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Understanding data quality issues in dynamic organisational environments – a literature review

Anstiss, Sarah 2012, Understanding data quality issues in dynamic organisational environments – a literature review, in ACIS 2012 : Location, location, location : Proceedings of the 23rd Australasian Conference on Information Systems 2012, ACIS, [Geelong, Vic.], pp. 1-10.

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Title Understanding data quality issues in dynamic organisational environments – a literature review
Author(s) Anstiss, Sarah
Conference name Australasian Conference on Information Systems (23rd : 2012 : Geelong, Victoria)
Conference location Geelong, Victoria
Conference dates 3-5 Dec. 2012
Title of proceedings ACIS 2012 : Location, location, location : Proceedings of the 23rd Australasian Conference on Information Systems 2012
Editor(s) Lamp, JohnORCID iD for Lamp, John orcid.org/0000-0003-1891-0400
Publication date 2012
Conference series Australasian Conference on Information Systems
Start page 1
End page 10
Total pages 10
Publisher ACIS
Place of publication [Geelong, Vic.]
Keyword(s) data quality
information quality
data quality issues
unstructured data
literature review
Summary Technology has been the catalyst that has facilitated an explosion of organisational data in terms of its velocity, variety, and volume, resulting in a greater depth and breadth of potentially valuable information, previously unutilised. The variety of data accessible to organisations extends beyond traditional structured data to now encompass previously unobtainable and difficult to analyse unstructured data. In addition to exploiting data, organisations are now facing an even greater challenge of assessing data quality and identifying the impacts of lack of quality. The aim of this research is to contribute to data quality literature, focusing on improving a current understanding of business-related Data Quality (DQ) issues facing organisations. This review builds on existing Information Systems literature, and proposes further research in this area. Our findings confirm that the current literature lags in recognising new types of data and imminent DQ impacts facing organisations in today’s dynamic environment of the so-called “Big Data”. Insights clearly identify the need for further research on DQ, in particular in relation to unstructured data. It also raises questions regarding new DQ impacts and implications for organisations, in their quest to leverage the variety of available data types to provide richer insights.
Notes Reproduced with the kind permission of the copyright owner.
Language eng
Field of Research 089999 Information and Computing Sciences not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
Related work DU:30049020
Copyright notice ©2012, The Authors/ACIS
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30049090

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