Turning “big data” into “smart data”: A key theme at ePharma Summit 2014
Earlier this week, Visible attended the ePharma Summit in New York City, where social data and social analytics was a frequently discussed subject among the various panelists and keynote speakers from the world’s largest healthcare and pharmaceutical companies. One speaker effectively summarized this theme by talking about the difference between “Big Data” and “Smart Data.” There is a rising need in the healthcare and pharmaceutical space to more clearly understand various behavioral patterns of patients with various types of diseases and conditions and their so-called “patient journey,” which may include within the diagnosis and treatment stage, the use of various prescribed drugs. In addition to understanding the patient journey and various behavioral trends, such as a patient’s propensity to follow their prescribed treatment plan, there’s also a need to target and understand the voice of the physician or healthcare professional.
In both cases, understanding this level of detail requires the ability to slice and segment data from the social web in thoughtful and repeatable ways, so that key findings can be substantiated through the analysis of a particular trend over time. Visible calls it “getting to wisdom,” but it’s the same thing as turning “big data” into “smart data.” Smart data is contextual. It’s the ability to create meaning out of chaos, and above all, it’s actionable.
As an example, let’s look at Type 2 Diabetes. For a pharma company who has drugs on the market that address this disease, it’s potentially interesting to look at all conversations around Type 2, however, there’s not likely to be much actionability when looking at millions of posts. What’s more impactful is to compartmentalize the data, beginning by splitting it into various segments representing each stage of the patient journey, as an example, pre-diagnosis, diagnosis, treatment, and remission. From here, we can take a deeper look into any of these stages, and apply additional types of segmentations that increase the likelihood of finding something actionable. One example would be looking at the segment of data representative of Type 2 Diabetes patients in the treatment stage, then building additional queries that are designed to examine whether or not patients are adhering to their treatment schedule, which may include discussions around specific types of drugs that treat Type 2 Diabetes.
You’ve effectively compartmentalized an enormous amount of social data, to now focus in on a specific trend in a sub-segment of the overall data population. From a researcher’s standpoint, this yields more actionable insight because we can now score the relative frequency, or percentage in which specific trends are occurring over long periods of time, validate them with historical data, and help guide a number of business decisions across departments within the company.
Did I mention that these techniques can all be done within the Visible Intelligence platform?
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