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Validating Data Quality Actions in Scoring Processes

Published: 15 January 2018 Publication History

Abstract

Data quality has gained momentum among organizations upon the realization that poor data quality might cause failures and/or inefficiencies, thus compromising business processes and application results. However, enterprises often adopt data quality assessment and improvement methods based on practical and empirical approaches without conducting a rigorous analysis of the data quality issues and outcome of the enacted data quality improvement practices. In particular, data quality management, especially the identification of the data quality dimensions to be monitored and improved, is performed by knowledge workers on the basis of their skills and experience. Control methods are therefore designed on the basis of expected and evident quality problems; thus, these methods may not be effective in dealing with unknown and/or unexpected problems. This article aims to provide a methodology, based on fault injection, for validating the data quality actions used by organizations. We show how it is possible to check whether the adopted techniques properly monitor the real issues that may damage business processes. At this stage, we focus on scoring processes, i.e., those in which the output represents the evaluation or ranking of a specific object. We show the effectiveness of our proposal by means of a case study in the financial risk management area.

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  • (2023)DQSOps: Data Quality Scoring Operations Framework for Data-Driven ApplicationsProceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering10.1145/3593434.3593445(32-41)Online publication date: 14-Jun-2023
  • (2023)A Method to Classify Data Quality for Decision Making Under UncertaintyJournal of Data and Information Quality10.1145/359253415:2(1-27)Online publication date: 21-Apr-2023
  • (2023)Estimating the Likelihood of Financial Behaviours Using Nearest NeighborsComputational Economics10.1007/s10614-023-10370-x63:4(1477-1491)Online publication date: 25-Mar-2023
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Published In

cover image Journal of Data and Information Quality
Journal of Data and Information Quality  Volume 9, Issue 2
Challenge Paper, Experience Paper and Research Paper
June 2017
77 pages
ISSN:1936-1955
EISSN:1936-1963
DOI:10.1145/3155015
Issue’s Table of Contents
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

New York, NY, United States

Publication History

Published: 15 January 2018
Accepted: 01 September 2017
Revised: 01 August 2017
Received: 01 February 2016
Published in JDIQ Volume 9, Issue 2

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Author Tags

  1. Data quality
  2. assessment
  3. decision processes
  4. decision support

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Cited By

View all
  • (2023)DQSOps: Data Quality Scoring Operations Framework for Data-Driven ApplicationsProceedings of the 27th International Conference on Evaluation and Assessment in Software Engineering10.1145/3593434.3593445(32-41)Online publication date: 14-Jun-2023
  • (2023)A Method to Classify Data Quality for Decision Making Under UncertaintyJournal of Data and Information Quality10.1145/359253415:2(1-27)Online publication date: 21-Apr-2023
  • (2023)Estimating the Likelihood of Financial Behaviours Using Nearest NeighborsComputational Economics10.1007/s10614-023-10370-x63:4(1477-1491)Online publication date: 25-Mar-2023
  • (2022)Process-driven quality improvement for scientific data based on information product mapThe Electronic Library10.1108/EL-08-2021-015740:3(177-195)Online publication date: 21-Apr-2022

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