Data Analysis of Tech Company Layoffs (2020 - 2024)

📖 Project Overview

This project analysis trends in technology company layoffs over recent years. The goal is to identify which industries, countries, and company stages were mostly impacted and to uncover any temporal patterns through a comprehensive data cleaning and analysis.

🔧 Tools Used

• Microsoft Excel: For initial data cleaning
• SQL (MySQL): For advanced data cleaning, transformation, and analysis
• Power BI: For data visualisation and dashboard creation (Future enhancement)

📊 Dataset

The dataset contains information about tech company layoffs with the following columns:

• Company: Company name
• Location_HQ: Company headquarters location
• Industry: Industry sector
• Laid_Off_Count: Number of employees laid off
• Percentage: Percentage of workforce laid off (when available)
• Date: Date of layoff announcement
• Stage: Company development stage (Series A, B, C, IPO, etc.)
• Country: Company's home country
• Funds_Raised: Total funds raised by company (when available)

🗂️ Project Structure

Tech-Layoffs/
├── README.md
├── Tech Layoffs Scripts.sql
├── data/
│   └── (dataset would be here)
└── results/
    └── (analysis results would be here)

🧹 Data Preparation

The dataset underwent a two stage cleaning process:

1. Initial Exploration & Cleaning in Excel:

   • Performed an initial review of the dataset to understand its structure and identify obvious issues.
   • Used Excel filters to quickly identify and remove some duplicate entries, inconsistent formatting, and removed source links.
   • Replaced empty gaps with NULL values to standardise the missing data before converting to CSV for SQL-based analysis.
   • This step was crucial for getting a high-level understanding of the data before writing complex SQL queries.

2. Comprehensive Cleaning in SQL: The core cleaning was done mainly in SQL to ensure the process was clear and consistent.

   • Standardisation:
     • Trimmed extra spaces from company names using TRIM()
     • Converted date text to proper DATE type for accurate analysis
     • Standardised formatting across all text columns
   • Handling NULLs: Identified and removed records where critical fields (Laid_off_count, Percentage, Funds_Raised) were all NULL while preserving records with partial data.
   • Duplicate Removal: Created staging tables and used ROW_NUMBER() with PARTITION BY to systematically identify and remove exact duplicate records across all columns.
   • The detailed, step by step SQL code for this process is available in the Tech Layoffs Script.sql file.

🔍 Key Questions Explored

A comprehensive Exploratory Data analysis was done to answer the below questions:

1. What was the largest single layoff event (headcount and %)?
2. Which companies laid off their entire workforce (100%), and how many companies were there?
3. Which company had the highest total layoffs?
4. Among the 100% layoff companies, which had raised the most funding?
5. Which countries experienced the most layoffs?
6. Which industries and company stages were most affected?
7. How did layoffs trend over time (yearly, monthly, and cumulative rolling totals)?
8. What were the top 3 companies with the most layoffs each year?

📈 Key Findings

1. Largest Single Layoff Event (Headcount and %)

The Largest single layoff event (headcount) involved 14,000 employees.
The Maximum layoff percentage recorded is 100%, (represented as 1.0 in the dataset). This indicates that the most significant layoff event involved 14,000 employees, while the highest layoffs percentage represents companies that shut down entirely.

2. Companies with 100% Layoffs

A total of 86 companies experienced a 100% layoff, meaning they laid off their entire worforce. However, the number of employees affected in these 100% layoffs is generally smaller compared to the top 5 largest layoffs (Tesla, SAP, Dell, Getir, Cisco, and Toshiba).

3. Company with the Highest Total Layoffs

The company with the largest single layoff event was Tesla, with 14,500 employees laid off, followed by;

• Sap - 8,000
• Dell - 6,000
• Getir - 5,800
• Cisco - 4,600
• Toshiba - 4,000
• Xerox - 3,000
• Microsoft - 2,900
• VMware - 2,837
• Paypal - 2,500

Note: This number reflects the largest single layoff headcount in the dataset and not Tesla’s total workforce.

4. 100% Layoff Companies with Highest Funding

The company that raised the most funds before laying off 100% of it's employees was Convoy, a USA-based logistics company, raised $1.1 billion prior to the lay off of 500.

