📊 Kimia Farma Performance Analytics

This project is part of the Project-Based Internship: Big Data Analytics at Kimia Farma, conducted through Rakamin Academy.
The goal of this project is to analyze Kimia Farma’s business performance (2020–2023) using Google BigQuery and Google Looker Studio to generate data-driven insights that support strategic decision-making.

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🏢 About Kimia Farma

Kimia Farma is Indonesia’s first pharmaceutical company, founded by the Dutch East Indies Government in 1817 under the name NV Chemicalien Handle Rathkamp & Co.
In 1958, the Indonesian government nationalized several Dutch pharmaceutical companies into Perusahaan Negara Farmasi Bhinneka Kimia Farma.
On August 16, 1971, it became PT Kimia Farma (Persero), which continues to be a major player in Indonesia’s pharmaceutical industry.

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🎯 Project Description

This project focuses on analyzing Kimia Farma’s transactional data to evaluate sales performance, branch distribution, and customer satisfaction.
The dataset includes transaction, product, customer, and branch rating data from 2020 to 2023, stored and processed in Google BigQuery.

Problem Statements

1. What are the sales trends of Kimia Farma from 2020–2023?
2. Which provinces contribute the most to sales performance?
3. Is there a relationship between branch ratings and transaction ratings?

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🧩 Tools & Technologies

• Google BigQuery – Data processing and SQL querying
• Google Looker Studio – Data visualization and dashboarding
• Microsoft Excel – Data validation and initial aggregation

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📂 Repository Structure

📁 kimia-farma-performance-analytics/
│
├── 📂 sql/
│ └── pembuatan_tabel_kf_analisa.sql
│
├── 📂 dashboard/
│ ├── dashboard_complete.pdf
│ ├── dashboard_link.txt
│ └── dashboard_description.md
│
├── 📂 documentation/
│ ├── insight_summary.md
│ ├── presentation_link.txt
│ └── FinalTask_KimiaFarma_BDA_Dwi Budi Setyonugroho.pdf
│
├── README.md
└── LICENSE

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📊 Dashboard

All visualizations were built using Google Looker Studio.
For detailed chart explanations and insights, refer to:
📄 Dashboard Description & Interactive Links

Files in the dashboard folder:

• dashboard_complete.pdf – Full PDF version of the dashboard
• dashboard_link.txt – Direct link to the interactive Looker Studio dashboard
• dashboard_description.md – Detailed description and insights for each chart

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📚 Documentation

The Documentation folder contains all supporting materials and deliverables that complement the analytical dashboard and report.

Files included:

• insight_summary.md – Summary of analytical findings and insights.
• presentation_link.txt – Google Drive link to the project presentation file.
• FinalTask_KimiaFarma_BDA_Dwi Budi Setyonugroho.pdf – Final project report in PDF format summarizing methodology, analysis, and results.

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🧩 SQL

The SQL folder contains the main query script used to process and transform Kimia Farma’s business data in Google BigQuery.

File included:

• pembuatan_tabel_kf_analisa.sql – Complete SQL script for creating and analyzing the kf_analisa table, including data preparation, joins, and aggregation logic.

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📈 Key Insights

1. Sales Trend (2020–2023)
   Overall sales remained relatively stable across the observed years.
   Strategic marketing initiatives and operational improvements are needed to drive sustainable growth.

2. Sales Distribution by Province
   West Java contributed the highest proportion of sales to Kimia Farma’s total revenue, indicating significant market potential and customer concentration in the region.

3. Branch vs. Transaction Ratings
   High branch ratings did not always align with high transaction ratings.
   Service quality and customer experience improvements are necessary to enhance overall satisfaction and transaction frequency.

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👨‍💻 About the Author

I’m Dwi Budi Setyonugroho, a Geological Engineering graduate with a deep passion for Data Analytics.
Currently pursuing the IBM Data Analyst Professional Certificate on Coursera, I continuously develop analytical and visualization skills to build a strong data-driven career foundation.

Core Skills:

• Data Analysis: Advanced Excel, SQL (JOINs, Aggregation, Subqueries, CTE)
• Programming: Python (Pandas, NumPy, Seaborn, Matplotlib)
• Visualization Tools: Google Looker Studio, IBM Cognos Analytics, Power BI

Driven by curiosity and precision, I aim to uncover meaningful patterns from data and transform them into actionable business insights that support strategic decisions.

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📜 License

This project is licensed under the MIT License.
See LICENSE for more information.