Today, we're announcing the newly-designed Mode developer site. The new site contains extensive documentation of commonly used API endpoints, plus a few new ones. We've also added an API cookbook give you templates for common tasks. These new resources make it easier than ever before to build with Mode.

As analysts and data scientists, we have to make our work compelling and persuasive to change the way our organizations think. It can be easy to assume that our well-constructed graphs and tables are plenty convincing on their own. But this ignores a fundamental fact of analytics: it's still a human making the decision behind those numbers, and that person is rarely as quantitatively inclined as we are. Persuading this audience takes a skill that isn't discussed enough in the data science community: empathy.

A hands-off approach would seem reckless for questions about things like security. And yet that approach is not just the norm for analytical questions in most organizations; it's often the ideal. Many analytics teams aspire to enable as much “self-serve” as possible - in other words, to remove themselves from as many decision-making processes as they can.

There's a fundamental change taking place in the way that organizations make decisions. For analysts and data scientists, the days of being forced to use conventional, monolithic data tools are ending. The individuals who answer the hardest questions within an organization can use the tools they choose. These tools let them investigate a problem, solve it, and move on.

Regardless of how agile your team might be, you're limited by how quickly you can provision a warehouse, build ETL pipelines, set up event tracking, and integrate an analytics tool. Doesn't this all take significant time and expertise to build?

One of the most common reasons an organization reaches out to Mode is that they're about to begin investing in analytics, and they want our advice. Sometimes these organizations are only a few months old, sometimes they've been around for decades. No matter how old they are, they all want to transition from being a company that has data to a company that truly uses their data.