Collaboration with Data Recipes

I’ve always felt the push and pull of balancing synchronous and asynchronous communication channels for analytics work. On one hand, it’s essential to make sure everyone is on the same page with a shared understanding of the problem and the data, for which meetings and collaborative sessions are key. On the other hand, time for focused deep work is precious, so limiting meetings is also critical. Meeting fatigue can kill productivity.

The good news is that DataChat is well-positioned to support finding the right balance when working with others. In particular, our Collaborate skill allows multiple users to co-create on the same analytics problem in real time by editing the data, charts, and models in a shared session.

For asynchronous teamwork, we also facilitate co-creation without requiring extensive meeting time. Every data product in DataChat, from charts to machine learning models, is backed by a data recipe, which provides a complete data history. We call these recipes “workflows”, and they can be shared and edited, allowing team members to review and refine each other’s work.

DataChat workflows provide unrivaled understandability, transparency, and collaboration by exposing every step of the analytics work. Often, I can just send a workflow to a colleague or client to explain my ideas on how to approach a complicated topic. We can then go back and forth on edits to make sure it actually solves the problem. Similar to collaboration on Google Docs, now you can collaborate and co-create data recipes in DataChat. In fact, we think of recipes very much like documents when we think of collaboration.