Common data model mistakes made by startups
After helping build out a few startup’s analytics stacks, one starts to see some patterns. Sometimes these patterns are happy ones. Just about everyone loves the moment when you go from having no idea what’s going on, to having a foggy idea of what happened last week.
It’s important to note that the anti-patterns we’ll discuss below are specific to startups. Some of these patterns are actually good ideas for later-stage companies, but for small, pre-product-market-fit, resource-constrained startups, these are mistakes you don’t need to make.
Whether it’s test accounts, staff accounts, different data programs, or orders that come in through feline telepathy, too many companies include data that require you to ignore certain events or transactions in many or most of your queries.
By polluting your database with test data, you’ve introduced a tax on all analytics (and internal tool building) at your company. You can balance this tax against transactional efficiency, or developer productivity. Sometimes this tax is worth it, sometimes it isn’t. For large companies, transactional efficiency is an important enough goal that you can afford to spend a couple engineers’ or analysts’ time to clean up the results.
