Storytelling with Data

• Stories are powerful tools for teaching and sharing knowledge, not just entertainment.
• Data analysts are storytellers who use data, visuals, and narratives to inform and persuade.
• A data narrative explains the origin and purpose of data, providing context to findings.
• Visuals aid in bringing data insights to life and making them easier to understand.
• Effective data storytelling combines narrative and visuals to create a comprehensive and compelling picture.
• The goal is to craft a story that is both informative and persuasive for the audience.

Explain, Enlighten, and Engage

• The three Es of data storytelling are: Explain, Enlighten, and Engage.
• Effective data storytelling combines data, narratives, and visuals.
• Data and narrative explain the situation and provide context.
• Data and visualization enlighten the audience by clearly showing what the data means.
• Narrative and visualizations engage the audience, helping them internalize the information.
• The goal is to lead the audience to an "aha moment" where everything clicks.
• A good data story transforms raw data into a compelling narrative that persuades the audience.

Telling a Compelling Story

• A compelling data story typically consists of four parts: setup, buildup, climax, and conclusion.
• Start with a hook to capture the audience's interest, often in the form of a question derived from curiosity.
• The buildup involves describing the investigative steps and communicating findings from data exploration.
• The climax is where you explain the root cause of the issue using your key finding.
• In the conclusion, present actions to remedy the issue and discuss potential causes.
• Storytelling with data is particularly important when insights are hard to understand or have a large impact on the business.
• Look for underlying stories in data that shows unexpected, unpleasant, complex, costly, or surprising information.

Remember, while not all data interpretations require a story format, using narrative techniques can make your insights more impactful and memorable.

iNterpreting Data & Storytelling

The iNterpret checklist is a two-step process for understanding and explaining data analysis results.

• Step 1 focuses on understanding the analysis results through critical questions about objectives, data insights, and confidence levels.
• Step 2 involves explaining findings through a structured presentation with five key components: recap, method, visualization, explanation, and recommendation.

• This approach ensures a comprehensive understanding of the data and effective communication of insights to stakeholders.
• The checklist emphasizes the importance of considering the analysis's business context and potential limitations.

iNterpreting Data

• Data interpretation is crucial for making analysis impactful and actionable in an organization.
• A well-structured presentation follows the OSEMN process: Obtain, Scrub, Explore, Model, and iNterpret.
• Effective presentations include clear objectives, methods, visualizations, explanations, and recommendations.
• The presentation structure should guide the audience through setup, buildup, climax, and conclusion.
• Visualizations play a key role in engaging the audience and making complex data understandable.
• Recommendations based on data insights are essential for applying the analysis in a business context.
• A logical progression in the presentation helps make the analysis easy to follow and conclusions more compelling.