Data profiling challenges in engineering asset management data: conceptual design for next generation data profiling software
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(Published version)
Date
2013
Authors
Gao, J.
Woodall, P.
Koronios, A.
Parlikad, A.K.
Editors
Talburt, J.
Pierce, E.
Pierce, E.
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Conference paper
Citation
ICIQ 2013: the 18th International Conference on Information Quality, 2013 / Talburt, J., Pierce, E. (ed./s), pp.239-249
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18th International Conference on Information Quality (ICIQ 2013) (7 Nov 2013 - 9 Nov 2013 : Little Rock, Arkansas)
Abstract
Engineering asset management (EAM) is the process of managing the assets (from manufacturing machines to trains, planes and road bridges etc) in an organisation. In order to manage these assets organisations must have good quality data about the assets. Otherwise, decisions about when to maintain an asset can be made incorrectly, and as a consequence, can adversely impact the business financially. To improve data, the first commonly accepted stage is data quality assessment, and to support this stage, data profiling software is often used. Data profiling tools can be used to uncover and measure the scale of the data quality problems and they do this by defining data quality rules. This research investigated the data profiling needs of EAM. In particular, existing profiling tools often contain generic data quality rules that are not always applicable to EAM business users. Creating EAM data quality rules without the relevant domain knowledge is very difficult and hence the best people to develop these rules are the EAM business users. This research therefore proposes an enhanced data profiling solution, which is based on the community-based central pseudo-code DQ rule repository. The proposed data profiling solution enables business users to develop and share EAM-related data quality rules promoting rule adaptability and reusability.
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Copyright 2013 MIT Information Quality Program