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Managing dirty data in organizations using ERP: lessons from a case study

Managing dirty data in organizations using ERP: lessons from a case study The integrity of the data used to operate and make decisions about a business affects the relative efficiency of operations and quality of decisions made. Protecting that integrity can be difficult and becomes more difficult as the size and complexity of the business and its systems increase. Recovering data integrity may be impossible once it is compromised. Stewards of transactional and planning systems must therefore employ a combination of procedures including systematic safeguards and user‐training programs to counteract and prevent dirty data in those systems. Users of transactional and planning systems must understand the origins and effects of dirty data and the importance of and means of guarding against it. This requires a shared understanding within the context of the business of the meaning, uses, and value of data across functional entities. In this paper, we discuss issues related to the origin of dirty data, associated problems and costs of using dirty data in an organization, the process of dealing with dirty data in a migration to a new system: enterprise resource planning (ERP), and the benefits of an ERP in managing dirty data. These issues are explored in the paper using a case study. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Industrial Management & Data Systems Emerald Publishing

Managing dirty data in organizations using ERP: lessons from a case study

Jodi Vosburg; Anil Kumar
Industrial Management & Data Systems , Volume 101 (1): 11 – Feb 1, 2001
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References (19)

Publisher
Emerald Publishing
Copyright
Copyright © 2001 MCB UP Ltd. All rights reserved.
ISSN
0263-5577
DOI
10.1108/02635570110365970
Publisher site
See Article on Publisher Site

Abstract

The integrity of the data used to operate and make decisions about a business affects the relative efficiency of operations and quality of decisions made. Protecting that integrity can be difficult and becomes more difficult as the size and complexity of the business and its systems increase. Recovering data integrity may be impossible once it is compromised. Stewards of transactional and planning systems must therefore employ a combination of procedures including systematic safeguards and user‐training programs to counteract and prevent dirty data in those systems. Users of transactional and planning systems must understand the origins and effects of dirty data and the importance of and means of guarding against it. This requires a shared understanding within the context of the business of the meaning, uses, and value of data across functional entities. In this paper, we discuss issues related to the origin of dirty data, associated problems and costs of using dirty data in an organization, the process of dealing with dirty data in a migration to a new system: enterprise resource planning (ERP), and the benefits of an ERP in managing dirty data. These issues are explored in the paper using a case study.

Journal

Industrial Management & Data Systems – Emerald Publishing

Published: Feb 1, 2001

Keywords: Data; Data integrity; Enterprise resource planning; Systems management

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