In Silicon Valley, “Big Data” is all the rage these days. For good reason: It’s all around us, and the world we live in today keeps producing more and more of it. Thus, the promise, and the eventual impact, of these new “Big Data-whispering” technologies (that seem to pop up daily) is very real. No industry, public or private is safe. (Hide yo wife, hide yo servers, etc.) However, we also seem to live in a world where startups can slap a “Big Data-vore” tag on their t-shirt, walk into their local VC firm and come out with a multi-million-dollar check.
For anyone who now reflexively rolls their eyes at the sight of this buzzword — and those afflicted by “Big Data Drain” — Ayasdi could just be a breath of fresh air. Relatively speaking, of course. Though it’s a long way from sexy, Ayasdi’s enterprise-focused, machine learning-powered data analysis platform is attracting some of the world’s biggest institutions.
In an era of year-old “Big Data” startups raising big-dollar seed rounds, the Palo Alto-based startup is adding a hefty $30.6 million of its own to the bank, in series B financing round led by Institutional Venture Partners (IVP), with participation from new investors Citi Ventures and G.E. Ventures.
The round also follows on the heels of the $10 million Khosla and Floodgate-led investment Ayasdi raised in January, bringing its total to just under $44 million.
While that would seem to be another case of over-zealous funding in Big Data crazy times, Ayasdi’s new funding is actually somewhat late, arriving well into a decades-long development process. The company was officially founded in 2008, after a dozen years of research at Stanford University, spearheaded by Gurjeet Sing, a Ph.D. mathematics student and Stanford professor Gunnar Carlsson. Years prior, backed by grants from DARPA and NSF, Carlsson and Ayasdi co-founder Harlan Sexton had embarked on a mission to apply “Topological Data Analysis” to real-world problems in an effort to reinvent the methods by which “Big Data” is transformed into actionable knowledge.
Geeky cheerleading aside, today this means that Ayasdi’s platform is attempting to automate the so-called “insight discovery process,” allowing companies and institutions to identify valuable nuggets of intelligence within massive, complex datasets in near realtime. In other words, the founders explain, the process traditionally used by organizations to extract value from Big Data often involved constructing infrastructure ad-hoc, on-site by way of large teams of data scientists, who would spend months or years mining the data for these nuggets of actionable insight.
Not only are there vast permutations of questions that one needs to ask of this data and too few data scientists to do the asking, but once this intelligence is identified, companies then need to deploy the solutions needed to help fix what are often company or infrastructure-wide problems.
As we wrote when last covering Ayasdi:
The problem with this is that queries are inherently based on human assumptions and biases, and, in turn, query results tend to only reveal slices of data, rather than providing visibility into the relationships between similar groups of data. This method of discovering insight in Big Data tends to rely heavily on iterative guesswork and chance, and thus takes time to produce real results …
And while Big Data continues to grow, yet, while governments, businesses and scientists have spent years (and millions of dollars) attempting to address the world’s biggest problems by analyzing Big Data, progress has been incremental. Although Big Data tools have improved over time, Ayasdi is of the mindset that they are still failing to yield the kind of breakthrough insights that lead to true innovation.
As a result, the startup developed its so-called “Insight Discovery Platform” to enable businesses to automate the discovery process, automatically mapping entire structure and unstructured datasets, to help them solve those problems faster. The startup’s unique brand of mathematics (Topological Data Analysis), combined with its machine learning algorithms, also allows institutions to extract value without requiring coding, manual queries or scripts.
While it’s notable that Ayasdi’s approach to making sense of the thorny problems inherent to analyzing massive and complex Big Data sets has led to support from big-name investors, it means little if customers don’t buy into that approach. However, six months from its official launch, the startup has attracted customers that represent some of the largest “Big Data sets” out there, including General Electric, Citigroup, Merck, the U.S. Food and Drug Administration (FDA), the Centers for Disease Control and Prevention (CDC), the University of California San Francisco, Mount Sinai Hospital, Texas A&M, Harvard Medical School and the U.S.D.A.
In concert with its customers and collaborators, Ayasdi is helping these companies to discover new drugs and to improve “cancer therapy by discovering new insights from an 11-year old breast cancer dataset that included new sub-populations of breast cancer survivors, for example. It’s also being used to explore new energy sources, identifying patterns that can lead to more accurate drilling, predict fraud and help prevent terrorist attacks,” as we wrote in January.
“The answers to today’s most important scientific, business and social problems lie in data,” Ayasdi CEO Gurjeet Singh said at the time. “The biggest challenge in Big Data today is asking the right questions of data, so the real opportunity in Big Data lies in the automation of insight discovery — regardless of the complexity of that data — without requiring users to ask questions. The goal is for Ayasdi to provide users with answers to questions that they didn’t know to ask in the first place.”