Steve Sliwa previously founded Insitu, the maker of unmanned aircraft that sold to Boeing for a reported purchase price of $400 million five years ago. Now, Sliwa is back in the startup game with Seeq, a big data startup that’s announcing $6 million in series A funding today from Madrona Venture Group, Clear Fir Partners and Second Avenue Partners, one of the original investors in Insitu.
We first discovered Seeq back in May, but at the time Sliwa wasn’t saying much about the startup. Now, he’s shedding a bit more light on the company, which employs a dozen people spread across North America.
Seeq, a play on the word “seek,” allows manufacturers to more effectively mine and gain insights from industrial process data. Sliwa came up with the idea after he was asked to consult on a business, discovering that some of the tools being used to collect and analyze the data were old.
“The methods were old, but the data was incredibly valuable,” said Sliwa. “Industry is spending about $2 billion a year collecting and managing this data. And many of them have lots of information, but they are not able to exploit and make the decisions that they want. It just occurred to me that there was a lot of money being spent, and it was time for a refresh, a 2.0 in this area.”
Much of the data being mined is time-sensitive data from sensors and instruments, with much of that data now being stored in disparate databases across the enterprise. For example, Sliwa said an oil & gas refinery may have as many as 150,000 sensors tracking data, everything from temperature controls to flows. When engineers ask questions of that data, Sliwa said it can take weeks or months for them to retrieve answers.
In some ways, that’s a similar idea to Tableau Software, whose founding mission is to help organizations make better sense out of data.
I asked Sliwa what makes Seeq unique, and he said Tableau can’t really be used with this sort of industrial process data. However, he said once it is processed by Seeq, it could be loaded into Tableau so users could easily search and analyze it. He said a company like Tableau could be a good partner in the future.
“Most big data is transaction based, so it is like a time stamp. In ours, you’ve got signals where every data point next to each other is important, as well as every data point across each other, and it is all differential equations, so you can’t just collect it and store it the normal way. You have to have special ways to get it and process it. And, if you just dump that data into a tool like Tableau, you can’t get much information out of it, because all of it is is a string of numbers — you don’t understand what are the units; what was the start and stop of a process; what was the time that fuel was being added. You just have numbers. So, until you start adding that context to the numbers, you can’t start plotting things against each other.”
Sliwa said the biggest player in the field right now is OSIsoft, maker of an enterprise historian software that collects sensor data. Seeq plans to sit on top of those systems, making that data more accessible throughout the organization. It also plans to help organizations make that sensor data relatable to other data within the organization, including business rules.
“We bring that data together, and make it available for the customers to be able to do analyses,” he said.
Seeq started in May, and it’s operating as a “lean startup” with conversations going on with potential customers to gain feedback and help build the product. Pricing has not yet been set for the product.
As a result of the latest funding, Pete Higgins of Second Avenue Partners has joined the board as chairman.
Interestingly, Seeq has no centralized offices, though the mailing address is Second Avenue Partners in downtown Seattle. Sliwa splits his time between Phoenix and the Columbia River gorge area, while other team members live in California, Colorado and Canada.
“One nice thing about being virtual is that you are less about politics, and more about milestones, so you are more productive getting things done,” he said. “What I’ve been able to observe is that the team I am able to recruit is like 80 percent better since I am not constrained to a specific location.”