Why IoT needs data solutions: The what, how, and why of data
Much of the focus on the Internet of Things (IoT) has been on the “Things” themselves – the sensors and the everyday (or not so everyday) objects to which they connect. From connected home products that promise energy-savings and on-demand responses to personal health trackers to keep track of our diets and exercise, connected products are both big news and big business.
As described by the Harvard Business Review, products like health trackers have three core aspects: physical components, smart components, and connectivity; “Smart components amplify the capabilities and value of the physical components, while connectivity amplifies the capabilities and value of the smart components and enables some of them to exist outside the physical product itself.”
As the industry of connected products continues to mature, clear front runners will emerge and likely be acquired by the major players in each industry. However, there remains an untapped and unspoken-for aspect of IoT: data. What will happen to all of the data collected by these sensors? How will data become a commoditised product? How can businesses prepare now to not simply store and protect this data but to actually use it to drive everything from product design to buyer behavior?
Taking a look at the big picture
Let’s start by taking a look at the data collected from one connected product – a health tracker. As the user sees it, the product is tracking activity level, steps, and sleep patterns; however, to calculate this information, the sensor(s) is really tracking movement, pace, heart rate, perspiration, pulse, and location in addition to steps and sleep patterns. The software needs this information in conjunction with the user’s inputs (gender, age, weight, etc.) to calculate an “activity level” for the user.
So, now that the manufacturer of the health tracker has this information – what can they do with it? This type of information could be used to do everything from predicting trends to steering buying behavior. But it is useless on its own – as a set of static data points. The power comes in combining this data with that of many other sensor manufacturers. Who will do this?
Data intermediaries, ‘datafacturing’ and infomediaries
Just as the advancements in sensor production, operating system size and functionality, and connectivity have driven us to the Internet of Things, the breadth of available data is driving the creation of new data-centred ecosystems.
According to a white paper published by Cisco, big data comes in two forms: structured and unstructured. Structured data is simple to deal with and is what most businesses are already harnessing to drive efficiencies and optimize processes. Unstructured data is unwieldly; it is “random, difficult to analyze and enormous.” The extent of data available from connected products (health trackers, smart appliances, connected vehicles), the public internet (social media, searches, apps), and corporate or private intranets (customer relationship management tools, inventory management tools, supplier management tools) cannot be tackled by experts that are already neck-deep in the demands of their own industry.
When combined with volume (Facebook alone registers more than 2 billion likes and comments a day) and velocity (data creation outpaces the current abilities of data warehouses processing) the need for data-focused experts arises. The players in this area will not be linked to any single industry and will, instead, act as the intermediaries between industry, consumer products, and marketing.
Data intermediaries, informediaries or data-facturers will gather information from all sources and develop the tools needed to analyze that information and distill it into actionable facts – in real-time. These facts will drive actions that improve customer retention, reduce traffic congestion, reduce energy waste, improve health and eliminate unsafe conditions. The creators of this data (which, in many cases, will be users) will begin to understand the value of the data they create and their ability to trade that data for real value.
New companies are just beginning to build the teams and the solutions that will drive the big data analysis industry – and it is a monumental task. Developing a network capable of gathering data is only half the battle. The real-work will come in creating algorithms to process all of the incoming data, triggers to note action-worthy trends and dashboards to display the results – all with the capability to keep up with an ever-increasing stream inputs.
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