🎉 Hey there, data aficionados! Are you tired of dealing with messy, inconsistent data? Today, we’re going to show you how to clean up your data effortlessly using Bubble.io and Make.com (formerly Integromat). We’re diving into a real-world example where we convert inconsistent age data into a uniform format. Let’s get started! 🚀

Why Data Cleanup is Crucial 🧹

Data is the lifeblood of any business. But what happens when your data is inconsistent? It leads to inaccurate reports and poor decision-making. Our client had collected age data in various formats over the years, creating chaos in their database. Some entries were numbers, while others were text strings like “30 years” or “3 months.” This inconsistency made running accurate reports a nightmare.

Step-by-Step Data Cleanup Process 🛠️

Let’s break down the process we followed to clean up the data:

1. Set Up a Filtered View in Airtable 🔍

First, we created a filtered view in Airtable to isolate records containing the word “years.” This made it easier to apply the same logic to all the data in that field. For example, if the field contains “30 years,” we only need to extract the number 30.

2. Use Make.com to Automate the Cleanup 🤖

We utilized Make.com to automate the data cleanup. Here’s a quick overview of the steps:

• Search Records: We used the “Search Records” module to pull in all records from our filtered view in Airtable.
• Extract Numbers: We applied a simple script to extract the number from the text string. For example, “30 years” becomes 30.
• Update Records: We updated the records in Airtable with the extracted number.

3. Run the Script 🏃‍♂️

After setting up the script, we ran it to process the data. This script can handle up to 1,000 records at a time. If any records are missed, simply rerun the scenario, and it will process correctly.

Benefits of Automation 🌟

Why spend hours manually cleaning up data when automation can do it for you in minutes? Here are some benefits:

• Time-Saving: A task that would take hours manually can be done in 5-10 minutes.
• Cost-Effective: Using Make.com for this process is incredibly cheap, costing roughly a dollar for several thousand operations.
• Accuracy: Automated scripts reduce the risk of human error, ensuring your data is accurate and reliable.

Advanced Tips for Data Cleanup 🧠

For more complex cases, consider using regular expressions (regex) to extract specific sets of numbers. For example, you can match the first set of numbers or apply more complex logic to match subsequent sets. Make sure to turn off global match in your regex settings to get precise results.

Conclusion 🎯

Data cleanup doesn’t have to be a daunting task. With the power of Bubble.io and Make.com, you can automate the process, saving you time and effort. So, why wait? Start automating your data cleanup today and enjoy cleaner, more reliable data for your business. 🌐

Happy automating! 🚀