Day 1: Introduction to Data Quality Management
Objectives:
- Introduce participants to the concept of data quality management and its importance in decision-making.
- Learn about data quality standards and dimensions.
- Understand how to collect and organize data to ensure its quality.
Topics:
- The Concept of Data Quality Management
- Definition of data quality and its dimensions (accuracy, integrity, timeliness, usability).
- The importance of data quality in improving performance and decision-making.
- The relationship between data quality and business performance.
- Data Quality Standards
- International and local data quality standards.
- How to measure data quality using Key Performance Indicators (KPIs).
- Data Collection and Organization
- Methods for collecting data from various sources.
- Organizing data and using the appropriate tools to ensure its quality.
Day 2: Data Analysis and Advanced Analytical Techniques
Objectives:
- Understand various data analysis methods.
- Learn how to use software tools for data analysis.
- Apply advanced analytical techniques to extract valuable insights from data.
Topics:
- Basic Data Analysis Methods
- Descriptive analysis: Understanding data and identifying patterns.
- Predictive analysis: Forecasting future trends based on historical data.
- Software Tools for Data Analysis
- Introduction to analysis tools like Excel, Power BI, Python.
- How to use these tools in data processing and analysis.
- Advanced Data Analysis Techniques
- Big Data analysis and advanced analytical techniques.
- Machine learning and artificial intelligence in data analysis.
- Using statistical analysis to extract actionable insights.
Day 3: Using Data for Strategic Decision-Making
Objectives:
- Learn how to turn data analysis results into actionable decisions.
- Understand the role of data in supporting strategic decision-making.
- Learn how to evaluate the outcomes of data-driven decisions.
Topics:
- Turning Analysis into Strategic Decisions
- How to use data analysis results to support strategic decisions.
- Integrating data with market and environmental understanding for scientifically-based decisions.
- The Role of Data in Organizational Decision-Making
- The importance of data in decisions related to resources, staffing, and strategic planning.
- Practical examples of using data to improve organizational performance.
- Evaluating Data-Driven Decisions
- How to measure the effectiveness of data-driven decisions.
- Measuring the impact of decisions on overall organizational performance.