GCP: People Analytics with Google Cloud SQL and Tableau

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About the Project

In this project, I explored the domain of People Analytics, emphasizing its value in making data-driven decisions about a company’s most critical resource — its people.

Through this journey, I:

• Learned the fundamentals of People Analytics and its real-world applications.
• Generated realistic HR datasets using Python.
• Processed and stored the data using Google Cloud SQL.
• Performed ad-hoc reporting and query-based exploration with SQL.
• Built an interactive dashboard in Tableau focused on employee demographics and income trends.

The end goal was to complete the full People Analytics cycle — from data wrangling to insight generation.

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What is People Analytics?

People analytics is the practice of collecting and analyzing workforce data to convert it into actionable insights that improve organizational outcomes.
— Visier

Also known as Workforce Analytics or HR Analytics, People Analytics focuses on employee-centric data and helps HR professionals solve problems like:

• High turnover
• Low engagement
• Skill gaps
• Diversity and inclusion metrics

Note: While traditional reporting provides static summaries, people analytics uses advanced techniques to uncover patterns, predict outcomes, and inform strategic decisions.

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The Three Pillars of People Analytics

Source: Visier

1. Data
   Sourced from tools like HRIS, ATS, HCM, and payroll systems — the raw foundation of people analytics.

2. Analytics
   The process of transforming raw data into insights using statistical models, SQL queries, and machine learning.

3. Insights
   Delivered in the form of dashboards, charts, and reports that support decision-making at all organizational levels.

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What is a People Analytics Strategy?

A People Analytics strategy outlines how to systematically use workforce data to solve business challenges.
— Visier

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Tools & Technologies Used

Tool	Purpose
Python	Data generation & preprocessing
Google Cloud SQL	Data warehousing & query storage
SQL	Ad-hoc analysis and metrics tracking
Tableau	Data visualization & dashboarding

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Sections

1. Process

• Dataset: Started with ~9,000 records in a flat structure.
• Database Design:
  • Created a star schema (fact/dimension tables) for optimized analytics.
  • Designed the ERD using SmartDraw.
  • Implemented the schema via PostgreSQL (using PG Admin).
• ETL Pipeline:
  • Loaded raw data into a staging table.
  • Transformed/cleaned data before loading into the star schema.
• Scalability & Cloud Migration:
  • Migrated the database to Google Cloud SQL for accessibility and reporting.

2. Analytics & Reporting

• SQL Reports: Wrote 15 SQL queries to answer key business questions.
• Visualization:
  • Initially planned to use Looker (GCP integration issues prevented this).
  • Switched to Tableau, built a semantic model, and designed an interactive dashboard.
  • Dashboard features:
    • Personnel demographics (gender, age, education).
    • Income trends by location/role.
    • Other HR-centric KPIs.

3. Development Approach

• Agile (Scrum):
  • 4 iterations (1 day each) due to time constraints.
  • Focused on incremental improvements (data model → reports → dashboard).
• Inspiration:
  • Credit to Data With Baraa for dashboard design ideas.

4. Tools & Technologies

• Database: PostgreSQL (local + Google Cloud SQL).
• Design: SmartDraw (ERD).
• ETL: SQL scripts for staging → star schema.
• Visualization: Tableau.

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Key Outcomes

• Optimized star schema database for analytics.
• Shareable cloud-hosted SQL reports.
• Interactive Tableau dashboard for HR insights.

"Without data, you're just another person with an opinion."
— W. Edwards Deming

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