MGMT 47400: Predictive Analytics

Elements of Data Communication

Professor: Davi Moreira

Overview

  • Six General Principals

    1. Context Matters
    2. Visualization Derives From Data
    3. Less is More: Separate Signal From Noise
    4. Hierarchy Among Data
    5. Beauty Counts: Not All Data Are Equally Important
    6. Telling Your Story
  • Posters

The Forest and the Trees

The Forest and the Trees

Forest

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?

Essential Elements of Data Communication

Let’s break down the data communication process into six general principles:

  1. Context Matters
  2. Visualization Derives From Data
  3. Less is More: Separate Signal From Noise
  4. Hierarchy Among Data
  5. Beauty Counts: Not All Data Are Equally Important
  6. Telling Your Story

Context Matters

Context

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.

Context Can Come from New Information…

…or Reinforce Existing Information

Visualization derives from data

Choosing the Chart

  • 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!

Use graphs instead of tables!

Use graphs instead of tables!

Scales Can Be Misleading

Scale Fail

Avoid Dual Axes

Dual Axis

Or Triple Axes!

Triple Axis

Fewer Pie Charts…

Pie vs Bar

What?!

About Pie Charts

Oof

Chart Fail

Check Potential Ideas! The Python or R Graph Galleries


Less is More
Separate Signal From Noise

Eliminating Noise


  • 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

\[ \text{Data-Ink Ratio} = \frac{\text{Data-Ink}}{\text{Total ink used to print the graphic}} \]

Data-Ink Ratio


Your objective must be to reduce your audience cognitive costs.


Step-by-Step Cleanup


Eliminating the Border


Cleaning the Grids


Removing the Points


Processing the Axes


Adjusting the Label


Adjusting Colors

Before and After


Before and After

Hierarchy Among Data

Count the Number 3s

Count the Number 3s

Ways to Draw Attention

Highlighting with Colors

Returning to Our Example

Returning to Our Example

Use Colors Strategically

Beauty Counts
Not All Data Are Equally Important

Emphasizing the Main Point

Emphasizing the Main Point

Telling Your Story

Bringing It All Together

Let’s tell a story starting from the chart below, making step-by-step adaptations we’ve discussed. What is it telling you?

Bringing It All Together

Bringing It All Together

Bringing It All Together

Bringing It All Together

Final Narrative

Final Narrative

Final Narrative

Final Narrative

Final Narrative

Final Narrative

Final Narrative

Final Narrative

Before and After

Applications

COVID-19 Evolution

Moving Average

Deaths in New York

Additional Material

Poster

Why a Poster Presentation?



  • Showcases your work

  • Organizes your ideas and results

  • Is visually appealing

  • Encourages interactive discussion

  • Demonstrates your mastery of predictive analytics concepts

Designing a Predictive Analytics Poster: Objectives



  • Communicate key findings and impact of your project

  • Highlight the predictive approach, methodology, and novel insights

  • Highlight the business implications and insights

Planning Your Poster: Template and Rubric



Planning Your Poster: Visual Hierarchy


  • 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

Planning Your Poster: Layout & Design

  • Keep It Simple

    • Limit text and rely on figures, charts, and bullet points
    • Use consistent color schemes, fonts, and alignment
    • Use a QR Code to drive your audience to a dashboard or the project webpage
  • Use of Space

    • White space is your friend — avoid clutter
    • Group related sections in boxes or areas for clarity
    • 60% Graphics / 40% Text
  • Key Text Guidelines

    • Title: ~85–100 pt
    • Headings/Subheadings: ~46–55 pt
    • Body Text: ~34–38 pt
    • Captions: ~26–28 pt
    • Should be readble from 3 feet away

Planning Your Poster: Organizing Your Content

  1. Title & Authors: Clear, concise, and visible
  2. Abstract/Introduction: One clear and concise paragraph to show why your problem is important and what are the main results.
  3. Business Problem: What is your project goal?
  4. Analytics Problem Framing: What you aim to predict or explain and why
  5. Research Question: Summarise your project in a question you will answer
  6. Data and EDA: Key variables, data collection.
  7. Methodology: Modeling approach focusing on the response variable.
  8. Model Building and Evaluation: Model results clear and appropriately evaluated
  9. Business Implication: Business validation of model/solution demonstrated. Main findings, performance metrics, interpretation
  10. Conclusion & Future Work: Wrap-up and potential next steps
  11. Acknowledgments & References: Recognize collaborators and sources

Designing for Predictive Analytics

  • Emphasize the Predictive Component

    • Model Choice: Clearly state if you used linear regression, random forests, neural networks, etc.
    • Metrics: Include accuracy, RMSE, AUC, or other relevant metrics
    • Model Interpretation: Highlight key features or coefficients that drove the predictions
  • Data Visualization Tips

    • Use labeled graphs (e.g., bar charts, scatter plots, confusion matrices)
    • Show before-and-after comparisons if you performed feature engineering
    • Explain the importance of training vs. testing sets (or cross-validation)

Crafting a Clear Narrative

Crafting a Clear Narrative

Word Count

Crafting a Clear Narrative

  • Introduction & Problem Statement

    • Present the real-world problem or question
    • Briefly mention why it matters (e.g., business impact, social relevance)
  • The “So What?” Factor

    • Emphasize the value of your predictive findings
    • Illustrate how stakeholders can use predictions or insights

Research Design Flow

Show each step as a section in your poster or as bullet points under Methodology

Effective Figures and Tables

  • Charts and Graphs

    • Label axes, include legends only if necessary
    • Highlight the data that supports your main argument
    • Provide short, descriptive captions beneath each figure
  • Tables

    • Keep tables simple, emphasize key results
    • Consider whether a chart might be more impactful than a table

Results & Interpretation


  • Showcase Predictive Performance

    • Provide confusion matrices for classification models
    • Compare model performance with baseline or benchmark
    • Discuss practical implications of the performance metrics
  • Critical Thinking

    • Explain why the model performed well or poorly
    • Reflect on limitations (e.g., small dataset, missing variables, etc.)
    • Suggest ways to improve performance or replicate your findings

Conclusion & Future Work

  • Main Takeaways

    • Recap key predictions and findings in plain language
    • Emphasize the impact and potential applications
  • Next Steps

    • Possible avenues for further research or new data
    • Encouraging further validation, deployment, or real-world testing

Final Touches

  • Acknowledgments & References

    • Recognize supporting faculty, funding sources, and collaborators
    • Cite any references (including data sources and libraries) in a consistent format
  • Proofreading & Practice

  • Spell-check all text, verify data accuracy, ensure images are clear

  • Practice explaining your poster to a non-expert

Presenting your Poster

  • 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

    • Follow the dress code!

Summary

Summary

Main Takeaways from this lecture:

  • Data Communication Principles:

    • Context matters: Tailor your analysis to the audience and goal.
    • Focus on the story: Highlight insights, not the process.
    • Beauty and clarity: Simplify visuals, use appropriate colors, and remove unnecessary elements.
  • Visualization Best Practices:

    • Use graphs instead of tables where possible.
    • Avoid misleading scales and excessive dimensions.
    • Prioritize hierarchy and emphasize key data points.
  • Poster:

    • Simplicity and clarity win over clutter
    • Emphasize the predictive analytics approach and metrics
    • Ensure visual impact and logical flow
    • Practice delivering a concise overview of your work
  • Final Message:

    • Less is more. Reduce complexity to communicate data effectively.
    • Always keep your audience’s decision-making needs at the forefront.

Thank you!