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What I’ve Learned About Data Cleaning (and Why It’s Critical)
Data cleaning isn’t just the first step—it’s the backbone of any reliable analysis.
From Excel checks to SQL validations, I’ve learned that clean data = smart decisions.
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What I’ve Learned About Data Cleaning (and Why It’s Critical)
If there’s one thing I underestimated when stepping into analytics, it was the importance of data cleaning. Everyone talks about insights, dashboards, and SQL queries—but few highlight how data cleaning forms the foundation of every decision we make.
💡 Here’s the truth:
Bad data = bad insights = bad decisions.
Clean data, on the other hand, empowers business stakeholders to trust our reports and act with confidence.
🔍 My Learnings on Data Cleaning:
- Garbage in, garbage out:
You can’t analyze what you don’t understand or clean. Raw data is often messy—missing values, inconsistent formats, typos, duplicate entries, and irrelevant rows. - Tools won’t save you from messy logic:
Whether it’s Excel, SQL, or Python—you must know what to clean and why. Tools are just a means; the thinking must come first. - Understand the source:
Knowing where the data is coming from (API, manual sheet, CRM, etc.) helps you assess its quality and reliability. At Swiggy, I always validate raw API pulls using cross-checks in Snowflake or the peak payout sheets. - Create a checklist before cleaning:
- Null or missing values
- Outliers or extreme data points
- Format mismatches (dates, text vs. number)
- Duplicates
- Inconsistent labels or spellings
- Misaligned columns
I apply this list regularly while preparing Instamart and GIG payout datasets.
- Automate what’s repetitive:
At GlobalLogic, I built semi-automated Excel models with macros to handle repetitive tasks like column formatting, blank checks, and conditional filters. - Maintain a ‘Raw vs Cleaned’ sheet:
Especially when sharing with stakeholders—showing a before/after helps build transparency and shows the depth of your effort. - Data cleaning isn’t one-time:
It’s iterative. Each refresh, new dataset, or new Jira ticket at Swiggy brings unique issues, which means continuous refinement.
✅ My Go-To Tools for Data Cleaning:
| Tool | Use Case |
|---|---|
| Excel | Quick filters, blanks, formatting, conditional color checks |
| SQL (Snowflake) | Null checks, distinct counts, data type validation |
| Power BI | Handling model relationships, removing duplicates |
| Google Sheets + API outputs | Revalidating automated outputs from Postman |
💭 Final Thought
Data cleaning may not be glamorous, but it’s where analysts prove their value. Without clean data, the fanciest dashboards mean nothing. I’ve come to respect this step just as much—if not more—than the analysis itself.
🔄 Coming up next:
📢 “Stakeholder Communication – What No One Teaches You” – Let’s talk about the human side of analytics!
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Have your own horror story with messy data? Or a go-to trick for fast cleaning?
💬 Drop your tip or question in the comments—I’d love to learn from you too!
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