research-article

Dual Assessment of Data Quality in Customer Databases

Authors:

G. ShankaranarayananAuthors Info & Claims

Journal of Data and Information Quality (JDIQ), Volume 1, Issue 3

Article No.: 15, Pages 1 - 29

https://doi.org/10.1145/1659225.1659228

Published: 01 December 2009 Publication History

Abstract

Quantitative assessment of data quality is critical for identifying the presence of data defects and the extent of the damage due to these defects. Quantitative assessment can help define realistic quality improvement targets, track progress, evaluate the impacts of different solutions, and prioritize improvement efforts accordingly. This study describes a methodology for quantitatively assessing both impartial and contextual data quality in large datasets. Impartial assessment measures the extent to which a dataset is defective, independent of the context in which that dataset is used. Contextual assessment, as defined in this study, measures the extent to which the presence of defects reduces a dataset’s utility, the benefits gained by using that dataset in a specific context. The dual assessment methodology is demonstrated in the context of Customer Relationship Management (CRM), using large data samples from real-world datasets. The results from comparing the two assessments offer important insights for directing quality maintenance efforts and prioritizing quality improvement solutions for this dataset. The study describes the steps and the computation involved in the dual-assessment methodology and discusses the implications for applying the methodology in other business contexts and data environments.

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Cited By

Serra FPeralta VMarotta AMarcel P(2024)Use of Context in Data Quality Management: A Systematic Literature ReviewJournal of Data and Information Quality10.1145/367208216:3(1-41)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3672082
Reda OZellou A(2023)Fulmqa: a fuzzy logic-based model for social media data quality assessmentSocial Network Analysis and Mining10.1007/s13278-023-01148-y13:1Online publication date: 8-Nov-2023
https://doi.org/10.1007/s13278-023-01148-y
Ali SNaureen FNoor AKamel Boulos MAamir JIshaq MAnjum NAinsworth JRashid AMajidulla AFatima I(2018)Data Quality: A Negotiator between Paper-Based and Digital Records in Pakistan’s TB Control ProgramData10.3390/data30300273:3(27)Online publication date: 19-Jul-2018
https://doi.org/10.3390/data3030027
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Index Terms

Dual Assessment of Data Quality in Customer Databases
1. Software and its engineering
  1. Software notations and tools
    1. General programming languages
      1. Language features
        Data types and structures

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Published In

cover image Journal of Data and Information Quality

Journal of Data and Information Quality Volume 1, Issue 3

December 2009

109 pages

ISSN:1936-1955

EISSN:1936-1963

DOI:10.1145/1659225

Issue’s Table of Contents

Copyright © 2009 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 01 December 2009

Accepted: 01 June 2009

Revised: 01 May 2009

Received: 01 November 2007

Published in JDIQ Volume 1, Issue 3

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Cited By

Serra FPeralta VMarotta AMarcel P(2024)Use of Context in Data Quality Management: A Systematic Literature ReviewJournal of Data and Information Quality10.1145/367208216:3(1-41)Online publication date: 17-Jun-2024
https://dl.acm.org/doi/10.1145/3672082
Reda OZellou A(2023)Fulmqa: a fuzzy logic-based model for social media data quality assessmentSocial Network Analysis and Mining10.1007/s13278-023-01148-y13:1Online publication date: 8-Nov-2023
https://doi.org/10.1007/s13278-023-01148-y
Ali SNaureen FNoor AKamel Boulos MAamir JIshaq MAnjum NAinsworth JRashid AMajidulla AFatima I(2018)Data Quality: A Negotiator between Paper-Based and Digital Records in Pakistan’s TB Control ProgramData10.3390/data30300273:3(27)Online publication date: 19-Jul-2018
https://doi.org/10.3390/data3030027
Kraska T(2018)NorthstarProceedings of the VLDB Endowment10.14778/3229863.324049311:12(2150-2164)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.14778/3229863.3240493
Wijayanti WHidayanto AWilantika NAdawati IYudhoatmojo S(2018)Data Quality Assessment on Higher Education: A Case Study of Institute of Statistics2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI)10.1109/ISRITI.2018.8864476(231-236)Online publication date: Nov-2018
https://doi.org/10.1109/ISRITI.2018.8864476
Boeckling TBronselaer ADe Tré G(2018)Randomness of Data Quality ArtifactsInformation Processing and Management of Uncertainty in Knowledge-Based Systems. Applications10.1007/978-3-319-91479-4_44(529-540)Online publication date: 18-May-2018
https://doi.org/10.1007/978-3-319-91479-4_44
Chung YKrishnan SKraska T(2017)A data quality metric (DQM)Proceedings of the VLDB Endowment10.14778/3115404.311541410:10(1094-1105)Online publication date: 1-Jun-2017
https://dl.acm.org/doi/10.14778/3115404.3115414
Zak YEven A(2017)Development and evaluation of a continuous-time Markov chain model for detecting and handling data currency declinesDecision Support Systems10.1016/j.dss.2017.09.006103:C(82-93)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1016/j.dss.2017.09.006
Klas MPutz WLutz T(2016)Quality Evaluation for Big Data: A Scalable Assessment Approach and First Evaluation Results2016 Joint Conference of the International Workshop on Software Measurement and the International Conference on Software Process and Product Measurement (IWSM-MENSURA)10.1109/IWSM-Mensura.2016.026(115-124)Online publication date: Oct-2016
https://doi.org/10.1109/IWSM-Mensura.2016.026
Rao DGudivada VRaghavan V(2015)Data quality issues in big dataProceedings of the 2015 IEEE International Conference on Big Data (Big Data)10.1109/BigData.2015.7364065(2654-2660)Online publication date: 29-Oct-2015
https://dl.acm.org/doi/10.1109/BigData.2015.7364065
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