Personal blog of Yzmir Ramirez

Without good data, it’s impossible to build an accurate predictive machine learning model. Explorium, a company that has been building a solution over the last several years to help data pros find the best data for a given model, announced a $75 million Series C today — just 10 months after announcing a $31 million Series B.

Insight Partners led today’s investment with participation from existing investors Zeev Ventures, Emerge, F2 Venture Capital, 01 Advisors and Dynamic Loop Capital. The company reports it has now raised a total of $127 million. George Mathew, managing partner at Insight, and former president and COO at Alteryx, will be joining the board, giving the company someone with solid operator experience to help guide them into the next phase.

Company co-founder and CEO Maor Shlomo, says that in spite of how horrible COVID has been from a human perspective, it has been a business accelerator for his company and he saw revenue quadruple last year (although he didn’t share specific numbers beyond that). “It’s related to the nature of our business. We’re helping enterprises and data practitioners find new data sources that can help them solve business challenges,” Sholmo explained.

He says that during the pandemic, a lot of companies had to find new data sources because the old data wasn’t especially helpful for predictive models. That meant that customers required new sources to give them visibility into the shifts and movements in the market to help them adjust and make decisions during pandemic. “And given that’s basically what our platform does in its essence, we’ve seen a lot of growth [over the past year],” he says.

With the revenue growth the company has been experiencing, it has been adding employees at rapid clip. When we spoke to Explorium last July, the company had 87 people. Today that number has grown to 130 with plans to get to 200 perhaps by the end of 2021 or early 2022, depending on how the business continues to grow.

The company has offices in Tel Aviv and San Mateo, California with plans to open a new office in New York City whenever it’s possible to do so. While Shlomo wants a flexible workplace, he’s not going fully remote with plans to allow people to work two days from home and three in the office as local rules allow.

Back when I was a wee lad with a very security-compromised MySQL installation, I used to answer every web request with multiple “SELECT *” database requests — give me all the data and I’ll figure out what to do with it myself.

Today in a modern, data-intensive org, “SELECT *” will kill you. With petabytes of information, tens of thousands of tables (on the small side!), and millions and perhaps billions of calls flung at the database server, data science teams can no longer just ask for all the data and start working with it immediately.

Big data has led to the rise of data warehouses and data lakes (and apparently data lake houses), infrastructure to make accessing data more robust and easy. There is still a cataloguing and discovery problem though — just because you have all of your data in one place doesn’t mean a data scientist knows what the data represents, who owns it, or what that data might affect in the myriad of web and corporate reporting apps built on top of it.

That’s where Select Star comes in. The startup, which was founded about a year ago in March 2020, is designed to automatically build out metadata within the context of a data warehouse. From there, it offers a full-text search that allows users to quickly find data as well as “heat map” signals in its search results which can quickly pinpoint which columns of a dataset are most used by applications within a company and have the most queries that reference them.

The product is SaaS, and it is designed to allow for quick onboarding by connecting to a customer’s data warehouse or business intelligence (BI) tool.

Select Star’s interface allows data scientists to understand what data they are looking at. Photo via Select Star.

Shinji Kim, the sole founder and CEO, explained that the tool is a solution to a problem she has seen directly in corporate data science teams. She formerly founded Concord Systems, a real-time data processing startup that was acquired by Akamai in 2016. “The part that I noticed is that we now have all the data and we have the ability to compute, but now the next challenge is to know what the data is and how to use it,” she explained.

She said that “tribal knowledge is starting to become more wasteful [in] time and pain in growing companies” and pointed out that large companies like Facebook, Airbnb, Uber, Lyft, Spotify and others have built out their own homebrewed data discovery tools. Her mission for Select Star is to allow any corporation to quickly tap into an easy-to-use platform to solve this problem.

The company raised a $2.5 million seed round led by Bowery Capital with participation from Background Capital and a number of prominent angels including Spencer Kimball, Scott Belsky, Nick Caldwell, Michael Li, Ryan Denehy and TLC Collective.

Data discovery tools have been around in some form for years, with popular companies like Alation having raised tens of millions of VC dollars over the years. Kim sees an opportunity to compete by offering a better onboarding experience and also automating large parts of the workflow that remain manual for many alternative data discovery tools. With many of these tools, “they don’t do the work of connecting and building the relationship,” between data she said, adding that “documentation is still important, but being able to automatically generate [metadata] allows data teams to get value right away.”

