Classifying data quality problems in asset management

Date

2014

Authors

Woodall, P.
Gao, J.
Parlikad, A.
Koronios, A.

Editors

Tse, P.W.

Advisors

Journal Title

Journal ISSN

Volume Title

Type:

Conference paper

Citation

Lecture Notes in Mechanical Engineering, 2014 / Tse, P.W. (ed./s), vol.19, pp.321-334

Statement of Responsibility

Conference Name

8th World Congress on Engineering Asset Management (WCEAM 2013) & the 3rd International Conference on Utility Management & Safety (ICUMAS) (30 Oct 2013 - 1 Nov 2013 : Hong Kong)

Abstract

Making sound asset management decisions, such as whether to replace or maintain an ageing underground water pipe, are critical to ensure that organisations maximise the performance of their assets. These decisions are only as good as the data that supports them, and hence many asset management organisations are in desperate need to improve the quality of their data. This paper reviews the key academic research on data quality (DQ) and Information Quality (IQ) (used interchangeably in this paper) in asset management, combines this with the current DQ problems faced by asset management organisations in various business sectors, and presents a classification of the most important DQ problems that need to be tackled by asset management organisations. In this research, eleven semi-structured interviews were carried out with asset management professionals in a range of business sectors in the U.K. The problems described in the academic literature were cross checked against the problems found in industry. In order to support asset management professionals in solving these problems, we categorised them into seven different DQ dimensions, used in the academic literature, so that it is clear how these problems fit within the standard frameworks for assessing and improving data quality. Asset management professionals can therefore now use these frameworks to underpin their DQ improvement initiatives while focussing on the most critical DQ problems.

School/Discipline

Dissertation Note

Provenance

Description

Access Status

Rights

Copyright 2014 Springer

License

Grant ID

Call number

Persistent link to this record