By running analytics on your collected customer data, you can predict customers’ behavior, in terms of what, when, how, where, and why they buy.
Around the mid-2000s, the growth rate of Internet-connected devices, primarily computers and mobile phones, began to outpace that of the global population. By 2008, there were more Internet-enabled devices than people in the world.
That trend will accelerate, but it won’t be personal computers and mobile devices on the leading edge of the next wave. According to Gartner, by 2020, there will be 26 billion non-PC and mobile devices connected to the Internet of Things (IoT), everything from thermostats and cars to medical diagnostic tools and basketballs.
Despite their enormity, these figures only scratch the surface. Cisco estimates that the ceiling for the sprawling, interconnected IoT could be as high as 50 billion total devices by 2020, while IDC shoots for the moon with a prediction of 212 billion.
The only aspect of the IoT more enormous than the “things” themselves will be the tsunami of data unleashed. The IoT will never sleep: billions of Internet-enabled items will become data collectors, producing a deluge of information. This constant stream of data could provide real-time context and sentiment information about customers.
How can companies harness all this potential without drowning?
The Dilemma of the Data Deluge Isn’t New
Much of this information overload will come in the form of “dark data.” As Pitney Bowes wrote last week, dark data is a term (developed before the rise of the IoT) for the information that businesses collect and store, but that has traditionally remained relatively stagnant because it isn’t used for analytical purposes. Dark data can include customer demographic information, purchasing histories and satisfaction levels, or general product data.
Think about all the untapped potential in your existing dark data.
When used selectively, such as to better understand customers, dark data is invaluable to businesses, as it allows them to uncover additional insights more efficiently.
But, is there ever a case of “too much of a good thing” when it comes to data? The Internet of Things may be just as likely to create new opportunities for businesses that properly leverage this information as it is to deepen and further obscure dark data for those that “hoard” extraneous data.
Be a Data Illuminator, Not a Data Hoarder
As the Internet of Things expands, industry observers have been quick to sound the alarm. Some are so concerned about data overload, and resulting security vulnerabilities, that they have called for a Bill of Rights that would govern how data is collected and shared across the IoT.
Even if these macro efforts succeed, the responsibility will ultimately rest on businesses to not become “data hoarders.” Pitney Bowes helps businesses to adopt a “use it or lose it” mentality with dark data. Instead of drowning companies in more data, we help businesses wade through and selectively utilize information, and then transform it into actionable insight.
As an example, with our powerful analytics platform, Portrait Uplift, businesses can leverage information about key target markets and past customer buying history to identify the “persuadables” who are most likely to be influenced by marketing actions, instead of wasting resources on the “sure things” (those who will buy regardless), the “lost causes” (those who won’t) and the “sleeping dogs” (those who could react negatively).
Without a tool to aggregate data and draw out trends and patterns, the information used to form these valuable market segments would drift aimlessly and without purpose. This data clutter is what distracts businesses and prevents them from performing even the basic data analytics practices that were common before the evolution of the IoT. That’s a competitive advantage that cannot be jeopardized.
Dark data contains a wealth of information, and with the right tools, businesses can uncover all of its potential.
– See more at: http://blogs.pb.com/digital-insights/2014/04/28/internet-things-big-data/#sthash.WhNR2h7e.dpuf