What Is Data Cleaning and Why Is It Crucial?
DATAEN-US
Lucas Lumertz
6/6/20253 min read
Hey, what's up, everyone? I hope so! Have you ever tried to bake a cake with wrong or missing ingredients? If the sugar is salty or the flour is full of lumps, no matter how good the recipe is, the result will be bad. The same thing happens with data! If it's messy, incomplete, or wrong, any analysis done with it can lead to incorrect conclusions.
That's why today I'm going to explain to you what Data Cleaning is, why it's so important, and how to do it the right way. Let's go!
What Is Data Cleaning?
Data Cleaning is the process of fixing, correcting, and organizing data before using it for analysis. It's like washing and chopping the ingredients before cooking; you wouldn't put a dirty, whole tomato in a salad, right?
Some common problems that data cleaning solves:
Missing data (like a registration without an email).
Typographical errors (a misspelled name, like "Joãp" instead of "João").
Duplicate data (the same person registered twice).
Inconsistent format (dates written in different ways: 01/05/2023, 1-May-2023).
Impossible values (age = 150 years, height = 5 meters).
What Is It Used For?
Data cleaning is used to:
✔ Avoid errors in reports and analyses.
✔ Ensure that decisions made based on the data are reliable.
✔ Save time, because analyzing dirty data can lead to rework.
✔ Improve the quality of results (whether in business, health, research, etc.).
Simple Example: If you are calculating the average age of a group, but some records have "0" or "999" in the "age" field, the final result will be completely wrong!
Why Is It So Important?
Imagine these situations:
A hospital uses dirty data to study a disease and may arrive at dangerous conclusions.
An online store has wrong prices because of unverified data and could lose sales.
A bank fails to detect duplicate customers and might approve credit twice for the same person.
Uncleaned data = bad decisions = losses. That's why companies and data professionals spend up to 80% of their time on a project just cleaning and organizing the data!
Tools for Data Cleaning:
Fortunately, there are tools that help with this work and make our lives much easier. Let's separate a few by level:
For Beginners:
Excel/Google Sheets → Filters, duplicate removal, formulas like VLOOKUP/XLOOKUP.
OpenRefine → A free and easy-to-use tool for cleaning data manually.
For Those Who Already Know Programming:
Python (Pandas) → A powerful library for automating the cleaning process.
SQL → For cleaning data directly in databases.
Advanced Tools:
Trifacta → Professional software for automated cleaning.
DataWrangler → A visual tool for quickly tidying up data.
Besides these, obviously, many others exist. The ones above are just a few examples of what we can use.
Examples of Use Cases:
Now, to make it even clearer and more sensible, I'm going to list a few use cases so you can associate everything I've said so far.
1. E-commerce (Amazon, Mercado Livre):
Problem: Products registered with wrong prices (R$ 1.00 instead of R$ 100.00).
Solution: Using Data Cleaning to automatically correct discrepant values.
2. Medical Research:
Problem: Patients with incomplete data (tests without results).
Solution: Removing or completing missing records before analysis.
3. Social Media (Instagram, X/Twitter):
Problem: Fake accounts or bots with repeated names.
Solution: Identifying and removing duplicates to improve statistics.
Recap and Conclusion
Well, that's all for this article, everyone. Let's summarize what we learned today:
🔹 Data Cleaning is the process of tidying up data before analyzing it.
🔹 It serves to prevent errors and ensure that decisions are based on reliable information.
🔹 It is crucial because dirty data leads to losses in business, health, finance, and more.
🔹 Tools like Excel, Python, and SQL help automate the cleaning process.
If you work with data (or want to start), remember: Clean Data = Reliable Analysis = Better Results.
So, have you ever needed to clean data? Tell me about your experience in the comments! 🚀
📌 Want to learn more about data analysis? Follow me for more content like this!
That's it for today, everyone. All the best, and until the next topic. 😊