How I Turned a 30-Minute Daily Task into a 5-Minute Automation (Saved 105 Hours a Year!)

From Excel Hell to Python Heaven

Flow diagram 2021 x 2025:

It was 2018.

If you knew how to make a pivot table in Excel, you were a prodigy. If you could automate something with VBA, you were a God.

Back then, I was responsible for a daily sales report that included 12 pages of KPIs, comparisons and charts. The report was built in a 20MB Excel file, packed with 30+ pivot tables. Every morning, I had to go through this 7-step dance:

Old Workflow (Manual)

1. Download 5 ERP reports in .xlsx format
2. Open the Excel template and clean the old data
3. Paste in the new data
4. Refresh 30+ pivot tables manually
5. Cross-check 12 sheets for consistency
6. Select multiple sheets with CTRL + Click
7. Export as PDF and send via email

📉 Performance:

**Daily Time Spent:** ~30 minutes  
**Annual Time Spent:** 126 hours (~7590 minutes)  
**Key Risk:** 100% dependent on 1 person

It was brittle. It was bloated. And if I was out sick or late, chaos would follow.

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The 2025 Automation Flow

After 3 months mapping out the steps and testing multiple automation options, I released a fully automated version by the end of 2021.

Here’s what the process looks like now:

New Workflow (Automated)

# Colab Script (simplified)
import gspread
import pandas as pd
import numpy as np

# Authenticate and connect to Google Sheets
# Process data from uploaded spreadsheets
# Update Sheets with cleaned + calculated metrics

1. Drop the .xlsx files into a Google Drive folder
2. Hit "Run" on a Google Colab notebook
3. Data is parsed and uploaded to 2 Google Sheets
4. Google Slides automatically pull the charts and tables
5. Click “Update Linked Objects” → Export as PDF → DONE!

📉 Performance:

**Daily Time Now:** ~5 minutes  
**Annual Time Now:** 21 hours (~1265 minutes)

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Net Savings

• 25 minutes saved daily
• 105 hours saved per year
• Zero manual steps
• No more Excel crashes
• No more single-person dependency

This wasn't about throwing AI at the problem. It was pure backend automation and smart tooling. I used:

• Google Colab for the Python backend
• gspread to connect with Google Sheets
• numpy/pandas for all the math
• Google Slides for a clean visual frontend

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Lessons Learned

• Optimization isn't always about new tech: it's about smarter flows
• Mapping the steps is 80% of the solution
• Public tools (Colab, Sheets, Slides) can scale more than we think
• Always measure the actual time cost of recurring tasks

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Have You Done Something Similar?

I’d love to hear from anyone who’s pulled off a process optimization like this. Especially if you did it with:

• Python
• Excel automation
• Google Workspace tools
• Airtable / Notion
• APIs and scraping

Feel free to drop a comment or share your wins!