Ever looked at a daily task and thought: “Why are we still doing this manually?”
I did.
One of the processes I worked on involved a daily assignment report.
The old process was simple, but repetitive:
Portal → Fetch assignments → Update report → Update SharePoint
Every day. 🔄
So I asked myself:
What if this could run without someone doing it manually?
💡 What I changed
I built an automated flow where:
SQL Query → Fetch required assignments
⬇️
Task Scheduler → Runs the process automatically
⬇️
Data Processing → Prepares the report
⬇️
SharePoint → Automatically updates the report
So the daily routine changed from:
❌ “Let me fetch the report and update SharePoint.”
to:
✅ “The process has run and the report is ready.”
And along the way, I learned a few things that go beyond writing code.
1️⃣ Don't automate the task. Understand the problem first.
My first instinct was:
“Let's write a script!”
But that's not where automation should start.
Before writing code, I had to understand:
👉 Where does the data come from?
👉 What happens to it after extraction?
👉 Which validations are performed?
👉 Which steps are repetitive?
👉 Where do errors usually happen?
👉 What should the final output look like?
And I realized something important:
You can't automate what you don't understand.
Sometimes the biggest challenge isn't coding.
It's understanding the process.
2️⃣ Find the repetitive 80%
Not every step needs to be automated.
Some decisions require human judgment.
But if you're repeatedly doing things like:
🔹 Opening files
🔹 Copying data
🔹 Formatting columns
🔹 Applying the same rules
🔹 Performing repetitive calculations
🔹 Generating reports
🔹 Uploading the final output
...that's a signal.
That's where automation can create real value.
The goal isn't:
❌ “Remove humans from the process.”
The goal is:
✅ “Remove unnecessary repetitive work from humans.”
3️⃣ Automation starts with data — and data can be messy!
Here's something I learned very quickly:
Your automation is only as reliable as the data it receives.
What happens when:
A column is missing?
A date is in the wrong format?
A record appears twice?
A value is blank?
Someone changes the Excel structure?
Your script can't simply assume that everything will always be perfect.
That's why validation became an important part of the automation.
Instead of:
Extract → Process → Output
the better approach is:
Extract → Validate → Process → Verify → Output
Because a fast wrong report is still a wrong report. 😄
4️⃣ “What if it fails?” is one of the most important questions.
Imagine your automation runs every morning.
But one day:
🚨 The input file isn't available.
Or:
🚨 A required column is missing.
Or:
🚨 The source system isn't accessible.
What happens next?
This made me realize that error handling isn't an extra feature.
It's part of the automation itself.
A reliable solution should tell you:
✅ Did the process start?
✅ Did the input arrive?
✅ Did validation pass?
✅ Was the report generated?
❌ If something failed, where did it fail?
Automation should not silently fail.
It should tell you what happened.
5️⃣ The real success isn't “I wrote a script.”
This was probably my biggest takeaway.
Writing Python code that works is one thing.
Building an automation that people can trust is another.
A successful automation should provide:
Accuracy + Reliability + Consistency + Maintainability
The real achievement isn't:
“I automated this task.”
It's:
“This process can now run reliably with significantly less manual effort.”
And that's a much more meaningful outcome.
💭 One question I'm taking forward
Every time I come across a repetitive task now, I try to ask:
“If I'm doing the same thing repeatedly, should I really be doing it manually?”
Maybe the answer is yes.
But maybe...
it's an opportunity to automate. 🚀
What about you?
What's one repetitive task in your work that you wish you never had to do manually again?
Drop it in the comments. 👇
It might just be the next thing worth automating.
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