Einblick Analytics: The hard parts of data science made easy

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EMANUEL ZGRAGGEN: My name is Emanuel Zgraggen. I'm the CEO and co-founder of Einblick, and we're an MIT spinoff that started in 2020. And we're building a data science platform that makes it easier for a lot of people to work and use data.

So I started working on this idea of making data science more accessible as part of my PhD, which I did at Brown University. And I'm more of a human computer interaction visualization person for my research. And over time, I realized that I need somebody that can help me a little bit with the database, data management, and machine learning aspect of it.

So fairly early on I started to work with Tim Kraska, who was at the time a young professor at Brown University. And we started working on this project together and went through multiple different prototypes. The research group became bigger and bigger. And then at some point, Tim moved to MIT to become a professor at MIT. And I had followed him as a postdoc there. Then after two years or so, continuing working on the research at MIT, we decided it was a good time to spin it out as a company.

So the problem that we're solving with Einblick Analytics is basically just making data science much more accessible to a broader range of users. So you can imagine, there's a lot of people that are domain experts who care a lot about the data, the domain they're working in, but they're not trained data scientists, but they still want to take advantage of all the modern machine learning and data science methods that are out there to optimize their day-to-day job.

And so we're building Einblick to make it easier for those people to work over their data sets and take advantage of those data science methods. The old way of doing data science was having data science teams work in things like Jupyter Notebooks-- so very code-heavy environments, work on their own, solve the problem, come back a week later, discuss with the business stakeholders, and then go and iterate again, come back another week later.

I think what Einblick allows you to do is iterate much faster over data science problems. So it can act as a common language between business experts, domain experts, and more technical folks like data scientists. And they can use Einblick as the shared medium to work over data problems together.

So the platform basically has three key innovations. One of them is we started sort of with a fundamental redesign of the user interface and how people actually work over data. So the way to think about it is it's like a big interactive whiteboard where you can lay out your data analysis or your data science workflows, you can share it easy with other people, you can collaborate, multiple people can jump in virtually in the same canvas and work together over data.

And the second piece that we innervated on was the underlying data processing piece of it. So we wanted to create this very fluid, very interactive environment where you never have to wait for answers over your data even if the data is really big or the computation is really complex. So to achieve that, we built this processing engine that basically works over sample of your data, gives you back an answer over the sample first, and then as you wait, it will return more and more accurate results as you go. But it enables you to really quickly debug and see what's happening with your data without having to wait even if the data is really big.

Yeah, and so the last area where we innovated on by building this platform was this idea of packaging up common data science tasks into easy-to-use visual operators. So for example, we have this automated machine learning operator that makes it really easy to visually configure and build predictive models even if you're not a data scientist. But it's really unique to have this deep collaboration over data science and I think we're the only platform that offers that

Yeah, so in the next six to 12 months, we are definitely interested in building out our freemium offerings. So Einblick comes in multiple-- you can use it in multiple different ways. But there's actually a free version of it that everybody in the world can just go log on, make an account, and start using right away. So interested in building out that go-to-market function a little bit more, get more adoption there sort of with the freemium aspect of it.

At the same time, still looking to work closer with some more enterprise customers. Get a couple of more teams interested in using Einblick and learn from their use cases. And basically just in this mode of quick iteration, work with customers closely, learn what they're doing, and then let that feedback influence the product again.

Yeah, so in terms of good partners for us, I think it could be data science teams, it could be business teams, it could be innovation teams at large or even smaller companies that are looking to empower some of their workforce to do-- and be more data-driven with the data that they have available.

So since we're fairly domain-agnostic, I think it doesn't really matter what industry you're in, but I feel like most companies have a lot of data and probably not enough capacity to actually analyze that data, and I think Einblick can help unlock the power of that data.

So we always had really close ties with the ILP since day one when we started the company. Some of our early adopters actually came through connections from MIT. So we're super excited to be part of STEX25 and looking to get to know more people in this community and see if there's other companies that are interested in using Einblick.

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