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The First Time I Used SQL to Solve a Real Issue
I used SQL for the first time to fix a real payout issue at Swiggy—spotting duplicate orders that Excel couldn’t handle.
That one smart query saved hours of manual work and proved how SQL can be a game-changer for analysts.
The First Time I Used SQL to Solve a Real Issue
There’s a big difference between learning SQL and actually using it to solve a messy, real-world business problem.
For me, that transformation happened when I was knee-deep in incentive data chaos for gig workers at Swiggy. Here’s the story. 👇
⚠️ The Real-World Problem:
Our gig worker incentive model relied on tracking completed orders during peak hours. But our system was over-reporting — payouts were exceeding forecasts.
Something wasn’t adding up.
After digging in, we found a critical issue:
Duplicate entries of the same order were inflating payouts and distorting performance metrics.
🧮 The Initial Manual Approach:
Before SQL, the usual method was to export data into Excel, apply filters, do VLOOKUPs, and try to manually find patterns.
But we were dealing with:
- 10,000+ records daily
- Multiple data columns (timestamps, user IDs, incentives, etc.)
- Repeated reporting across zones
Manual detection was slow, error-prone, and mentally exhausting.
🧠 How SQL Became the Game-Changer:
I realized this was the perfect opportunity to apply my growing SQL knowledge.
Here’s how I solved it step by step:
| 🔢 Step | 💻 Action | 🧰 Tools Used |
|---|---|---|
| 1 | Imported raw order data into SQL Server | SQL Server |
| 2 | Identified fields causing duplication (order_id, timestamp, gig_worker_id) | SQL |
| 3 | Wrote a query using GROUP BY and HAVING COUNT(*) > 1 to isolate duplicates | SQL |
| 4 | Applied ROW_NUMBER() logic to flag true duplicates based on timestamp priority | SQL |
| 5 | Exported cleaned data for incentive processing in Excel | SQL + Excel |
| 6 | Automated this weekly task using a reusable SQL script | SQL |
💬 Sample Query Snippet:
sqlCopyEditSELECT order_id, COUNT(*) AS duplicate_count
FROM orders
GROUP BY order_id
HAVING COUNT(*) > 1;
Then, I filtered only the first valid instance of each order using:
sqlCopyEditWITH RankedOrders AS (
SELECT *, ROW_NUMBER() OVER (PARTITION BY order_id ORDER BY timestamp ASC) AS rn
FROM orders
)
SELECT * FROM RankedOrders WHERE rn = 1;
💥 The Outcome:
| ✅ Before SQL | ✅ After SQL |
|---|---|
| 3+ hours/week spent on Excel checks | Automated checks in under 10 minutes |
| Frequent human errors | Near 100% accuracy |
| Disconnected insights | Connected order IDs with gig performance |
| Manual duplicate tagging | Dynamic identification using ROW_NUMBER() |
| Reactive reporting | Proactive issue spotting |
The operations team was able to:
- Save time ⏱️
- Maintain payout integrity 💰
- Resolve technical glitches causing duplicate logs ⚙️
- Appreciate the true power of SQL 🎯
And me? I received my first ever direct shoutout on Slack for saving the day — and a boost of confidence in my BA journey! 🌟
🔍 Lessons That Stuck With Me:
- SQL isn’t just a querying language — it’s a business lifeline.
- You don’t need fancy dashboards to solve problems. Sometimes, a smart query does the job.
- Business Analysts aren’t just data readers — we’re data problem solvers.
- The cleaner the input data, the better the business outcome.
- Automation in analysis = long-term productivity.
📣 Final Thoughts:
That incident made me realize:
💬 “Learning SQL is great, but using SQL to bring real value? That’s when the magic happens.”
Today, it’s my go-to tool for anything data — from cleaning, filtering, transforming to delivering real insights.
🧭 What’s Coming Next?
Ready to go from querying data to visualizing performance?
Tomorrow, I’ll share how I use Power BI to track and present Gig Worker performance through interactive dashboards at Swiggy! 📊🚀
🏷️
#SQLJourney, #ProblemSolving, #BusinessAnalysis, #AnalyticsWithSakshamPulak, #EfficiencyMatters, #DataCleaning, #SQLforBusiness, #RealWorldSQL, #CareerGrowth, #SakshamPulak
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