One of the more interesting aspects in the evolution of big
data is that it has largely remained in the background for the average
consumer. While recent scandals involving Big Brother-esque government spying
put big data on the front page, few consumers can articulate how big data has
impacted them personally.

For most companies, consumer-related big data has focused on
acquisition and marketing. Companies are extensively mining consumer behavior
and attempting to correlate it in a manner that allows them to predict
behaviors and, ultimately, sales. Interestingly, big data has come of age
concurrently with sensor-enabled consumer products, where everything from
thermostats to workout gear has embedded sensors and WiFi radios onboard that
can gather all manner of data. The companies behind these products are often
leveraging these data, but still missing a key opportunity.

Beyond marketing

My wife is a fairly technology-savvy consumer and tends to
be on the cutting edge of new mobile apps, especially when it comes to
applications that are family- or child-related. When our children were infants,
she would diligently track feedings, sleep schedules, and even diaper changes
in a baby-related app. When I gave her some gentle ribbing about her data
obsession, she noted that she had been able to correlate sleeping longevity (a major
“KPI” for any parent) with feeding times. As the babies started to
eat solid foods, she could similarly see which would affect sleep or diaper
changes.

She complained that she identified these correlations
through deduction, rather than the app highlighting these fairly obvious
trends. Similarly, many of the consumer devices that gather all manner of data
report it to the cloud for marketing analysis, but do little more than rote
trend analysis for the consumer who actually purchased the product. Clearly
everything from my favorite free diet application to my running watch are
reporting data to backend analytics that are likely driving targeted marketing,
but none of them perform even basic data analysis that might proactively warn
me that I tend to have a few too many cocktails on Fridays, or that my running
pace is significantly better in the evening versus the morning.

What’s rather surprising is that the necessary
infrastructure to offer consumers big data-driven analysis already exists. Most
of these data gathering applications already report their findings to a
cloud-based server, and in all likelihood the owner of the service is already
slicing and dicing data for marketing purposes.

Productizing big data

The products you can derive from your big data resources
should be fairly obvious. Put yourself in the consumer’s shoes and ask “What
would I like to know from these data?” In most cases, the components
necessary to intelligently address consumers’ concerns are already in place.
For example, companies like Google are slowly moving in this direction. For
years smart phones have held calendars, clocks, and mapping applications, and
Google finally combined the data from each application to tell users what time
they should leave their current location in order to arrive at a scheduled
appointment. Similarly, applications to locate friends and family were a
popular trend, yet only addressed one of the consumer’s obvious concerns: where
is my friend and when will they get to me, a major missed opportunity.

The opportunities to answer consumers’ questions are
everywhere. My Nest thermostat is a beautiful device and can be controlled from
anywhere in the world, but the company still doesn’t answer fairly obvious
questions like: “If I adjusted the temperature, how much money would I
save on my electric bill in the summer and winter?” or “How about we
adjust the temperature to save energy when it’s cloudy outside?” Once
again, the connected device is there gathering data, historical and forecasted
weather data are readily available, and trend and energy usage analysis can be
easily culled from the thousands of Nest devices in the field. I’d be willing
to pay for the fruits of big data analytics and, when combined and presented in
novel ways, big data can become a product in itself rather than just a forecast
engine.

When
you consider the vast amount of resources many companies have invested in their
big data capabilities, it’s a wonder more companies are not attempting to
provide consumers with analytics-driven products. Instead of using big data
only to sell, why not sell the output of your big data itself?