layoffs_analysis

Analysis of Layoff in Tech industry

Context

Tech firms around the globe are fighting the economic slowdown. The slow consumer spending, higher interest rates by central banks and strong dollars overseas are hinting toward a possible recession and tech firms have started laying employees off. This economic slowdown has made Meta recently firing 13% of its workforce, which amounts to more than 11,000 employees. This dataset was made with the hope to enable Kaggle community to look into analyzing recent tech turmoil and discover useful findings.

Tracking the tech layoffs reported on the following platforms:

• Bloomberg
• San Francisco Business Times
• TechCrunch
• The New York Times

The data availability is from when COVID-19 was declared as a pandemic i.e. 11 March 2020 to present (11 November 2022).

Data Source

The source of the layoff dataset is Kaggle. Startup layoffs as reported on Layoffs.fyi from COVID (11 March 2020) to today.

Technologies

• Database: BigQuery
• Data Transformation Tool: dbt

Analysis

Based on analysis I found in Kaggle, these are the transformations that will be applied on the staging model:

Transformations in the staging layer:

• industry, total_laid_off, percentage_laid_off, stage and funds_raised columns contain null values
  • industry - Convert NULL to 'Unknown'
  • total_laid_off - Convert NULL to 0
  • percentage_laid_off - Convert NULL to 0
  • stage - Convert NULL to 'Unknown'
  • funds_raised - Convert NULL to 0
• Data types conversions:
  • company - VARCHAR
  • location - VARCHAR
  • total_laid_off - INTEGER
  • percentage_laid_off - FLOAT
  • date - TIMESTAMP
  • stage - VARCHAR
  • country - VARCHAR
  • funds_raised - FLOAT
• location - Convert 'SF Bay Area' to 'San Francisco'
• Generate an auto-incrementing identifier AS id
• Add ingested_at column that represents the timestamp at which the row was ingested by dbt.

Business Metrics

Based on the operational data, I divided my analysis based on industry, country and stage. This will allow seeing the number of layoffs by industry, country and company stage.

Staging models

• stg_layoffs is the staging model. It bears a one-to-one relationship with the source data table it represents. It has the same granularity, but the columns have been renamed or recast.

Marts models

• layoffs_by_industry is the model that aggregates the number of layoffs by industry.
• layoffs_by_country is the model that aggregates the number of layoffs by country.
• layoffs_by_funding_stage is the model that aggregates the number of layoffs by funding stage.

Data Lineage