A process pattern model for tackling and improving big data quality

Wahyudi, A, Kuk, G ORCID logoORCID: https://orcid.org/0000-0002-1288-3635 and Janssen, M, 2018. A process pattern model for tackling and improving big data quality. Information Systems Frontiers. ISSN 1387-3326

[thumbnail of PubSub10082_Kuk.pdf]
Preview
Text
PubSub10082_Kuk.pdf - Published version

Download (1MB) | Preview

Abstract

Data seldom create value by themselves. They need to be linked and combined from multiple sources, which can often come with variable data quality. The task of improving data quality is a recurring challenge. In this paper, we use a case study of a large telecom company to develop a generic process pattern model for improving data quality. The process pattern model is defined as a proven series of activities, aimed at improving the data quality given a certain context, a particular objective, and a specific set of initial conditions. Four different patterns are derived to deal with the variations in data quality of datasets. Instead of having to find the way to improve the quality of big data for each situation, the process model provides data users with generic patterns, which can be used as a reference model to improve big data quality.

Item Type: Journal article
Publication Title: Information Systems Frontiers
Creators: Wahyudi, A., Kuk, G. and Janssen, M.
Publisher: Springer
Date: 25 January 2018
ISSN: 1387-3326
Identifiers:
Number
Type
10.1007/s10796-017-9822-7
DOI
9822
Publisher Item Identifier
Rights: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Divisions: Schools > Nottingham Business School
Record created by: Linda Sullivan
Date Added: 26 Jan 2018 14:26
Last Modified: 01 May 2018 15:33
URI: https://irep.ntu.ac.uk/id/eprint/32569

Actions (login required)

Edit View Edit View

Statistics

Views

Views per month over past year

Downloads

Downloads per month over past year