[data-ottawa]

I hate the equivalence of data to A/B testing. If all your data people do is say a button should be yellow then you're not using data effectively.

You should be using data to invalidate your assumptions, separate the real from the perceived, and to draw those aha moments mentioned in the article. Then use that to prioritize and decide what’s worth iterating on and when its good enough to move on to bigger problems.

As the article says, data won’t tell you everything, which is why your data people need to also be product people, and not just sql monkeys or phds in a backroom doing analyses nobody will understand or read.