Managing Data Quality Risk in Accounting Information Systems

Authors:
Xue Bai;Manuel Nunez;Jayant R. Kalagnanam
Affiliations:
Department of Operations and Information Management, School of Business, University of Connecticut, Storrs, Connecticut 06269;Department of Operations and Information Management, School of Business, University of Connecticut, Storrs, Connecticut 06269;IBM T. J. Watson Research Center, Yorktown Heights, New York 10598
Venue:
Information Systems Research
Year:
2012

Citing 11
Cited 3

Anchoring data quality dimensions in ontological foundations

Communications of the ACM
Data quality in context

Communications of the ACM
A product perspective on total data quality management

Communications of the ACM
Quality information and knowledge

Quality information and knowledge
Data quality assessment

Communications of the ACM - Supporting community and building social capital
Data Quality Management using Business Process Modeling

SCC '06 Proceedings of the IEEE International Conference on Services Computing
Beyond accuracy: what data quality means to data consumers

Journal of Management Information Systems
Formulating the Data-Flow Perspective for Business Process Management

Information Systems Research
Optimal Software Development: A Control Theoretic Approach

Information Systems Research
On Data Reliability Assessment in Accounting Information Systems

Information Systems Research
Comet: an application of model-based reasoning to accounting systems

IAAI'96 Proceedings of the eighth annual conference on Innovative applications of artificial intelligence

A multilabel text classification algorithm for labeling risk factors in SEC form 10-K

ACM Transactions on Management Information Systems (TMIS)
A Mathematical Framework for Data Quality Management in Enterprise Systems

INFORMS Journal on Computing
Data Quality of Query Results with Generalized Selection Conditions

Operations Research

Quantified Score

Hi-index	0.00

Visualization

Abstract

The quality of data contained in accounting information systems has a significant impact on both internal business decision making and external regulatory compliance. Although a considerable body of literature exists on the issue of data quality, there has been little research done at the task level of a business process to develop effective control strategies to mitigate data quality risks. In this paper, we present a methodology for managing the risks associated with the quality of data in accounting information systems. This methodology first models the error evolution process in transactional data flow as a dynamical process; it then finds optimal control policies at the task level to mitigate the data quality-related risks using a Markov decision process model with risk constraints. The proposed Markov decision methodology facilitates the modeling of multiple dimensions of error dependence, captures the correlated impact among control procedures, and identifies an optimal control policy. A revenue realization process of an international production company is used to illustrate this methodology.