Data Quality Management: Assurance & Control
A 5-day professional program designed to equip technical and management professionals with the knowledge and tools to establish, implement, and sustain high-quality Data Quality Management Systems.
Course Overview
This advanced program provides participants with a comprehensive understanding of Data Quality Management Systems (DQMS), emphasizing both Quality Assurance (QA) and Quality Control (QC). Participants will learn how to implement, monitor, and sustain effective DQMS to ensure accurate, reliable, and actionable data. The course combines theoretical concepts with practical exercises, tailored to professionals in geosciences, database management, and senior management.
Course Objectives
By the end of this program, participants will be able to:
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Implement and maintain robust Data Quality Management Systems.
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Apply QA and QC methodologies to ensure data integrity.
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Use statistical and sampling techniques to assess data quality.
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Conduct verification, validation, and auditing of datasets.
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Develop sustainable strategies for continuous data quality improvement.
Course Audience
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Geochemists and Senior Geologists seeking to ensure data reliability in their analyses.
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Section Heads in Geology responsible for overseeing data quality and team performance.
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Geotechnicians and Database Managers involved in data collection, entry, and management.
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Senior Management and decision-makers engaged in data governance, reporting, and strategic planning.
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Professionals aiming to implement or improve Data Quality Management Systems (DQMS) within their organizations.
Course Methodology
- Interactive lectures and case studies
- Hands-on practical exercises
- Group discussions and problem-solving workshops
- Real-world scenario assessments
Course Outline
Day 1: Introduction to Data Quality Management
- Core concepts of DQMS, QA, and QC
- Importance of data quality in geoscience and technical domains
- Overview of industry standards and best practices
Day 2: Quality Control Frameworks
- Quality acceptance criteria and performance indicators
- Sampling theory and practical applications
- Verification and validation techniques
- Hands-on QC assessment exercises
Day 3: Statistical Methods for Data Quality
- Basic and applied statistics for data evaluation
- Data profiling, anomaly detection, and trend analysis
- Practical exercises using statistical tools
Day 4: Auditing and Continuous Improvement
- Conducting data quality audits
- Monitoring, reporting, and KPIs
- Corrective and preventive actions
- Case studies and group discussions
Day 5: Implementation and Integration
- Aligning DQMS with organizational goals
- Tailored strategies for geoscience and technical teams
- Developing action plans for real-world implementation
- Final assessment and interactive group exercises
Certificates