Cognitive Scale Applies “Cognitive Learning” to Big Data Problems

Startup: Cognitive Scale

What they do: Provide a “cognitive computing cloud platform” that accelerates the value of Big Data.

Headquarters: Austin, TX

CEO: Akshay Sabhikhi. He was previously the global leader for IBM Smarter Care, where he led IBM’s entry into a new market at the intersection of social and health. Prior to this role at IBM, he led IBM’s Smarter Commerce initiative.

Founded: 2013

Funding: They’ve received an undisclosed amount of funding through the Entrepreneurs’ Fund.

Problem they tackle: According to Cognitive Scale, the three biggest reasons that as many as 55 percent of Big Data projects fail are because: first, 70-80 percent of the typical company’s data is trapped in silos within and outside company’s walls without a secure and reliable way to access it.

Second, more than 80 percent of data today is unstructured (call center notes, geolocation data, mobile device info, email, social media threads, etc.) and is therefore left out of Big Data analysis. This “dark” data is often neglected because it’s too hard to process, but it’s important to have if you want to get real insights, rather than educated guesses.

Third, Big Data inherently doesn’t really have the ability to continuously think and learn based on previous inquiries and preferences. The learning element is where Cognitive Scale is planting its flag.

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What I like about them: To plug the above gaps in Big Data processing, Cognitive Scale has developed a method to bring together relevant internal and external data sources on a platform that understands natural language and generates personalized insights that are continuously learning.

The platform gives users the ability to collect, process, and source “dark data.” Dark data is very hard to collect and process, yet it makes up 80 percent of data today. The platform also comes with prepackaged insights based on data already collected, many of which come in the form of industry-specific apps for travel, health care, and retail. Combining dark data insights with prepackages ones allows users to start acting on data-based insights almost immediately, and the cognitive cloud-based platform then continues to learn as it collects more data.

Of course, the ability to more effectively use data can result in better business decisions and customer relationships.

For example, if a cancer research and treatment center knows that $125B is spent annually on cancer care in the U.S. and that a third of cancer cases are preventable, it could learn from the data that one of the best ways to help prevent cancer is by more fully engaging patients in every aspect of their health on an on-going basis. This includes proactive support for exercise, nutrition, and stress reduction.

Using cognitive computing, the cancer center could bring all of its data together – including patient information, clinical trials data, physician’s notes, lab and imaging systems, a patient’s own reporting of his/her diet and exercise history, etc. Then, by analyzing individual patient history and external data sources, which can indicate the probability of illness, the cancer center could make personalized healthcare recommendations for patients. The more information the system is fed, the smarter and more personalized the recommendations become.

Competitive Landscape: Competitors include Saffron Technologies, Digital Reasoning, Enterra, Numenta, AlchemyAPI, Lumiata, and Quid.

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