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Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

Employing Neural Networks to Assess Data Quality

Employing Neural Networks to Assess Data Quality
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Author(s): Abdullah Al-Namlah (Florida Institute of Technology, USA) and Shirely A. Becker (Florida Institute of Technology, USA)
Copyright: 2002
Pages: 4
Source title: Issues & Trends of Information Technology Management in Contemporary Organizations
Source Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-930708-39-6.ch009
ISBN13: 9781930708396
EISBN13: 9781466641358


Neural networks have been successfully employed in a wide variety of fields, such as signal processing, pattern recognition, medicine, speech recognition, and business, in order to solve complex problems. It is proposed in this paper that neural networks can also be applied to the data quality problem that is so pervasive in legacy software systems. The focus of this work is on the use of neural networks to learn how to identify duplicate records in a data source. This problem has been recognized as extremely important to many organizations, due to the size and complexity of today’s database systems. The initial use of neural nets has shown that they can be trained to perform data quality checks. Our ongoing research will address larger, more complex databases systems, as well as, learning capabilities to solve other data quality problems.

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