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Quality assurance is the overarching term used to describe the activities employed to ensure the proper performance of a system. These activities can be classified into three categories: quality control, quality assessment, and internal corrective measures. Typically, these activities work cyclically: quality control is performed before and during the analysis, while quality assessment occurs during and after the investigation. Internal corrective measures are implemented based on the findings...
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Qualitative and Quantitative Validation of Tools with Rating Scales Aimed at Assessing the Quality of University Service-Learning
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TBDQ: A Pragmatic Task-Based Method to Data Quality Assessment and Improvement.

Reza Vaziri1, Mehran Mohsenzadeh1, Jafar Habibi2

  • 1Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran.

Plos One
|May 19, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces a proactive Task-Based Data Quality (TBDQ) method to enhance data quality in organizations with weak IT infrastructure. TBDQ identifies risky tasks and adds improvements, proving effective for small and medium businesses.

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Area of Science:

  • Information Science
  • Business Process Management
  • Data Management

Background:

  • Growing recognition of data quality (DQ) as critical for organizational success.
  • Existing DQ methods are often reactive, focusing on direct data manipulation, unsuitable for weak IT infrastructures.
  • Need for a proactive DQ approach addressing human-agent task impacts in less integrated systems.

Purpose of the Study:

  • To develop and evaluate a simple, practical, proactive method for improving structured data quality.
  • To address the limitations of reactive DQ methods in organizations with weak IT infrastructure.
  • To introduce a structured approach for identifying and mitigating data quality risks.

Main Methods:

  • Development of a Task-Based Data Quality (TBDQ) method.
  • Identification of potentially risky tasks within business processes.
  • Integration of new improving tasks to counteract identified risks.
  • Implementation of an award system for continuous improvement and task selection.

Main Results:

  • The TBDQ method is designed for simplicity and practical application, particularly for small and medium organizations.
  • A case study in an international trade company demonstrated TBDQ's effectiveness.
  • TBDQ successfully identified optimal activities for DQ improvement based on cost and impact.

Conclusions:

  • The TBDQ method offers a proactive and effective solution for enhancing data quality, especially in environments with limited IT resources.
  • Simplicity of implementation is a key feature of TBDQ, making it accessible for small and medium enterprises.
  • The method demonstrates practical value in optimizing DQ improvement efforts by considering both cost and effectiveness.