Equinox

EDA Reporting

3 min read

A good exploratory data analysis report usually follows this workflow:

Understand the Objective

Start by asking:

  • What question am I trying to answer?
  • What business problem am I solving?

Example:

Understand sales performance and identify trends, patterns and opportunities for improvement

Understand the Dataset

Describe what the data contains.

Example:

  • Orders
  • Customers
  • Products
  • Sales
  • Discounts
  • Profit
  • Dates

Identify

  • Number of rows and columns
  • Types of variables:
    • Numerical (Sales, Profit, Quantity)
    • Categorical (Category, Region, Customer Segment)
    • Dates (Order Date, Ship Date)

Assess Data Quality

Check for potential issues:

  • Missing values
  • Duplicate records
  • Incorrect data types
  • Impossible values
  • Outliers

Document what you would do to clean the data, even if you don’t actually modify it.

Generate Descriptive Statistics

For important numerical variables:

  • Mean
  • Median
  • Standard Deviation
  • Minimum
  • Maximum

Ask

  • What is a typical sale?
  • How much variability exists?
  • Are there extreme values?

Explore Relationships

Use visualizations to answer questions.

Bar Charts

Compare categories using Bar Charts.

Questions

  • Which categories sell the most?
  • Which product has the best margins?

Line Charts

Analyze changes over time using Line Charts.

Questions

  • Are sales increasing/decreasing?
  • Is there seasonality?
  • Are there unusual spikes?

Histograms

Study data distribution using Histograms.

Questions

  • Are sales concentrated in a certain range?
  • Are there outliers?
  • Is the distribution skewed?

Interpret Findings

Most important step.

Don’t just say:

“Sales reached 500000”.

Explain instead:

“Sales peaked in November and December, suggesting seasonal demand that could be leveraged through targeted marketing campaigns.”

Always move from:

Observation -> Possible explanation -> Business implication

Example

Observation

  • High discounts often produce negative profits

Possible Explanation

  • Some product are being discounted too aggressively

Business implication

  • The company should review its discount policies to prevent selling at a loss

Summary Key Insights

A good report usually ends with 3-5 major findings.

Example

  • Most daily sales are relatively small, with a few exceptionally high-sales days.
  • Sales exhibit seasonal peaks during certain months.
  • A small number of products generate a large share of revenue.
  • Large discounts are associated with negative profits.
  • Sales performance differs across categories and regions.

Provide Recommendations

Translate findings into actions.

Example

  • Investigate causes of high-sales periods and replicate them.
  • Monitor discount strategies.
  • Focus marketing on top-performing categories.
  • Analyze underperforming products and regions.

Report Structure

  1. Introduction: Objective of the analysis
  2. Dataset Description: columns and data types
  3. Data Quality Assessment: Missing values, duplicates, assumptions
  4. Descriptive Statistics: Mean, Median, Std Dev
  5. Visual Analysis
    • Bar Chart
    • Line Chart
    • Histogram
  6. Key Findings: Patterns, trends, outliers
  7. Business Implications and Recommendations
  8. Conclusion: main takeaways and next steps

Graph View