Successful CMOs achieve growth by leveraging technology. Join us for GrowthBeat Summit on June 1-2 in Boston
, where we'll discuss how to merge creativity with technology to drive growth. Space is limited. Request your personal invitation here
DataRPM launched two years ago to bring a simple, Google search-like interface to business intelligence software. Since then, startups like Birst, ThoughtSpot, and Upshot have since come forth with similar designs, indicating that there’s a real demand for this kind of technology.
Now investors are showing their support for DataRPM’s approach, too. Today the startup announced a $5.1 million round of funding, which will enable it to focus on growing its user base.
But the startup won’t stand in place. Cofounder and chief executive Sundeep Sanghavi explained told VentureBeat that, going forward, DataRPM will push a considerably more expansive feature set than what it launched last year.
To date, it has functioned as a business intelligence (BI) tool you aim at an existing storehouse of data and query with free-form “search” queries instead of statements in the traditional SQL query language.
Now, in addition to running on top of a data warehouse from a vendor like Teradata or the open-source Hadoop file system for storing huge piles of unstructured data, DataRPM will also offer to store and “index” lots of different kinds of data from multiple sources — effectively becoming an alternative to a data warehouse.
It’s a bold initiative, but the advantages Sanghavi described do make the idea sound compelling.
It takes too much time to get data into data warehouse hardware like Teradata or IBM Netezza, and it takes too much time and effort to get data out of Hadoop, Sanghavi said. With DataRPM, which is available as cloud-based software or in an on-premises package, it takes less time to start asking ad-hoc questions. Partly, that’s because now the software takes a look at all the data users load in and takes guesses about possible relationships. After that, the software asks users to verify which of those relationships are correct. In effect, the method lets DataRPM take shortcuts when it indexes the data and gets it ready for actual BI queries.
Sanghavi knows it’s bold to talk about replacing big systems for storing data, so he prefers a more all-encompassing pitch.
“You can’t come out on the market and saying you’re replacing data warehouses and the Hadoop cluster,” he said. “What we’re saying is, ‘Hey, we’re agnostic. We don’t care if data resides in Hadoop clusters, a data warehouse, a log file, a .CSV dump, an RDBMS (relational database management system). We’ll take it from there, we’ll index it, and we’ll give you natural-language analytics, without having you wait six or nine or 12 or 18 months.'”
Based in Fairfax, Va., the startup has around 30 employees, with 13 paying customers and another 17 pilots under way, Sanghavi said.
It competes with traditional BI tools, like SAP’s Business Objects, as well as visualization-focused vendors like Tableau and QlikView and startups like ClearStory Data and Looker.
DataRPM has raised $5.9 million since opening in 2012. InterWest Partners led this latest funding round, and CIT GAP Funds also participated.
DataRPM is an award-winning, industry pioneer in smart machine analytics for big data. DataRPM enables Automatic Data Modeling from disparate data sources using cognitive algorithms, eliminating the need to manually build complex data ... read more »
Powered by VBProfiles
VentureBeat’s VB Insight team is studying marketing analytics...
Chime in here, and we’ll share the results