With about 40 customers 18 months ago and over 250 now, cloud business-intelligence startup Looker says a $30m funding injection unveiled today will help finance the next phase of expansion.
The series B round, led by Meritech Capital Partners, with Sapphire Ventures, and existing investors Redpoint Ventures, First Round Capital, and PivotNorth, brings the total raised by the Santa Cruz, California-based company to $48m since launch in March 2013.
The web-based Looker platform, mainly deployed in the cloud, uses proprietary SQL-based modelling language LookML and allows data analysts to give business users the ability to explore large data stores themselves instead of depending on standard reports.
Looker CEO Frank Bien said, as a Silicon Valley tech company, some of the new investment will inevitably go into expanding its engineering operations but Looker's commercial activities will be the main beneficiary.
"It's really about growth at this phase. What this is all about is expansion. So we're opening an office in London. We've opened an office in New York. We have the existing location in the Bay Area," Bien said.
"It's typical for a SaaS company. We're entering that growth phase where you start to add more sales and marketing resources to drive into new markets and that's where the majority of the money goes. Now clearly engineering and the development on the product side scales with that."
Bien, who founded the company with former Netscape technologist Lloyd Tabb, has worked at EMC, Greenplum, and Virsto - now owned by Pivotal and VMware, respectively - and Vignette.
"Where the old environments were all about extracting data out of back-end systems, what we're about is providing a lens on top of very large and complex datasets - the data lake if you want to call it that," he said.
"What we do, which is a bit contrarian, is really focus the sales effort on data people. Data analysts have been greatly underserved. They've been given tools that aren't quite appropriate for what they'd like to accomplish and which are more end user-oriented, with this push to visualisation and self-service."
According to Bien, Looker's focus on data analysts is designed to provide them with tools to explain and curate large data environments so business users can explore them.
"Most business intelligence is about, 'Hey, data guy give me this set of data so that I can get an answer'. What Looker is doing is giving those data people tools they want to use - code-based tools, GitHub-integrated, all that kind of stuff - so that they can curate experiences for the business users, much like the early web developers and web masters were doing," he said.
"They were providing these experiences where people could click and link and find information on their own. [It's] not just one or two people having access to the data, not where just the CEO suite has access but where the people working in many different departments can all collaborate and work with it and make decisions based on it."
Bien said Looker has three primary components. The first is an environment where data people can explain and curate data using the LookML data modelling language.
"That's a very key element. It's the ability for data people to explain and curate very quickly. Where that's very unlike traditional tools is our world is built around this notion of transformation and query. It's this notion that people have created messy data stores and they want to allow people to explore them," he said.
Looker's second element is a complementary end-user browser-based environment, where ordinary employees can freely explore the data without using code or LookML.
"They can be looking at things by geography and then they decide they want to look at things by inventory item and then by web behaviour. They can really be bouncing around and looking at data from many different angles. That gets rid of this queue of requests - the data bread line of people constantly asking for data," he said.
The third part of Looker is its architecture, which is designed to allow it to operate entirely in the database.
"Most business intelligence was extracting data out of applications using heavy ETL tools to move it in a very manicured way into a data warehouse in a star schema, and then put business intelligence tools on top of that that extract little bits of it out so that people could operate on very small silos," Bien said.
"What Looker is doing is empowering these very large datasets that have been created already. People have deployed large Amazon Redshift clusters or Vertica, or really fast MySQL instances even, and what they want to do is they want to provide a lens on top of those big-data stores. What we're doing is completely building on that infrastructure, where others were relying on this whole chain of ancient tools."