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2023 Volume 3 Number 1 https://doi.org/10.57599/gisoj.2023.3.1.5Kajetan Wojsyk1, Jakub Wojsyk2USE OF UNIVERSAL ADDRESS TO ENSURE HIGH QUALITY DATA Abstract: The article describes how to ensure high
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How to fill out data quality assurance model
How to fill out data quality assurance model
01
Identify the purpose of the data quality assurance model
02
Define the criteria for data quality
03
Choose appropriate tools and methodologies for assessing data quality
04
Collect and analyze the data to assess its quality
05
Identify areas for improvement and develop a plan to address them
06
Implement the plan and monitor the results to ensure ongoing data quality
Who needs data quality assurance model?
01
Businesses that rely on data for decision making
02
Government agencies that collect and analyze data for policy making
03
Research institutions that depend on accurate data for their studies
04
Any organization that wants to ensure the accuracy and reliability of their data
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What is data quality assurance model?
The data quality assurance model is a structured framework or set of guidelines designed to ensure that data collected and processed is accurate, reliable, and valid for its intended purpose.
Who is required to file data quality assurance model?
Organizations and entities that collect, process, or manage data are typically required to file the data quality assurance model, especially those in regulated industries or sectors.
How to fill out data quality assurance model?
To fill out the data quality assurance model, stakeholders need to follow specific guidelines provided in the model, including data collection methods, validation processes, and quality metrics.
What is the purpose of data quality assurance model?
The purpose of the data quality assurance model is to establish standards and processes that ensure data integrity and improve the overall quality of data used for decision-making.
What information must be reported on data quality assurance model?
Information that must be reported includes data sources, data collection methods, quality control measures, and any identified data quality issues and their resolutions.
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