<aside> 💙 TL;DR — You can now bring analytics into Notion using Deepnote embeds. Deepnote is a collaborative data science notebook that lets you explore, analyze and present your data. If you want to try the new embedding experience yourself, sign up for free here.
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At Deepnote, we love and live in Notion. From keeping track of our user interviews, managing our hiring pipeline, all the way to shaping new features - Notion allows us to stay in sync. As a hybrid team distributed across North America and Europe, we have a strong writing culture. Everything said gets captured in bullet points, pages, database inputs, or tickets on a board. One thing that was missing was an analytical layer - something that would allow us to complement our Notion stories and ideas with numbers and data visualizations.
Analytics is something that Notion doesn't offer out of the box. This is where Deepnote comes in. In case you're new to Deepnote - hi 👋, we're building a collaborative data science notebook that brings teams together to explore, analyze and present data from start to finish. Being the data nerds we are, we jumped at the opportunity to bring some of Deepnote's analytical power right into Notion's interface. In this quick demo video, you can see it in action.
CleanShot 2021-10-22 at 15.22.36.mp4
You can use Deepnote embeds to bring in data frames, code, data visualization, even interactive graphs - such as the Plotly graph below. This one captures current Covid-19 vaccination data in Norway. The block is live and powered by a scheduled Deepnote notebook - meaning the data is automatically updated from official government sources every day.
https://embed.deepnote.com/2ea01907-39ec-474d-883d-e28930fafc09/3d981233-ade8-4a94-b8f3-5ef117e0bdc3/00004-92fd9206-ad31-4276-84fa-4da972528a44?height=601
Product analytics is at the heart of our business at Deepnote. We're on a mission to build software people love to use. User feedback tells us a part of that story - the other part is told by metrics. As it happens, we track these metrics - such as retention, engagement, feature adoption, and revenue - and bring them into Deepnote to create an aggregate view of our historical performance, as well as future model performance (more on that on a different day).
Bringing Deepnote blocks into Notion allows us to lay down the narrative in native Notion blocks, and complete the story with numbers. This way, we get to keep the context all in one place.
Another powerful addition to our toolbox is the ability to tap into Notion databases we create and analyze them in notebooks. Using the Notion API and Deepnote notebooks, we can easily read our Notion databases and convert them to Pandas DataFrames, which are easy to visualize. With all this, we have data and metrics as first-class citizens in Notion!
Some more possibilities this unlocks: