The impact of poor data quality on the typical enterprise
Communications of the ACM
Improving data warehouse and business information quality: methods for reducing costs and increasing profits
An approximate method for generating symmetric random variables
Communications of the ACM
Criticality of data quality as exemplified in two disasters
Information and Management
Top-k selection queries over relational databases: Mapping strategies and performance evaluation
ACM Transactions on Database Systems (TODS)
Customer relationship management at Harrah's entertainment
Decision making support systems
Structure and evolution of blogspace
Communications of the ACM - The Blogosphere
IEEE Transactions on Knowledge and Data Engineering
Beyond accuracy: what data quality means to data consumers
Journal of Management Information Systems
On Data Reliability Assessment in Accounting Information Systems
Information Systems Research
Classification in Networked Data: A Toolkit and a Univariate Case Study
The Journal of Machine Learning Research
Knowing-Why About Data Processes and Data Quality
Journal of Management Information Systems
Social ties and their relevance to churn in mobile telecom networks
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Planetary-scale views on a large instant-messaging network
Proceedings of the 17th international conference on World Wide Web
Journal of the American Society for Information Science and Technology
A Framework for Reconciling Attribute Values from Multiple Data Sources
Management Science
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Effective Feature Selection on Data with Uncertain Labels
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
User profiling with hierarchical context: an e-Retailer case study
CONTEXT'07 Proceedings of the 6th international and interdisciplinary conference on Modeling and using context
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Drawing from the social and relational perspectives, this study offers an innovative conceptualization and operational approach regarding the validation of self-reported customer demographic data, which has become an essential corporate asset for harnessing business intelligence. Specifically, based on social network and homophily paradigms in which individuals have a natural tendency to associate and interact frequently with others with similar characteristics, we constructed a relational inference model to determine the accuracy of self-administered consumer profiles. In addition, to further enhance the reliability of our model's prediction capability, we employed the entropy mechanism that minimizes potential biases that may arise from a simple probabilistic approach. To empirically validate the accuracy of our inference framework, we obtained and analyzed over 20 million actual call transactions supplied by one of the largest global telecommunication service providers. The results suggest that our social network-based inference model consistently outperforms other competing mechanisms (e.g., weighted average and simple relational classifier) regardless of the criteria choice (e.g., number of call receivers, call duration, and call frequency), with an accuracy rate of approximately 93 percent. Finally, to confirm the generalizability of our findings, we conducted simulation experiments to validate the robustness of the results in response to variations in parameter values and increases in potential noise in the data. We discuss several implications related to business intelligence for both research and practice, and offer new directions for future studies.