Analytics craft

Doing analytics is hard. Doing analytics right is even harder.

There are a massive number of factors to consider: Is data missing? How do we make this insight discoverable? Why is my database locked? Are we even asking the right questions?

Compounding this is the fact that analytics can sometimes feel like a lonely pursuit.

Sure, our data is generally proprietary and therefore we can’t talk much about it. But we certainly can share what we’ve learned about working with that data.

So let’s all commit to sharing our hard won knowledge with each other—and in doing so pave the path for the next generations of analytics practitioners.

Most organizations spend at least some of their time contributing to an open source project. 100% of them, though, depend in some way on the output of open source communities.

If you’ve spoken to me lately, follow me on Twitter, or have taken my order at Wendy’s, you probably know how much I hate traditional dashboards. My dad, a psychotherapist, has been working with me to get to the root of my upbringing that led to this deep-rooted feeling.

💾 This article is for anyone who has ever questioned the sanity of a date not in ISO 8601 format

Have you ever been assigned to add new fields or concepts to an existing set of models and wondered:

• Why are there multiple models named almost the same but slightly different?

• Which model has the fields I need?

• Which model is upstream or downstream from which?