Paper Info
Title
Assessing Data Quality for Information Products: Impact of Selection, Projection, and Cartesian Product
Abstract
The cost associated with making decisions based on poor-quality data is quite high. Consequently, the management of data quality and the quality of associated data management processes has become critical for organizations. An important first step in managing data quality is the ability to measure the quality of information products (derived data) based on the quality of the source data and associated processes used to produce the information outputs. We present a methodology to determine two data quality characteristics--accuracy and completeness--that are of critical importance to decision makers. We examine how the quality metrics of source data affect the quality for information outputs produced using the relational algebra operations selection, projection, and Cartesian product. Our methodology is general, and can be used to determine how quality characteristics associated with diverse data sources affect the quality of the derived data.
Year
DOI
Venue
2004
10.1287/mnsc.1040.0237
Management Science
Keywords
Field
DocType
poor-quality data,probability calculus,data quality characteristic,quality characteristic,assessing data quality,cartesian product,associated data management process,information product,relational data model,source data,quality metrics,relational algebra,information quality metrics,diverse data source,information products,information output,data quality,decision maker,relation algebra,data management,information quality
Data mining,Data quality,Source data,Computer science,Quality function deployment,Relational algebra,Relational model,Data management,Data model,Information quality
Journal
Volume
Issue
ISSN
50
7
0025-1909
Citations 
PageRank 
References 
44
1.39
7
Authors
3
Name
Order
Citations
PageRank
Amir Parssian11205.87
Sumit Sarkar2835260.90
Varghese S. Jacob339234.13