Clustering data to unveil Maji Ndogo's water crisis
# SQL PROJECT 2 | 💧 Maji Ndogo Water Crisis – Turning Data into Community Insights
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## 👤 Author
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**Omotola Lawal**
📅 12th June, 2025
🔗 LinkedIn
## 📑 Table of Contents
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1. 📘 Project Overview – Setting the stage for our data exploration journey
2. 🗂️ Cleaning Our Data – Updating employee data
3. 🙌 Honouring the Workers – Finding our best
4. 🌍 Analysing Locations – Understanding where the water sources are
5. 💦 Diving into the Sources – Seeing the scope of the problem
6. 🛠️ Start of a Solution – Thinking about how we can repair
7. 📊 Analysing Queues – Uncovering when citizens collect water
8. 📝 Reporting Insights – Assembling our insights into a story
9. 🛠️ Practical Solutions – Recommendation
10. 📑 Reference
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## 📘Project Overview – Setting the stage for our data exploration journey
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This project is personal to me because it goes beyond running SQL queries — it’s about telling the story of the **Maji Ndogo water crisis** through data.
When I first explored the dataset, I didn’t just see rows and columns. I saw communities waiting in long queues, families depending on unsafe water, and field workers trying their best to bridge a massive gap.
Here’s how I approached it:
- I started by **cleaning and validating the data** to make sure the foundation was trustworthy.
- Then, I used **clustering and aggregation** to uncover the bigger narratives hidden beneath isolated records.
- I treated each data point as someone’s lived experience — not just a number — which made **integrity checks and auditing** essential.
- Finally, I zoomed in on both **granular details** (individual sources, employees, queues) and the **big picture** (provincial coverage, quality, and availability).
For me, this project isn’t just about technical SQL skills. It’s about showing how **data can become a voice** — turning hidden patterns into insights that can guide real-world solutions for the communities who need them most.
✅ **Skills Applied:** SQL · Data Aggregation · Cou …