Interpreting Data

• The final step in the OSEMN Framework is interpreting data analysis results.
• Interpretation involves generating insights and telling a story with the data.
• This week focuses on how to draw conclusions from analysis and create compelling data stories.
• The course will cover approaches to data interpretation and storytelling techniques.
• A real-life example will demonstrate how the OSEMN Framework can be applied to influence policymakers.
• Data-backed storytelling is a powerful skill for data analysts to develop.

Answer Your Business Question with Your Data

• The interpret stage is the final and crucial step in the OSEMN data analysis process.
• It involves translating analytical findings into a business context.
• The main goals of the interpret stage are:
  • Understanding the results and insights from your model
  • Explaining findings to non-technical audiences clearly and concisely
• Interpretation is essential for making analytical results actionable and trustworthy.
• The focus is on generating insights that can drive better decision-making for the company.
• Effective interpretation bridges the gap between technical analysis and practical business applications.

Understand the Results of Your Model

• Revisit the initial analysis objective to maintain focus
• Evaluate how the data answers your questions and identify additional insights
• Consider how to apply findings in a business context
• Assess confidence in results using statistical testing
• Understand the factors affecting statistical significance: averages, dataset size, and data distribution
• Remember that the goal is to make informed decisions that drive business forward
• Balance confidence in results with business risk when deciding to take action
• Recognize that even the best models have limitations

Explain Your Findings

• The "Interpret" stage is crucial in the OSEMN process, where data insights are transformed into actionable recommendations.
• Effective data storytelling is essential for communicating findings to both technical and non-technical audiences.
• Slide presentations are a versatile medium for presenting data analysis results across various industries.
• A well-structured presentation should include:
  • Recap of the original problem
  • Overview of the OSEMN process steps
  • Clear and accessible data visualizations
  • Explanation of findings
  • Concrete recommendations based on the analysis
• Visualizations should be designed to make data easily understandable, highlighting key trends and patterns.
• The presenter acts as a translator, turning data into a compelling narrative that connects to the original problem.
• Recommendations should be clear, actionable, and directly linked to the findings from the data analysis.
• The ultimate goal is to bridge the gap between raw data and real-world decisions, demonstrating the power of data analytics in driving business improvements.