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Contribution of Artificial Neural Network in Predicting Completeness Through the Impact and Complexity of its Improvement

Published: 18 May 2020 Publication History

Abstract

The technological evolution and the immensity of the data produced, circulated into company makes these data, the real capital of the companies to the detriment of the customers. The erroneous data put the knockout to relationships with customers, the company must address this problem and identify the quality projects on which it must make an effort. In this article, we will present an approach based on qualitative and quantitative analysis to help the decision-makers to target data by its impacts and complexities of process improvement. The Qualitative study will be a survey and a quantitative to learn from survey data to decide the prediction and the completeness of data.

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  • (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

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    cover image ACM Other conferences
    NISS '20: Proceedings of the 3rd International Conference on Networking, Information Systems & Security
    March 2020
    528 pages
    ISBN:9781450376341
    DOI:10.1145/3386723
    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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    Published: 18 May 2020

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    Author Tags

    1. Data quality improvement project
    2. artificial neural network
    3. cost of data quality
    4. cost/benefit analysis
    5. data quality assessment and improvement

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    • (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

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