Data Analitics Study Notes

Storytelling with Data

Sep 1, 2024 — 2 min read

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.

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.

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.

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.