The ability to make sense of unstructured information and make predictions about diseases that could develop, thereby enabling physicians to make better decisions about treatment, is the goal of a set of tools from IBM that include its highly publicized Watson technology . Voluminous amounts of information come at doctors all the time, especially with thousands of new articles being published monthly in medical journals. Add to that lab results, health care systems and social networking sites for professionals, and it can become overwhelming pretty quickly.
"As you gain more and more information in the world, it becomes impossible for a person to manage a physical library or have an electronic collection unless you can create very intelligent access to it,'' explains IDC's Feldman. "Language is so rich and so variable that there are too many ways of saying the same idea, so you really need technologies that can understand all the various ways we can understand something."
Tools that structure various sources of information by applying analytics are helping the health care industry -- among others -- gain a whole new level of intelligence, observers say. IDC is projecting the overall big data information technology and services market to grow from $3.2 billion in 2010 to $16.9 billion in 2015.
"Why Watson is such a breakthrough is it understands questions and tries to interpret them," while making hypotheses through its text mining and analytics capabilities, says IDC's Feldman. IBM built Watson to do what it calls "Deep Q/A," she says, with QA standing for question and answering.
"It's a whole new approach or technology that tries to understand what people are looking for," she says. The technology understands how one entity affects another; in health care, for example, the computer can analyze how a drug affects a particular type of person who has a certain type of disease.
This approach is much different from how a search engine works. A search engine attempts to answer a question that has been input using a few keywords, says Kohn. "Then you get sites or pages to look at to see if any are actually relevant to the question you have in mind. That takes a lot of time ... it's an ineffective process at best."
In contrast, Watson understands a question that's been posed in natural language, reads literature and, using a series of massively parallel "probabilistic algorithms" to analyze the information it is given, goes out and brings back prioritized suggestions on whatever the topic, Kohn says.
Answering complex questions