Select Star’s team, with CEO and founder Shinji Kim in top row, middle. Photo via Select Star.

In addition to just understanding data, Select Star can help data engineers begin to figure out how to change their databases without leading to cascading errors. The platform can identify how columns are used and how a change to one may affect other applications or even other datasets.

Select Star is coming out of private beta today. The company’s team currently has seven people, and Kim says they are focused on growing the team and making it even easier to onboard users by the end of the year.

In a world with growing amounts of data, finding the right set for a particular machine learning model can be a challenge. Explorium has created a platform to make that an easier task, and today the startup announced a $31 million Series B.

The round was led by Zeev Ventures, with help from Dynamic Loop, Emerge, 01 Advisors and F2 Capital. Today’s investment brings the total raised to $50 million, according to the company.

CEO and co-founder Maor Shlomo says the company’s platform is designed to help people find the right data for their model. “The next frontier in analytics will not be about how you fine tune or improve a certain algorithm, it will be how do you find the right data to fit into those algorithms to make them as useful and impactful as possible,” he said.

He says that companies need this more than ever during the pandemic because this can help customers find more relevant data at a time when their historical data might not be useful to help build predictive models. For instance, if you’re a retailer, your historical shopping data won’t be relevant if you are in an area where you can no longer open your store, he says.

“There are so many environmental factors that are now influencing every business problem that organizations are trying to solve that Explorium is becoming this […] layer where you search for data to solve your business problems to fuel your predictive models,” he said.

When the pandemic hit in March, he worried about how it would affect his company, and he put a hold on hiring, but as he saw business increasing in April and May, he decided to accelerate again. The company currently has 87 employees between offices in Israel and the United States and he plans to be at 100 in the next couple of months.

When it comes to hiring, he says he doesn’t try to have hard and fast hiring rules like you have a certain degree or have gone to a certain school. “The only thing that’s important is getting good people hungry to succeed. The more diverse the culture is, the more diverse the group is, we find the more fun it is for people to discover each other and to discover different cultures,” Shlomo explained.

In terms of fundraising, while the company needs money to fuel its growth, at the same time it still had plenty of money in the bank from last year’s round. “We got into the pandemic and we didn’t know how long it’s going to last, and [early on] we didn’t yet know how it would impact the business. Existing investors were always bullish about the company. We decided to just go with that,” he said.

The company was founded in 2017 and previously raised a $19.1 million Series A round last year.

Explorium, a data discovery platform for machine learning models, received a couple of unannounced funding rounds over the last year — a $3.6 million seed round last September and a $15.5 million Series A round in March. Today, it made both of these rounds public.

The seed round was led by Emerge with participation of F2 Capital. The Series A was led by Zeev Ventures with participation from the seed investors. The total raised is $19.1 million.

The company founders, who have a data science background, found that it was problematic to find the right data to build a machine learning model. Like most good startup founders confronted with a problem, they decided to solve it themselves by building a data discovery platform for data scientists.

CEO and co-founder, Maor Shlomo says that the company wanted to focus on the quality of the data because not much work has been done there. “A lot of work has been invested on the algorithmic part of machine learning, but the algorithms themselves have very much become commodities. The challenge now is really finding the right data to feed into those algorithms,” Sholmo told TechCrunch.

It’s a hard problem to solve, so they built a kind of search engine that can go out and find the best data wherever it happens to live, whether it’s internally or in an open data set, public data or premium databases. The company has partnered with thousands of data sources, according to Schlomo, to help data scientist customers find the best data for their particular model.

“We developed a new type of search engine that’s capable of looking at the customers data, connecting and enriching it with literally thousands of data sources, while automatically selecting what are the best pieces of data, and what are the best variables or features, which could actually generate the best performing machine learning model,” he explained.

Shlomo sees a big role for partnerships, whether that involves data sources or consulting firms, who can help push Explorium into more companies.

Explorium has 63 employees spread across offices in Tel Aviv, Kiev and San Francisco. It’s still early days, but Sholmo reports “tens of customers.” As more customers try to bring data science to their companies, especially with a shortage of data scientists, having a tool like Explorium could help fill that gap.