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This thesis investigates the quality of data in Scope 3 emissions reporting within the mining and extractive industries, focusing on the variabilities in reporting practices between Nordic and international
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Identify the data sources and ensure they are reliable.
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Define the criteria for data quality tailored to your needs.
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Collect data systematically, ensuring consistency in format and entry.
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Regularly clean the data to remove duplicates and correct errors.
05
Conduct audits on data quality periodically to identify issues.
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Implement validation checks at the point of data entry.
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Train staff on the importance of data quality and best practices.

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Quality of data refers to the accuracy, consistency, reliability, and relevance of data collected and analyzed for decision-making processes.
Individuals or organizations that collect, process, or analyze data, especially those in regulated industries, are typically required to file quality of data in.
To fill out quality of data in, one must gather accurate data, verify its consistency, document the sources, and ensure it meets the required standards before submission.
The purpose of quality of data in is to ensure data integrity and reliability for informed decision-making, compliance with regulations, and to enhance overall business performance.
Information that must be reported includes data sources, data collection methods, data accuracy levels, validation results, and any discrepancies or issues encountered.
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Relevant keywords

Scope 3 emissions Data Quality Sustainability Reporting Mining Industry Extractive Industries Carbon Accounting GHG Protocol Climate Change Nordic Companies International Practices

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