5. Countries with Most Layoffs

The country with the most layoffs is the United States with a count of 90,563, followed by:

• Germany: 9,049
• United Kingdom: 8,676
• India: 8,374
• Japan: 4,000
• Israel: 2,090

6. Industries and Company Stages Most Affected

The layoffs were not distributed evenly across the tech sector and specific industries and stages of company maturity beared the consequences of the workforce reductions

Most Affected Industries:
The Transportation industry was the most severely impacted by a signficant margin of 19,379 employees

• Other: 16,831 employees
• Hardware: 12,059 employees
• Retail: 11,640 employees
• Consumer: 10,920 employees

Most Affected Company Stages:
A clear majority of all layoffs occurred at mature, publicy traded companies

• Post-IPO: 79,000 employees
• Acquired: 16,732 employees
• Unknown: 13,304 employees

The result shows that the majority of layoffs were concentrated in large, established companies. The layoffs at Post-IPO companies were the most severe by far, with 79,000 job cuts when compared to other stages.

Key Takeaway: The scale of layoffs at the Post-IPO stage (79,000) and within industries like Transportation (19,379), Other (16,831), and Hardware (12,059) suggests these cuts were a strategic response to market corrections, investor pressure, and operational restructuring within more established sectors. This trend indicates that the downturn was felt most acutely by companies facing the public markets demands for profitability, rather than by early stage startups.

7. Layoff Trends Over Time

The analysis reveals a significant and accelerating trend in tech industry layoffs over the observed period.

Yearly Trend:
There was a massive 124% year over year increase in layoffs.

• 2023: 40,640 employees
• 2024: 90,916 employees (as of data capture)

Monthly Trend:
Layoffs were not evenly distributed. The beginning of 2024 saw an unexpected surge.

• The single worst month was January 2024, with 34,107 layoffs (over 37% of the entire year's total).
• Other high volume months include April 2024 (22,402), February 2024 (15,639), and August 2023 (10,178).

Cumulative (Rolling) Total:
The cumulative total shows a dramatic inflection point which started in early 2024, indicating a rapid increase in workforce reduction across the tech sector.

• The cumulative total grew steadily from 2,117 in July 2023 to 40,640 by the end of December 2023.
• In just the first month of 2024 (January), the cumulative total increased by 84%, jumping from 40,640 to 74,747.
• The cumulative total of layoffs reached 131,556 by June 2024.

Key Takeaway: The tech industry experienced a major wave of layoffs that began in the second half of 2023 and increased dramatically at the start of 2024, with January being the most severe single month. This suggests the downturn was a sudden and significant market correction that hit established companies early in the fiscal year.

8. Top 3 Companies with Most Layoffs Each Year

The top three companies by layoff count varied between 2023 and 2024, with an obvious increase in the scale of layoffs at the top

2023:

• VMware: 2,837 employees
• Getir: 2,500 employees
• Spotify: 1,500 employees

2024:

• Tesla: 14,500 employees
• SAP: 8,000 employees
• Dell: 6,000 employees

Key Takeaway: The scale of layoffs at the top companies increased dramatically year over year. The leading company in 2024, Tesla conducted layoffs that was over five times larger than the leading company in 2023 (VMware). This reveals the overall market trend of accelerated workforce reductions in the tech sector.

🚀 How to Use This Project

Prerequisites

• MySQL database system
• The tech layoffs dataset loaded into a database named Tech_Layoffs

Setup Instructions

1. Clone this repository
2. Ensure your MySQL database is running
3. Load your dataset into the Tech_Layoffs.layoffs table
4. Execute the SQL script in your MySQL client

Running the Analysis

Execute the entire SQL script to:

1. Clean and prepare the data
2. Perform all analytical queries
3. Generate insights about tech layoff trends

💡 Business Implications

This analysis provides valuable insights for:

• Investors: Understanding which sectors and company stages are most vulnerable to downsizing
• Job Seekers: Identifying potentially volatile sectors and companies
• Policy Makers: Recognising geographic regions most affected by tech industry changes
• Company Executives: Benchmarking against industry layoff trends

📝 Conclusion

This project demonstrates a complete data analysis workflow from raw data cleaning to actionable insights. The tech layoffs dataset offers a glimpse into how the technology sector has evolved and responded to economic pressures in recent years. Additionally, the analysis reveals patterns in how different company stages, industries, and geographic regions were affected by workforce reductions, providing valuable intelligence for various stakeholders in the tech ecosystem.

🎯 Acknowledgments

The dataset used in this analysis was sourced from:

• Title: Layoffs Dataset 2024 (2020 - 2024)
• Creator: Roger Lee
• Original Publisher: https://layoffs.fyi/
• Source: https://www.kaggle.com/datasets/theakhilb/layoffs-data-2022/data
• Date Accessed: August 22, 2025

Note: The specific dataset was downloaded on the above date. The source website may update layoff information continuously.*

• Inspired by the need to understand tech industry workforce trends and love for Data analyst❤️

🔮 Future Enhancement

1. Integration with Power BI for visualisation