Data Portability Is Table Stakes For Community Builders In The Age Of Ai

With the growing ubiquity of AI, we’re living through a transformation that no one fully understands. When it comes to how we access the knowledge created by online communities, we should all look with skepticism to any predictions about what the future looks like. 

How should we be thinking about the design of communities and the platforms on which they’re built?

One thing I feel more certain about is that community builders should consider data portability as table stakes when choosing which platform they should focus on in their community building efforts.

When community everywhere becomes community nowhere

The observation that online communities extend beyond any one platform led to the adoption of “community everywhere” strategies, focusing on meeting people where they are and engaging with them in the platforms they have chosen. It makes sense, and community builders who focus on their chosen platform exclusively do themselves a disservice. But that doesn’t mean it makes sense to completely relinquish control.

Large social media platforms have a history of changing their algorithms or imposing new limits on access to their APIs. Smaller community platforms change their pricing and terms, get acquired, or simply shut down. Search engines, and now AI search tools, change how they crawl available data and incorporate it into their index or models.

Through these changes, community builders have learned to navigate transitions, but they are often more painful than they need to be. 

While it makes sense to meet people where they are, it is prudent to anticipate more changes like these and build a home for your community on a platform where you control the data you create together and can take it with you when a new platform will better serve your needs.

Community SEO and how AI changes the game

Community managers have long prioritized SEO, recognizing its value for both users and organizations. Discoverable content not only helps people find answers but also drives traffic and attracts new members

With more people turning to AI for answers, high-quality, structured data is becoming essential for generating high-quality results.

While we’re seeing strong evidence that AI will mean less of certain kinds of traffic, as users are able to find answers they are looking for without clicking through to their source, the data itself is even more valuable, and its portability matters now more than ever.

Why lock-in limits growth

Community builders aren’t the only ones who understand that they have limited control over their data on certain platforms. Your most valuable members understand this too, and will be more willing to invest time into their contributions when they know they have a better chance of continuing to provide value to others. 

This is especially true for communities centered around technology, software products, and professions or other subjects where access to expertise is essential.

What data portability means in practice

With true data portability, it should be possible to access data at any time, independently validate its integrity, and be able to migrate to another provider or platform.

It should be possible to easily create a backup of your data, export the entire data set, or have a read-only mirror of your data on a system you control.

The schema of your data should be documented or self-documenting, and there should be tools to import it into another platform or a system you control.

While you can make these guarantees contractually or orchestrate  technical systems to bypass closed source community tools, open source platforms provide these guarantees implicitly.

Open source makes independently verifiable portability easy

With an open source platform, the data format is documented by the software itself, verified by automated tests anyone can inspect, and validated by a community of people who self-host the platform, whether or not you choose to do so yourself.

Open source platforms often maintain open source tools to migrate data to their platform, and their schema is available for people who want to develop tools to migrate data in the opposite direction.

Identifying your first steps towards greater data portability

Some questions that may be worth exploring with your team:

• What terms are in place to ensure we have access to our data (or that might prevent us from doing so)?
• Are we able to take backups of our data ourselves to ensure we have a snapshot of our data somewhere in our own control?
• How frequently should we take backups or validate a read-only replica of our data?
• If backups aren’t currently possible, are there other ways we can copy important parts of our data? For example, using our platform’s API or a scraper or crawler?
• Are there other ways we can leverage that data today, for example for deeper analytics or making it available to our own AI tools or other integrations with systems we control?
• How difficult would it be for us to move platforms tomorrow if we suddenly found ourselves in a situation where we felt this was urgent?

Consider which aspects of data portability are most important to you, your community, and your business. Build a sense of where you’d like to be eventually, but understand your current reality and the constraints of your current situation. 

As you explore these questions, decide what improvement to make next and what time frame you think is feasible to do so.

Data portability is a spectrum, so no matter where you are today, it’s not too late to start thinking about where you want to be and how you might get there.