Whats the Big Data

By 2006, it was time to move on and “get into something that was more personally satisfying to me,” says Rogers, as “our computational and technological advantages have started to decline.” Healthcare turned out to be the more personally satisfying domain and he became the global product manager for the Humphrey Visual Field Analyser, widely used in Glaucoma care.

In yet another application of adding a time dimension to data, Rogers worked with a research team to move beyond a single, one-time measurement of the patient’s peripheral vision and compute the rate of change and the progression of blindness over time.  “It became an important tool for tracking these patients and their response to therapy,” he says. And in yet another immersion in practical, hands-on computing, the solution involved adding networking software (licensed from Apple) to multiple devices in a clinic to facilitate the collection of data from past measurements.

Better access to data, Rogers understood from that experience, was crucial for improving healthcare. In 2009, when the US federal government started to give incentives for healthcare providers to use Electronic Medical Records (EMR) systems, he saw how the original paper silos were simply replaced with electronic silos, with each EMR system becoming a stand-alone database. Not only there was no physical connection, there was no interoperability “from a semantic point of view—descriptions in one system could not be directly compared with those in another.”

The solution was a cloud-based system that pulled data from a variety of sources and a machine learning software that constructed a table of all the codes and concepts in the clinical data and mapped them to each other. ”The more data we got, the better we got at mapping these concepts and building a robust set of associations,” says Rogers. “That allowed us to build a clinically intelligent search engine.”

You may think that this is “big data” in a nutshell—more data equals better learning or as some have called it, “the unreasonable effectiveness of data.” But you may want to reconsider admiring data quantity for quantity’s sake, given what Rogers and his colleagues found out while mining electronic medical records.

“63% of the key information that the doctor needs to know about you is not in your coded data at all,” says Rogers. “And 30% of the time, if you have a heart failure in your code, it’s not heart failure” and could have been a mistake or a related entry (e.g., a test for heart failure) in the billing system. As a result, most of the learning in Rogers’ machine learning system was dedicated to analysis of the text to “understand what information about the patient is actually correct.” An important big data lesson or what one may call the unreasonable effectiveness of data quality.

That system became the foundation of another startup, Apixio, which has recently raised $19.3 million in Series D venture capital funding. After serving there as Chief Scientist for 5 years, Rogers moved on again, in January 2015, this time from the world of startups to the corporate world and his current role at Intel.

As Chief Data Scientist he works internally on product road maps, providing input related to his expertise and the trends he sees. Externally, he works with the customers of Intel’s customers, helping them in “conceptualizing their entire analytics pipeline.” Providing free advice to consumers of analytics “helps keep Intel at the center of the computational world” and helps keep Rogers abreast of the latest data mining trends and developments. He learns about on-going concerns regarding whether a “new architecture” is required to accommodate the most recent data science tools and observes the rise of new challenges such as “monitoring many different real-time data streams.” And he reports that recently there has been a lot of interest in deep learning. Here, too, a key concern is integration–is it possible to build these new capabilities within the existing big data infrastructure?

Rogers’ role as a trusted advisor also includes working with partners. For example, the Collaborative Cancer Cloud, an Intel-developed precision medicine analytics platform. Currently, it is used by the Knight Cancer Institute at Oregon Health & Science University, Dana-Farber Cancer Institute and Ontario Institute for Cancer Research, to securely share patient genomic, imaging and clinical data to accelerate their research into potentially lifesaving discoveries.

Extrapolating from his current and previous work, Rogers sees the future of AI as “the development of machine learning systems that are good at figuring out the context.” A lot of the recent AI news has been about what he perceives as immature work—“image captioning is a sort of parlor trick,” says Rogers. “We will start to see an emerging AI capability” when we have machine or deep learning capable of identifying the context of the image.

Unlike others who see the machines as potentially replacing humans, Rogers envisions human-machine collaboration: “AI capabilities are most interesting when they are used to amplify human capabilities. There are things that we are good at cognitively but we cannot do at scale. We [should use machine learning] to surface the information from a large volume of data so we can do the next level of inference ourselves,” he says.

Understanding the context. Accepting and managing uncertainty. Linking pieces of data to uncover new insights. Like good data scientists, future business leaders will not look for “the answer.” With the right attitude, experience, and training, they will actively search for data to refute their assumptions, question their most beloved initiatives, and challenge their established career trajectories.

Originally published on Forbes.com