Big Data for Health

What do Iowa cows, NSA staffers, a Georgia homeowner with central air conditioning, a Netflix subscriber in Alaska, and a hypertension sufferer engaged with Healthrageous all have in common? They are all beneficiaries of Big Data, the pattern recognition trend being deployed successfully by so many firms in varying fields.

The way Big Data works is elegantly simple; yet execution can be extremely challenging and difficult. Random data are collected in codified bits and bytes and run through large computing systems to generate meaningful information about the end user or target.

At Palantir Technologies, a firm engaged by the Pentagon to keep America safe from terrorists, their mission is described thusly: We live in a world in which data, technologies, humans and environments are linked, at massive scale, often in unseen ways. At Palantir, we build the software that makes these unseen connections visible and intelligible. We engineer so that the world works – with less risk, higher potential and greater good.

Those who are producing the dairy and beef for the insatiable American appetite (The US consumes twice as much beef per person as the next closest country), have typed each cow in the herd in order to personalize nutrient-rich feed bags to optimize beef yield or milk production. Information on each cow is collected, sorted, analyzed and parsed through massive algorithmic-intense databases, with intelligent software performing the challenging, but rewarding analytics.

Opower is a new customer engagement platform for the utility industry. It reinvents the way utilities interact with customers—from the quality of the information provided to the way it’s presented and delivered. It helps people use energy more efficiently and ultimately save money on their energy bills. 
And it vastly improves the overall customer experience by making energy use personally relevant.

Few don’t understand how Netflix’s Big Data play is to observe – through data crunching – each subscriber’s personal tastes in media viewing in order to better appreciate how to service that customer with recommended film options.

Which brings us to Healthrageous; the machine learning delegate of the health care industry. If the previous cited examples can better understand their customer/target (Palantir’s focus on terrorists hardly qualifies as a customer intimacy example), then it stands to reason that Healthrageous can use Big Data and algorithmic computations to discern what is the special sauce that motivates each consumer to pursue a life with glycemic control, or more activity, or managed blood pressure, or fewer calories.

Every person seeking to improve their own health has begun a journey unique from others. Granted, some may want to walk their daughter down the aisle on her wedding day, but the journey to achieve that end may be highly personalized around which flight of stairs to trudge up each morning and what kind of yogurt to snack on in the late afternoon.

Just as energy users, terrorists, cows, and DVD-viewers all have their own unique reasons for doing what they do, so too do Healthrageous participants who have decided to change the way they’ve been living and to experience a new adventure that will lead to a healthier, longer, more satisfying life.