What kind of graph can make the data below easier on the end-user?

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Summary

The task at hand involves creating a graph to visualize aggregated responses to over 300 questions per team, currently presented in a tabular form in Power BI. The goal is to make the data easier to understand and interpret for end-users. Effective data visualization is key to achieving this goal.

Root Cause

The root cause of the issue is the overwhelming amount of data (over 300 questions per team) presented in a tabular form, which can be difficult to comprehend and analyze for end-users. The need for a more intuitive and engaging visualization method arises from the complexity and volume of the data.

Why This Happens in Real Systems

This issue occurs in real systems due to several factors:

  • Large datasets: Dealing with hundreds of questions and responses can be challenging.
  • Static presentations: Traditional tabular forms may not effectively communicate insights from large datasets.
  • User experience: End-users may struggle to extract meaningful information from complex, text-heavy tables.

Real-World Impact

The impact of not addressing this issue includes:

  • Decreased user engagement: Users may find the data overwhelming and thus less likely to engage with it.
  • Inefficiency in decision-making: Without clear insights, decision-making processes can be hindered.
  • Missed opportunities for analysis: The potential for deeper analysis and understanding of the data may be missed.

Example or Code (if necessary and relevant)

import pandas as pd
import matplotlib.pyplot as plt

# Sample dataset
data = {
    'Question': ['Q1', 'Q2', 'Q3'],
    'Team': ['Blue', 'Green', 'Red'],
    'YES': [5, 2, 4],
    'No': [0, 5, 4]
}

df = pd.DataFrame(data)

# Plotting
plt.figure(figsize=(10, 6))
plt.bar(df['Team'], df['YES'], label='YES')
plt.bar(df['Team'], df['No'], bottom=df['YES'], label='No')
plt.xlabel('Team')
plt.ylabel('Count')
plt.title('Aggregated Responses')
plt.legend()
plt.show()

How Senior Engineers Fix It

Senior engineers address this issue by:

  • Selecting appropriate visualization tools: Choosing the right graph type (e.g., bar charts, heatmaps) to represent the data effectively.
  • Implementing interactive elements: Adding features like search filters to enhance user experience and facilitate easier data analysis.
  • Optimizing for user experience: Ensuring the visualization is intuitive, engaging, and informative.

Why Juniors Miss It

Junior engineers might overlook the importance of effective data visualization due to:

  • Lack of experience: Limited exposure to dealing with large, complex datasets.
  • Focus on functionality: Prioritizing the functionality of the system over the user experience.
  • Insufficient training: Not having received adequate training in data visualization best practices and tools.