Startup analytics vendor Einblick emerges from stealth

Startup analytics vendor Einblick emerged from stealth on Wednesday after securing $6 million in seed funding.

Einblick, a German word that means insight when 'ein' and 'blick' are combined and one view when the words are separated, was founded in Cambridge, Mass., in 2020 after starting as a research project.

Amplify Partners led Einblick's funding round with participation from Flybridge and Samsung Next, and Sunil Dhaliwal, general partner at Amplify Partners, joined the startup's board of directors.

Einblick offers what it terms a visual data computing platform, not to be confused with a data visualization platform. A visual data computing platform enables users to see their data throughout the analytics process and is designed to enable users to both understand the past and predict the future while seeing their data in the present in order to make data-driven decisions in the moment.

In the past, BI platforms looked at the past so that users could understand what had already happened. Then they advanced to become predictive with models fueled by augmented intelligence and machine learning to foresee what will happen. Einblick is attempting to combine the two while also adding the ability to see data throughout the analytics process rather than have it cleansed and prepared in another platform.

The startup has attracted about five customers to date -- all large companies, according to founder and CEO Tim Kraska, a professor at MIT. In concert with emerging from stealth, the vendor is launching a founding customer program to help Einblick refine its analytics platform over the next 6 to 12 months.

"Given that we have to focus on decision-making, it's not enough to do predictive modeling, and it's not enough to have a single visualization," Kraska said. "You need something that combines these in a coherent way and then adds what-if analysis on top. Visual data computing is a new paradigm that gives you immediate feedback but also allows you to create complex data-flow pipelines."

It's that immediate feedback throughout the analytics process that has the potential to differentiate the Einblick analytics platform from those of other startups, as well as from those of established vendors, according to Dave Menninger, research director of data and analytics research at Ventana Research.

"What's most interesting is the way Einblick has integrated AI and ML [machine learning] with BI," he said. "Most BI tools either do very little AI/ML or require models to be developed externally and imported into the BI platform. Einblick makes the entire end-to-end process available in their visual computing platform. Not only do they bring AI and BI together, but they incorporate optimization, which is rarely part of either."

What's most interesting is the way Einblick has integrated AI and ML with BI. Most BI tools either do very little AI/ML or require models to be developed externally and imported into the BI platform.

Menninger added, however, that as with many startups offering something new, Einblick will face the challenge of getting organizations to give up their existing analytics tools -- for example, Tableau and Qlik for BI and DataRobot for modeling -- for a platform that combines the two capabilities.

If enterprises are able to seamlessly combine two tools to accomplish most of their goals, they may not be inclined to try something new, even if it may be more efficient and provides the ability to view data and gain insight at every step of the way.

"While the breadth of the platform is very promising, it will be interesting to see if organizations are willing to give up their existing visualization platforms to gain these additional capabilities," Menninger said.

As Einblick emerges from stealth, its analytics platform is geared toward both business analysts and data scientists.

It requires no coding, enabling business analysts to create and run data models that otherwise could only be done by data scientists. Meanwhile, the platform simplifies the time-consuming data preparation work often required of data scientists while also enabling them to add their own code and do more in-depth analysis.

"The default is very easy to use, but then you can dive deep and use Python if you need to modify any piece of it," Kraska said.

The response from customers, he added, has been positive.

"Most are immediately drawn in by the collaborative and visual features," Kraska said, "and then they discover that there is a whole bunch of power."