A tactical planning model for a job shop
Operations Research
Identifying controlling features of engineering design iteration
Management Science
A product perspective on total data quality management
Communications of the ACM
Communications of the ACM - Supporting community and building social capital
Data Mining for Design and Manufacturing: Methods and Applications
Data Mining for Design and Manufacturing: Methods and Applications
Dynamic Programming and Optimal Control
Dynamic Programming and Optimal Control
Microsoft Secrets: How the World's Most Powerful Software Company Creates Technology, Shapes Markets, and Manages People
Data Quality for the Information Age
Data Quality for the Information Age
Real-world Data is Dirty: Data Cleansing and The Merge/Purge Problem
Data Mining and Knowledge Discovery
Problem-Solving Oscillations in Complex Engineering Projects
Management Science
The Impact of Experience and Time on the Use of Data Quality Information in Decision Making
Information Systems Research
A Fault Threshold Policy to Manage Software Development Projects
Information Systems Research
WSC '04 Proceedings of the 36th conference on Winter simulation
Beyond accuracy: what data quality means to data consumers
Journal of Management Information Systems
Journey to Data Quality
Utility-driven assessment of data quality
ACM SIGMIS Database
Overview and Framework for Data and Information Quality Research
Journal of Data and Information Quality (JDIQ)
Optimal Data Quality in Project Management for Global Software Developments
COINFO '09 Proceedings of the 2009 Fourth International Conference on Cooperation and Promotion of Information Resources in Science and Technology
An Empirical Analysis of Contract Structures in IT Outsourcing
Information Systems Research
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We examine the management of data accuracy in inter-organizational data exchanges using the context of distributed software projects. Organizations typically manage projects by outsourcing portions of the project to partners. Managing a portfolio of such projects requires sharing data regarding the status of work-in-progress residing with the partners and estimates of these projects' completion times. Portfolio managers use these data to assign projects to be outsourced to partners. These data are rarely accurate. Unless these data are filtered, inaccuracies can lead to myopic and expensive sourcing decisions. We develop a model that uses project-status data to identify an optimal assignment of projects to be outsourced. This model permits corruption of project-status data. We use this model to compute the costs of using perfect versus inaccurate project-status data and show that the costs of deviation from optimal are sizable when the inaccuracy in the data is significant. We further propose a filter to correct inaccurate project-status data and generate an estimate of true progress. With this filter, depending on the relative magnitudes of errors, we show that accuracy of project-status data can be improved and the associated economic benefit is significant. We illustrate the improvement in accuracy and associated economic benefit by instantiating the model and the filter. We further elaborate on how the model parameters may be estimated and used in practice.