Why Multiple Datasets = Strategic Advantage
Working with multiple datasets isn’t just a backup plan – it’s a powerful methodology to ensure your IA stands out. Here’s why:
- Reduces risk of “dead-end” analysis
- Uncovers unexpected patterns through comparison
- Demonstrates critical thinking in dataset curation
- Helps avoid confirmation bias
The 3-Step Dataset Strategy
Step 1. Gather or create 2-3 datasets, either from the same category or different ones, to enrich your analysis.
Example: possible dataset options for a Sports IA- Team Season Stats: Compare stats like points scored or win rates for 2-3 different teams.
- Player Biometrics: Analyze heart rate or sleep logs for players from 2-3 different teams.
- Social Media Engagement Metrics: Measure likes, shares, or comments for 2-3 different teams.
Step 2. Perform Initial Analysis on All Datasets
- First Dataset: Establishes methodology (slowest phase) Example: Cleaning raw sales data, defining variables
- Subsequent Datasets: Reuse code/tools from first analysis (70% faster) Example: Apply same regression model to climate data
- Pro Tip: Use a master analysis template in Excel to automate repetitive tasks.
Step 3. Select the "Golden Dataset"
Case Study: Pizza Delivery IA
- Dataset A (Corporate Chain): Clean but bland correlations
- Dataset B (Local Shop): Messy but shows weather impact
- Dataset C (Your Delivery Job): Reveals tipping patterns by neighborhood
- Winner: Dataset C – unique personal angle + socioeconomic insights