well as ...
The issue of data quality is as old as data itself. However, the proliferation of diverse, large-sca...
Data Quality is, in essence, understood as the degree to which the data of interest satisfies the re...
Regardless of the field of study, sharing data is one of the most fundamental aspects of maintaining...
Attribute data quality is important object for each databases. If data quality doesn’t useful,...
We present the old-but-also-new problem of data quality from a statistical perspective, in part with...
In this commentary, I propose a framework for thinking about data quality in the context of scientif...
In this commentary, I propose a framework for thinking about data quality in the context of scientif...
In this commentary, I propose a framework for thinking about data quality in the context of scientif...
In this commentary, I propose a framework for thinking about data quality in the context of scientif...
In this commentary, I propose a framework for thinking about data quality in the context of scientif...
In this commentary, I propose a framework for thinking about data quality in the context of scientif...
The body of knowledge on data and information quality is highly diversified, primarily due to the cr...
Data quality (DQ) has been studied in significant depth over the last two decades and has received a...
Data quality is obviously a good thing and an attractive goal to pursue. But what is data quality? ...
Research and practice in data and information quality is characterized by methodological as well as ...
The issue of data quality is as old as data itself. However, the proliferation of diverse, large-sca...
Data Quality is, in essence, understood as the degree to which the data of interest satisfies the re...
Regardless of the field of study, sharing data is one of the most fundamental aspects of maintaining...
Attribute data quality is important object for each databases. If data quality doesn’t useful,...