|Author||Joglekar, Nitin R. ♦ Anderson, Edward G. ♦ Shankaranarayanan, G.|
|Source||ACM Digital Library|
|Publisher||Association for Computing Machinery (ACM)|
|Subject Domain (in DDC)||Computer science, information & general works ♦ Data processing & computer science|
|Subject Keyword||Aggregate project planning ♦ Kalman filtering ♦ Data quality ♦ Value of data|
|Abstract||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.|
|Age Range||18 to 22 years ♦ above 22 year|
|Education Level||UG and PG|
|Learning Resource Type||Article|
|Publisher Place||New York|
|Journal||Journal of Data and Information Quality (JDIQ)|
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