Analytics startups are starting to attract the attention of venture capitalists, potentially hastening the consumerization of Big Data.
ClearStory, a Palo Alto, Calif.-based company that seeks to bring average business users into the Big Data analytics fold, announced today that it had secured $9 million in a Series A round of financing. Kleiner Perkins Caufield & Byers led the round, with backing from previous investors Andreessen Horowitz and Google Ventures.
The news comes just two weeks after New York City-based startup SumAll, which specializes in Web-based data visualization tools for small businesses, announced it had raised $6 million. SumAll will use the money to strengthen its cloud-based data analytics platform.
Big Data for All
Sharmila Shahani-Mulligan, ClearStory's founder and CEO, says her company's analytics platform cuts through the alphabet soup of the Big Data technology ecosystem and eliminates many of the biggest barriers that prevent businesses from drawing actionable insights from mountains of data.
While the industry has been working to make Big Data more accessible, it has perpetuated some of corporate IT's bad habits, Shahani-Mulligan says.
In a company blog post, she writes, "Instead, managers and analysts are presented with complex interfaces to data platforms, a Babel of third-party APIs, high-priced analytic frameworks and silos of point products that each require a small army of tech-savvy data specialists, all of them deeply entrenched in their individual worlds."
Even seemingly easy tasks turn into time-consuming feats. "Simply retrieving a sliver of data from a single database or web site can be a chore; harder still is combining data from multiple internal and external sources," adds Shahani-Mulligan.
ClearStory's goal is summarized in its tag line: "Bringing clarity to big data chaos."
The startup asserts that its platform does more than remix analytics data and present it in a visually appealing way. "We make finding, querying and interacting with data — even when it requires numerous operations across multiple internal and external data sets — as easy as getting directions from your mobile driving app," writes Shahani-Mulligan.