Practical data analysis
Data Analysis and Interpretation for Better Business Decisions
Build the judgment to examine data, identify meaningful patterns, test possible explanations, and turn findings into responsible business recommendations.
Move from numbers to defensible decisions.
This course is designed for professionals who work with reports, metrics, operational data, or performance results and need to interpret what the data means before recommending action.
Frame the business question and determine whether available data is fit for analysis.
Summarize, compare, segment, and examine data using appropriate measures.
Identify patterns and investigate possible causes without overstating conclusions.
Separate facts, assumptions, interpretations, and alternative explanations.
Turn significant findings into insights, recommendations, and measurable next actions.
Course overview
A hands-on, discussion-based program using practical business scenarios and a complete data-analysis exercise. Open any module to review all included topics.
1. Understanding Data for Analysis
- Data, information, and insights
- Types and sources of data
- Defining the business question
- Identifying relevant measures
2. Assessing Data Quality
- Missing, inconsistent, and duplicate data
- Questionable values and outliers
- Data limitations
- Determining if data is fit for analysis
3. Summarizing Data
- Counts, percentages, and proportions
- Mean, median, and mode
- Minimum, maximum, and range
- Understanding variation and distribution
4. Comparing Performance
- Actual vs target
- Current vs previous period
- Group and segment comparisons
- Absolute vs percentage change
- Benchmarks and baselines
5. Identifying Patterns
- Trends and changes
- Peaks and dips
- Seasonal and recurring patterns
- Outliers and exceptions
6. Drilling Down into Data
- Breaking down overall results
- Segmenting by category, location, customer, or period
- Identifying concentration and contribution
- Pareto analysis
7. Investigating Causes
- Moving from symptoms to possible causes
- Testing explanations using data
- 5 Whys with supporting evidence
- Relationships between variables
- Correlation vs causation
8. Evaluating Findings
- Fact vs assumption vs interpretation
- Misleading averages and comparisons
- Small samples and bias
- Alternative explanations
- Strength and limitations of conclusions
9. Turning Findings into Insights
- Observation vs insight
- Asking "So what?"
- Identifying business impact
- Prioritizing significant findings
10. Making Data-Based Decisions
- Asking "Now what?"
- Developing recommendations
- Evaluating options and trade-offs
- Identifying next actions and measures
11. Practical Data Analysis Exercise
- Define the problem
- Analyze the data
- Identify key findings
- Investigate causes
- Develop insights and recommendations
Full course outline
Review the complete course structure and every included topic.
View full outline
- Understanding Data for Analysis
- Data, information, and insights
- Types and sources of data
- Defining the business question
- Identifying relevant measures
- Assessing Data Quality
- Missing, inconsistent, and duplicate data
- Questionable values and outliers
- Data limitations
- Determining if data is fit for analysis
- Summarizing Data
- Counts, percentages, and proportions
- Mean, median, and mode
- Minimum, maximum, and range
- Understanding variation and distribution
- Comparing Performance
- Actual vs target
- Current vs previous period
- Group and segment comparisons
- Absolute vs percentage change
- Benchmarks and baselines
- Identifying Patterns
- Trends and changes
- Peaks and dips
- Seasonal and recurring patterns
- Outliers and exceptions
- Drilling Down into Data
- Breaking down overall results
- Segmenting by category, location, customer, or period
- Identifying concentration and contribution
- Pareto analysis
- Investigating Causes
- Moving from symptoms to possible causes
- Testing explanations using data
- 5 Whys with supporting evidence
- Relationships between variables
- Correlation vs causation
- Evaluating Findings
- Fact vs assumption vs interpretation
- Misleading averages and comparisons
- Small samples and bias
- Alternative explanations
- Strength and limitations of conclusions
- Turning Findings into Insights
- Observation vs insight
- Asking "So what?"
- Identifying business impact
- Prioritizing significant findings
- Making Data-Based Decisions
- Asking "Now what?"
- Developing recommendations
- Evaluating options and trade-offs
- Identifying next actions and measures
- Practical Data Analysis Exercise
- Define the problem
- Analyze the data
- Identify key findings
- Investigate causes
- Develop insights and recommendations
Choose how to join.
Company teams
Request an exclusive session tailored to your reporting and decision context.
Request Proposal