Data Cleaning & Quality Assurance

Understanding our methodology for preparing reliable dashboard visualizations

🎯 Objective
We clean data to ensure that dashboard visualizations are representative, accurate, and privacy-safe. This is especially important in multi-country datasets where inconsistencies, outliers, and identifiable information can distort results or reveal confidential farm-level details. Our goal is to present clean, privacy-respecting, and analytically sound insights that allow users to make informed decisions while ensuring contributor anonymity.
🔧 Key Cleaning Steps
Filter by Country and Date
Data is filtered based on selected country and date range before analysis. This ensures results are relevant to the specific region and time period, eliminating unrelated noise.
Remove Outliers using IQR
Outliers are removed using the Interquartile Range (IQR) method. Values beyond 1.5×IQR from Q1 or Q3 are excluded. These thresholds are recalculated for each country and date.
Drop Zero, Null, or Non-informative Values
Null or zero values in key metrics (like area or yield) are removed. These often result from incomplete records and can distort visual outputs.
Remove Identifiable Fields
Fields such as farm_name and personal notes are excluded to protect privacy. Only anonymized data is included in visualizations.
Standardize Column Naming
We apply a consistent naming convention (e.g., lowercase, underscore-separated) across all variables.
📈 Impact on Visualizations

Without cleaning, visualizations may reflect extreme values or incomplete data. By cleaning the dataset, we improve comparability across time and countries and enhance trust in insights shown.

📚 Full Documentation

Explore our comprehensive methodology and technical implementation details:

📖 View Complete Data Cleaning Guide
Last updated: May 2025