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Data science beyond data science teams

By Jakub Jurovych

Updated on June 15, 2021

We’re excited to launch Deepnote for Teams to help give anyone in any organization a seat at the data table.

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We started Deepnote two years ago to build the best data notebook. We wanted to develop the solution we desperately wanted to use ourselves — something that would allow us to work in notebooks effectively, in the cloud, with all the bells and whistles we were used to from the software engineering world.

But data science goes beyond data scientists. We’re building not just the best notebook for data science teams. We are building the best notebook for teams working with data.

Today, we’re launching Deepnote for Teams. And going forward, we'll be building even more features that allow anyone in any organization (not just data scientists) to have a seat at the table.

When starting Deepnote, our initial focus was to build a notebook that would support data scientists. We allowed them to work together on the same projects at the same time, added all the integrations we could think of, embedded code intelligence so that data scientists could use Deepnote like they’d use their favorite IDE, and cracked the many challenges around versioning and reproducibility.

We believe we've delivered on that initial vision, but to make data endeavors truly successful, a lot more is needed. Data science is as much a scientific and creative process as an engineering one. It involves failing, learning, and going back to the drawing board. It requires a large amount of communication with other teams and stakeholders.

Data science is bigger than data scientists. It requires:

  • Framing the problem with business stakeholders
  • Finding the right, up-to-date, clean data with data engineers
  • Continuous conversations and iterations with product owners
  • Analyzing the prototypes, collecting feedback, and iterating on models among the data science team
  • Sharing the findings as dashboards and reports with customers, suppliers, or investors
  • Deploying the models to production with the help of the engineering and DevOps teams

Our next horizon is building a product that is not only powerful for data scientists, but also inclusive and accessible enough for everyone else in the organization.

Reimagining the data science process

Before we dive deeper, let’s take a step back and look at the market trends that are underpinning this shift.

Trend #1: The rise of the citizen data scientist

Using browser-first collaborative tools is becoming the new norm. Google Docs, Notion, and Figma paved the way. And much like the design process changed with the arrival of Figma, the data science process is going through the same fundamental transition.

Traditionally, data scientists used to work in isolation, only sharing the outcomes of their work once the process was completed. With the increasing availability of data, there’s a lot more curiosity about how to use it from all across the organization.

In the organizations of tomorrow, data scientists work directly with developers, product managers, and marketers around the same analysis, on the same data set, with different access rights. This leads to shorter iteration cycles, faster releases, better outcomes, and more engaged teams. Even better, the data scientists become developers, product managers, and marketers themselves.

To support this transition and empower citizen data scientists, we need tools that allow anyone to access data without barriers. But accessibility without understanding is not useful. We also need tools that help anyone in the organization analyze the data and apply insights autonomously.

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Trend #2: Competing in a data-driven world

The second trend we’re seeing is organizations realizing how data drives value and scaling their data capabilities to leverage that. The gap between industry leaders and laggards widens as organizations’ use of data and analytics keeps growing, and getting ahead of the curve is the core focus in many data-driven organizations.

As one of our customers, an engineering lead at a life sciences company, said, “I believe growing data science capabilities across the team today will give us a competitive edge later. I want all of our scientists to adopt these skills, so I like bringing everyone directly into notebooks.”

It's the same in retail, insurance, consulting, manufacturing, and other legacy industries. Companies with the greatest overall growth in revenue and earnings receive a significant proportion of that boost from data and analytics. Building data science capabilities is what gets you the competitive edge.

Trend #3: Remote-first workforce

With COVID-19, our work lives are moving remote. A McKinsey survey found that 90% of executives envision a future that’s either remote-first or hybrid, with some combination of remote and on-site work. This model of collaboration brings new challenges and requires new tools to step up and help. We need interfaces that allow us to collaborate effectively in any setting.

So how does Deepnote fit in?

At Deepnote, we believe notebooks are the tool that can respond to these trends and help companies make a shift from data scientists working in isolation to a data science process that is decentralized and inclusive for all in the organization.

1. Notebooks paradigm

First of all, Deepnote is a notebook. Notebooks were designed for exploratory programming and rapid prototyping. They were designed for answering questions. We love the notebook paradigm because we think it’s really useful for a productive conversation around data.

Teams see the data source and the logic in one place, they can rapidly explore and visualize data, and iterate on their findings. Naturally, this makes notebooks a great entry point for non-technical collaborators, who can easily understand the context. With Deepnote, they can contribute to the conversation using features like comments, no-code visualizations, and input cells where they can change variables without code.

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2. Browser-first

Deepnote is browser-first, allowing everyone to spin up a notebook in a couple of seconds with no hardware provisioning, no installation, and no engineering support.

"We'd had a lot of technical issues when trying to pair up on other notebooks during remote interviews," said Becca Carter, Product Analytics Lead at Gusto. "Deepnote was incredibly easy to set up and allows us to start new notebooks in seconds."

Having a browser-first notebook also means that everyone interacts in the same environment, with the same version of the notebook. Which brings us to….

3. Collaboration-native

Deepnote brings teams together and allows them to work together, either in real time or asynchronously. Luca Naef, the CTO of VantAI who uses Deepnote for real-time code reviews with his team, summarized this perfectly:

"Working in Deepnote is like code review and rapid prototyping at the same time, saving valuable time in the iteration cycles. But as opposed to code review via GitHub, you have direct access to the runtime and program state, which makes understanding complex models much easier and leads to much more spontaneous creative ideas."

Where are we going?

Ultimately, our vision for Deepnote is to help teams explore, analyze, and present data from start to finish. No more silos — we want to make notebooks the focal point for any data team.

In the coming weeks, we'll be rolling out new features that will help us deliver on this vision: no-code visualizations, interactive apps and dashboards, and publishing.

If you want to try out what a collaborative data notebook feels like, we’ve just launched a free plan that allows you to test the powers of Deepnote.

And if you’re excited about our mission, we’re hiring!

Jakub Jurovych

CEO @ Deepnote

Follow Jakub on LinkedIn

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How Deepnote uses Deepnote

By Megan Lieu

Updated on December 27, 2023

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