When we talk to analytics teams about what they're working on and what big projects are coming up, this is a common refrain we hear:

“Right now, we're working on getting data out to people and making sure everyone has the right dashboards. We also want to answer some of the bigger questions that are causing problems for our business, but it's important that people have what they need first.”

In case you missed it, we launched a free product earlier this year, Mode Studio.

Free products aren't common in the world of analytics. Even less common, we launched ours after already having built a strong business around a paid product. Quite often in the last six months, we've been asked why we did it.

Anytime you look for patterns in previous experiences, or make a tradeoff between one option and another, you're analyzing something. This means nearly everyone in the workforce does some sort of analysis every day, every time they make a decision.

The need for hard data to help make these decisions in the workplace gave birth to the idea of an “Analyst” as we know it.

A few weeks ago, Google announced a new feature, Dataset Search, that aims to make it easier for anyone to discover public datasets. Journalists, analysts, and data nerds will likely find Dataset Search useful: it uses metadata to help people discover different versions of a dataset and publications that reference it.

The internet is awash in Venn diagrams of SQL JOINs and explanations of why they're necessary. Though it would be nice if we could answer every data question with a single table, more often than not, to get a complete picture, we need to combine different data sets.

As analysts, we know that each question we ask of our data can be answered with multiple potential languages and toolkits. Each language has its strengths and we've often pondered the distinctions. We've previously examined Group By, window functions and a general framework for thinking in SQL and Python.