Initiating Data Quality: A Dynamic Rule-Based System for Detecting Errors in Data

In the contemporary technological landscape, reliance on data insights is commonplace for informed decision-making. The significance arises from the data's ability to unveil factual information, providing valuable guidance. However, the accuracy of these insights is inherently tied to the quali...

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Published in:2023 IEEE 11th Conference on Systems, Process and Control, ICSPC 2023 - Proceedings
Main Author: Zaini N.; Seman M.R.; Ismail A.N.; Majang B.C.; Fadhilah S.A.
Format: Conference paper
Language:English
Published: Institute of Electrical and Electronics Engineers Inc. 2023
Online Access:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186688465&doi=10.1109%2fICSPC59664.2023.10420385&partnerID=40&md5=b36e93c536d54c4351cd465ffcfc26de
id 2-s2.0-85186688465
spelling 2-s2.0-85186688465
Zaini N.; Seman M.R.; Ismail A.N.; Majang B.C.; Fadhilah S.A.
Initiating Data Quality: A Dynamic Rule-Based System for Detecting Errors in Data
2023
2023 IEEE 11th Conference on Systems, Process and Control, ICSPC 2023 - Proceedings


10.1109/ICSPC59664.2023.10420385
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186688465&doi=10.1109%2fICSPC59664.2023.10420385&partnerID=40&md5=b36e93c536d54c4351cd465ffcfc26de
In the contemporary technological landscape, reliance on data insights is commonplace for informed decision-making. The significance arises from the data's ability to unveil factual information, providing valuable guidance. However, the accuracy of these insights is inherently tied to the quality of the data. Ensuring high data quality is crucial for deriving precise insights. Despite accumulating and storing vast amounts of data, not all of it meets the standard for high quality, often harboring numerous issues. Within this context, the study aims to explore initial steps towards improving data quality by first implementing automatic detection of errors within datasets. Towards this main goal, the study outlines three primary objectives: firstly, to identify prevalent data-related issues based on recurrent errors; secondly, to devise effective methods for translating recurrent data issues into seamlessly integrated rules for automated detection; and finally, to investigate the most effective approach for routine error checks. All of these objectives will be attempted to be developed and integrated as part of a system. This exploration aims, in the end, for the system to be able to generate comprehensive issue reports with each iteration of error checking, ready for the next step towards enhancing data quality. © 2023 IEEE.
Institute of Electrical and Electronics Engineers Inc.

English
Conference paper

author Zaini N.; Seman M.R.; Ismail A.N.; Majang B.C.; Fadhilah S.A.
spellingShingle Zaini N.; Seman M.R.; Ismail A.N.; Majang B.C.; Fadhilah S.A.
Initiating Data Quality: A Dynamic Rule-Based System for Detecting Errors in Data
author_facet Zaini N.; Seman M.R.; Ismail A.N.; Majang B.C.; Fadhilah S.A.
author_sort Zaini N.; Seman M.R.; Ismail A.N.; Majang B.C.; Fadhilah S.A.
title Initiating Data Quality: A Dynamic Rule-Based System for Detecting Errors in Data
title_short Initiating Data Quality: A Dynamic Rule-Based System for Detecting Errors in Data
title_full Initiating Data Quality: A Dynamic Rule-Based System for Detecting Errors in Data
title_fullStr Initiating Data Quality: A Dynamic Rule-Based System for Detecting Errors in Data
title_full_unstemmed Initiating Data Quality: A Dynamic Rule-Based System for Detecting Errors in Data
title_sort Initiating Data Quality: A Dynamic Rule-Based System for Detecting Errors in Data
publishDate 2023
container_title 2023 IEEE 11th Conference on Systems, Process and Control, ICSPC 2023 - Proceedings
container_volume
container_issue
doi_str_mv 10.1109/ICSPC59664.2023.10420385
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85186688465&doi=10.1109%2fICSPC59664.2023.10420385&partnerID=40&md5=b36e93c536d54c4351cd465ffcfc26de
description In the contemporary technological landscape, reliance on data insights is commonplace for informed decision-making. The significance arises from the data's ability to unveil factual information, providing valuable guidance. However, the accuracy of these insights is inherently tied to the quality of the data. Ensuring high data quality is crucial for deriving precise insights. Despite accumulating and storing vast amounts of data, not all of it meets the standard for high quality, often harboring numerous issues. Within this context, the study aims to explore initial steps towards improving data quality by first implementing automatic detection of errors within datasets. Towards this main goal, the study outlines three primary objectives: firstly, to identify prevalent data-related issues based on recurrent errors; secondly, to devise effective methods for translating recurrent data issues into seamlessly integrated rules for automated detection; and finally, to investigate the most effective approach for routine error checks. All of these objectives will be attempted to be developed and integrated as part of a system. This exploration aims, in the end, for the system to be able to generate comprehensive issue reports with each iteration of error checking, ready for the next step towards enhancing data quality. © 2023 IEEE.
publisher Institute of Electrical and Electronics Engineers Inc.
issn
language English
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