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A New Company Uses Big Data To Fight Cancer (And Rethink Basketball)

Ayasdi, a new big-data firm with close ties to Stanford University and DARPA has plans to change the way researchers analyze cancer, money laundering, and professional sports.

Creating visualizations of huge data sets is big business. Companies like Palantir have been turning big-data visualization into huge profits for quite some time. One new entrant into the field, Palo Alto-based Ayasdi, just launched publicly on January 16. Ayasdi, which is a spin-off of a DARPA-funded Stanford research project and maintains close ties to the Stanford math department, uses a mathematical technique called topological data analysis to find unexpected insights for the pharmaceutical industry, the energy industry, and others. The company’s Iris platform creates visualizations of entire massive data sets, rather than smaller queries and slices.

In promotional materials, Ayasdi describes their product as a cloud-based machine learning platform that uses visualizations of massive data sets to discover unexpected patterns and connections. The company has been working in stealth mode for the past year but popped up at the MIT Sloan Sports Analytics Conference, where Ayasdi analyst Muthu Algappan argued that there are really 13 positions in basketball, not five. Instead of centers and point guards, Ayasdi’s software instead claims that coaches are better off looking at their players as "shooting ball handlers" and "scoring rebounders."

Ayasdi cofounder Gunnar Carlsson, a mathematician at Stanford University, worked on a DARPA- and National Science Foundation (NSF)-funded topological data analysis initiative aimed at improving the quality of visualizations of massive data sets. "It has become clear that topological data analysis has real-world applications—it finds insights more quickly," Carlsson told Co.Exist in an interview.

During the interview, Carlsson and CEO Gurjeet Singh mentioned that Ayasdi was already in use in numerous industries, including the energy industry, pharmaceuticals, health care, and defense. The company, however, was only comfortable discussing details of their platform’s use in pharmaceuticals and health care. Using 11 years of data mined from the Netherlands Cancer Institute-Antoni van Leeuwenhoek Hospital, Ayasdi was able to identify new, previously undiscovered populations of breast cancer survivors. Using connections and visualizations generated from the breast cancer study, oncologists can map their own patients’ data onto the existing data set to custom-tailor triage plans. In a separate study, Ayasdi helped discover previously unknown biomarkers for leukemia.

Much like Palantir, Ayasdi is also being used for counter-terrorism projects. One classified project the company’s platform is being used for involves the mining of large-scale structured and unstructured text data to find unexpected relationships; another project for a financial services client involves detecting previously unknown patterns of money laundering.

According to Singh, the company licenses its software to private parties, who are then responsible for data input. The company claims that users with no mathematical background can be trained in their software within five hours.

Sun Microsystems cofounder and venture capitalist Vinod Khosla’s Khosla Ventures, along with Floodgate, is backing Ayasdi with an initial $10 million investment. "Ayasdi’s Insight Discovery platform utilizes machine-powered intelligence to unearth important—and previously unattainable—answers that will help solve some of the most pressing global, social, and economic issues," Khosla said in a statement.

One of the biggest challenges, however, facing Ayasdi is that it is not the only data visualization firm out there. Big data firms—not even those with Stanford and DARPA ties—don’t exist in bubbles; even if the company is having good luck with its proprietary technology, visualization is big business. While Ayasdi might be concentrating on health care and pharmaceuticals, competitors lurk in other fields.

[Images: Ayasdi]

Correction: An earlier version of this article misidentified the mathematical technique used by Ayasdi. It is topological data analysis, not topographical data analysis.

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