In How I Use Airtable I described some of my Airtables. In this post, I’d like to talk about how I think about Airtable vs. Spreadsheets vs. Apps. Often I’m asked why Google Sheets isn’t adequate. The answer is that it is, but in the same way that you can build a house given only nails, wood, and a hammer. Airtable gives you screws, cement, brick, and most other tools to build a 21st century home.
When I want to organize data, which at the end of the day is what most applications do, that data is uniquely mine. An app will impose someone else’s idiosyncrasies on my data. Countless apps for shopping lists exist, but they own my data and dictate how I will be using it. I can’t evolve a system that works uniquely for me from it. I religiously believe in Gall’s law that any complex, working system has to evolve from a simple system that works. I think that Airtable provides a unique opportunity for anyone to create their own unique systems. It’s no longer just for people who can code. In Silicon Valley lingo: Airtable is democratizing app-building.
While you may have the ambition to turn your idea into a full-blown app, that takes hours, days or weeks. Creating an Airtable for your first prototype to get intimate with the data and get something out there takes minutes. Some systems just don’t deserve the time investment of a full-blown app up front. Worse, good ideas never get started because the upfront cost is high. That’s why today any personal system I build starts as an Airtable. I follow this 4-phase system when prototyping with Airtable, starting with the Minimum Viable Airtable:
Phase 1: Minimum Viable Airtable
As an example, I organize books recommended by friends. While Goodreads has the
functionality to save books with a
to-read label, it doesn’t allow me to
capture people’s personal recommendations, which at the end of the day is what
matters most to the next book I end up reading. Instead of not solving the
problem or spending hours building an app disconnected from all other tools I
use, I built a simple Airtable in 10 minutes to keep track of books and their
This is already valuable by itself. I can share this with friends; I could even create an Airtable Survey for people to enter in their recommendations and share the view publicly. That’s a stellar prototype. At this point in the process, there’s nothing fancy going on at all. It’s a pure and simple Airtable. If I find enough value to iterate further into phase 2, I might. Most of my bases remain and thrive in phase 1.
Phase 2: Airtable with Integrations
If you’re spending a lot of time in your Airtable doing things that could be automated, it might be time to add some integrations. Zapier allows a stunning amount of automation with email, Slack, Evernote, or just about any other application you can think of. An example might be that you’d like to announce to a Slack channel (or email) when a lead in your table converts into a customer to congratulate the sales team! Or perhaps you integrate with a dashboard application to create graphs and dashboards from your Airtable data. This is the time to explore what other applications can do with your data. You can focus on automation and business logic, not how to present and modify the data. Presenting and modifying the data is often the most time-consuming part in an app’s infancy.
If you’re a developer(or know someone), you can use the Airtable API to write your integrations. As described in How I Use Airtable I’ve written integrations to create flash cards from Airtable records and automate my tea-brewing process. I wrote an API client for Ruby to make this as easy as possible. My favorite integration is a script that imports single-word Kindle highlights into Airtable to learn the words, later converted into flash cards.
The beauty is that any time invested in this automation you can leverage for other Airtables. My flash-card integration started as useful for one Airtable, but now I have about five using it. As more of your tables move to this phase, Airtable is becoming a razor sharp tool to solve an extremely broad array of problems.
In this phase, you’re building simple automation on top of the Airtable created in Phase 1. The time investment in the system is still small at this point, but you’re still getting a lot of value.
Phase 3: Almost-App, Heavy Integrations, Airtable Backend
This step is the awkwardly beautiful phase in between a full-blown application and something scrappy in Airtable. With the investments made in (1) and (2) you’re a master of your data, the domain, and the schema. You should already have developed opinions about the optimum way of organizing your data.
Airtable is your backend; you’re essentially treating it like any other database. You’re still using Airtable to get a view of your data and do some administrative duties, but some of this has been taken over by a customly written frontend or integrations. You might be the only person knowing Airtable backs it, showing other people a custom frontend supported by Airtable. This is the stage where you’ve found enough value in your Airtable to consider paying someone to help you write integrations.
Airtable is still providing value at this stage because you don’t have to move your data, you’re still prototyping, and you get an admin area for free by signing into Airtable.
Phase 4: Bye Airtable! I’m building an app!
If you reach this stage, congratulations. Your prototype has evolved all the way from Airtable to a full-blown application. Airtable taught you about the schema of your data and justified your time investment to make it from (2) to (4), making it easy to lounge from silly idea to scrappy execution. The layout of the data makes it easy to migrate from (3) to (4). Your idea is now validated to the point where you’ve decided to make it into an app. You migrate the data to your own database for maximum power and start building your app. A well-executed domain-specific application will beat an Airtable in many cases (if the system aligns with your own habits, otherwise a personal Airtable might beat it, as described in the intro). That’s why Airtable hasn’t replaced every application on my phone that deals with structuring of data, such as tracking weightlifting.
What started for you as an Airtable of Kindle highlights has turned into a multi-national vocabulary enhancing empire as you strengthen the vocabulary of 10,000s of people. What started as a book endorsement Airtable 6 months ago you made in 10 minutes has progressed to the world’s most prestiogious ranking of books about spirit animals (you found an unexpected niche). On the contrary, you found out that the world is not ready for the Airtable you built for optimizing five features of tea-brewing for perfection—but it’s working amazingly for you (and for your friends to tease you about), sitting patiently in phase 2.
Airtable has made you a millionaire, and this blog post has inspired you to participate in the MINIMUM VIABLE AIRTABLE (MVA) movement. You’ve become a vociferous advocate, endorsing Airtable left and right (even more than in phase (2)).
Let’s return back to the Spreadsheet problem raised in the introduction. Why not use spreadsheets? Spreadsheets are great, especially if you’re dealing with a massive amount of numbers and awkward data layouts. However, if your spreadsheet is well-structured, it inherently follows a relational model which Airtable enforces directly. Spreadsheets work well for (1), but they don’t work with (2) and (3) because Google Sheets’ API is horrendous to work with. Airtable shines through all 4 stages. Airtable’s API models the data in a way that’s identical to how a relational databases work. Something most developers will recognize, unlike Sheet’s cell-driven API. It makes the transition from (3) to (4) much more seamless. It makes writing integrations easier because all Airtables follow a structured design by default.
Additionally, Airtable has a beautiful user interface that it makes it easy to model your data correctly, the same way you would in a relational database. The recruiting team used Airtable to track hires for a while, and it was impressive to see the lengths they went to to clean up and structure the data. Great tools inspire great work.
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