What should my experimentation data stack look like considering the maturity of the program and the size of the company?

Every experimentation program is different. Some run a few experiments a month while others have hundreds of experiments running simultaneously. Some rely on a simple client-side tool while others have advanced server-side setups.

Different programs require different tools. This goes for the experimentation data stack, too.

While a small experimentation program can do everything using just their testing tool, more experienced users are looking for a richer dataset that's often found in their analytics tool (i.e. GA4). But that's nothing compared to the most advanced setups that are warehouse-native, data moves in multiple directions between various platforms and reporting is automated.

Use this blueprint to better understand what an ideal data stack might look like for your experimentation program.

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