I’m currently assisting a non-profit with their Airtable base. It’s a massive project with 30+ years of data. The main table has about 14k records and 250 fields each. These are global environmental project proposals for micro-grants.
The database is pretty messy. It’s been put together by different people over time. They meant well but didn’t really get how relational databases work. So now I’m dealing with too many fields, weird table setups, and loads of views.
I’m finding it tough to organize everything. I’ve made some cool Airtable bases before, but this is next level. I need help tackling this monster!
Here’s a specific problem I’m facing: How can I split a text field with two phone numbers (separated by a comma) into two separate fields?
Update: I sorted it out by using Excel. I just copied the field there and split it into two columns. Sometimes the simple solutions slip your mind when you’re overwhelmed! Thanks for the nudge in the right direction.
Wow, that’s quite the undertaking! I’ve been in similar situations with large, messy databases, and it can be overwhelming. One approach that’s worked well for me is tackling it in stages. Start by identifying the most critical data points and focusing on cleaning those up first. For the phone number issue, I’m glad you found a solution with Excel. That’s often my go-to for quick data manipulation too.
For the broader organization, have you considered creating a separate ‘clean’ base and gradually migrating the data over? This allows you to build a more logical structure from scratch while keeping the original intact. It’s time-consuming, but it can lead to a much more usable end result.
Don’t hesitate to reach out to Airtable’s support team either. They’ve been surprisingly helpful when I’ve faced complex issues in the past. Good luck with your project!
Having worked with large environmental datasets before, I can relate to your struggle. One approach that’s proven effective is to start by creating a data dictionary. This involves documenting each field, its purpose, and data type. It helps identify redundant or unnecessary fields and provides a clear picture of the database structure.
For complex data cleaning tasks, I’ve found Python with pandas library invaluable. It can handle large datasets efficiently and offers powerful data manipulation tools. You might consider learning some basic Python if you haven’t already.
Regarding the overall organization, consider implementing a star schema design. This involves creating separate tables for different aspects (e.g., projects, locations, funding sources) with a central fact table. It can significantly improve query performance and data integrity.
Remember, transforming a 30-year-old database is a marathon, not a sprint. Take it step by step, and don’t hesitate to consult with domain experts to ensure you’re preserving all crucial information.
hey there! sounds like you’ve got ur hands full with that database. glad u figured out the phone number thing - excel can be a lifesaver sometimes. for the bigger picture, maybe try breaking it down into smaller chunks? like focus on one part at a time. also, have u thought about using some automation tools? they could help speed things up. good luck with ur project!