Elements of Data Communication
Six General Principals
We have explored many implementation details in recent days, focusing on individual aspects of each analysis.
Today, we want to take a step back to think less about the detail and more about the process.
After all, every data analysis has a purpose. How can we achieve it more effectively?
Let’s break down the data communication process into six general principles:
Every analysis has a goal and an audience.
It’s important to separate data exploration from the final analysis. Don’t fall into the temptation of showing everything you did.
Adapt the report to your audience. Decision-makers aren’t always interested in execution details.
So what? Keep a specific learning objective in mind. It will guide which information is relevant for your report.
Isolated numbers don’t tell us much. To make evidence-based decisions, it’s necessary to establish an appropriate basis for comparison for the goal of your report.
What type of data?
How many dimensions?
Most reports are consumed in 2D media. Showing more than that can confuse the reader.
Be careful with scales!
The more information in your visualization, the greater the cognitive load.
Your objective must be to reduce your audience cognitive costs.
Data-Ink Ratio Formula
\[ \text{Data-Ink Ratio} = \frac{\text{Data-Ink}}{\text{Total ink used to print the graphic}} \]
Your objective must be to reduce your audience cognitive costs.
Let’s tell a story starting from the chart below, making step-by-step adaptations we’ve discussed. What is it telling you?
Showcases your work
Organizes your ideas and results
Is visually appealing
Encourages interactive discussion
Demonstrates your mastery of predictive analytics concepts
Communicate key findings and impact of your project
Highlight the predictive approach, methodology, and novel insights
Highlight the business implications and insights
Provide a visually engaging, easy-to-navigate summary
Always in columns!
Headings: Large and bold to guide the reader
Color & Contrast: Choose a simple palette that highlights main points
Font Size: Text should be legible from ~3 feet away
Flow: Logical reading order from top-left to bottom-right
Keep It Simple
Use of Space
Key Text Guidelines
Emphasize the Predictive Component
Data Visualization Tips
Introduction & Problem Statement
The “So What?” Factor
Show each step as a section in your poster or as bullet points under Methodology
Charts and Graphs
Tables
Showcase Predictive Performance
Critical Thinking
Acknowledgments & References
Proofreading & Practice
Spell-check all text, verify data accuracy, ensure images are clear
Practice explaining your poster to a non-expert
Tips for Presenting Well
Arrive early to set up
Stay close and off to the side
Prepare a 30-second, 90-second, and 2-minute elevator pitches using your poster as a visual guide
Invite questions to spark in-depth discussions
Actively ask questions to your audience
Use your hands to direct your listener to your poster
Prevent you or someone else blocking the poster
Main Takeaways from this lecture:
Data Communication Principles:
Visualization Best Practices:
Poster:
Final Message:
Predictive Analytics