With around 2 billion IoT devices connected somewhere in the ethereal Internet of Things (IoT) landscape, it’s important to be sure to design the devices so they actually “talk” to each other. For example, let’s look at transportation and how interoperability can affect that sector.

Currently, vehicles carry an average of 60 to 100 sensors. As cars become more intelligent, hence the need for the Intelligent Transportation System (ITS), the number of sensors is projected to surpass 200 per car. Recent reports have estimated that these numbers will grow to about 22 billion sensors in the automotive industry per year by 2020.

With that in mind, it’s critical for sensors to interoperate both within the vehicle and to entities external to the vehicle for an enjoyable, sometimes entertaining, yet safe and secure experience. An extreme example of this can be seen when two cars collide, although in theory sensors would mitigate this as well. After a collision, sensors may fire to the following, to name a few:

• Emergency response
• Roadside assistance
• Traffic control
• Manufacturer
• Insurance
• Home notification systems
• Fleet management
• Train tracks and signals
• Hospitals

In an ideal IoT interconnected world, interoperability would allow all of these “domains” to immediately talk to each other, setting off alerts for an ambulance and/or tow truck, to traffic signals to alleviate congestion, to the manufacturer to see what might have happened mechanically, to a usage-based insurance company, to the driver’s home notification system, to possible fleet management systems to show delays in shipping, and so on.

Today, the schemas, protocols, formats, regulations, policies, and standards can be vastly different among different sensors and devices. It’s important to connect data sources coming from multiple systems in different schemas, protocols, and formats. And at the same time, the IoT design should allow a system to extract, transfer, and asynchronously load the data to different business-intelligence tools and analytics platforms, dashboards, and storage systems, providing interoperability to the interconnected systems that would otherwise live in silos.

Interoperability should be an integral part of IoT design. In addition, the interoperability should be seamless. Processing large volumes of disparate data coming in at high speeds not only requires vast computing resources, it also takes a long time. The time delay from when the data is received to when it turns into actionable insights can have a ripple effect, and often cause catastrophic results, as can be inferred from our transportation scenario above. Delays can also cause financial losses, which can include:

• Costly infrastructure
• Down time
• Operational inefficiencies
• Disaster recovery costs
• Policy violations
• Security violations
• Costs of standardization
• Lost opportunities

What’s the source of the delay? Existing solutions first store data, and then make sense out of it when needed. By doing this, 80 percent of an analytics project typically involves data preparation for analysis when it’s needed, leaving only 20 percent for actually performing the analysis. Data preparation includes items such as indexing, mapping, data reducing, organizing, and cleaning.

Traditional approaches try to solve this by making storage bigger and faster (improving traditional databases and building new storage solutions such as Hadoop, in-memory, and others), and building better analytics on top of it. But this comes with complexity and implementation expenses, as well as scalability and stability concerns.

A different approach, that of first understanding the data and then acting upon it in real time before storing it, is a required design element. In this case, business logic can be applied early in the process before data is stored; optimizing what needs to be acted on in real time and what needs to be routed to respective downstream sources.

This first-make-sense-out-of-it-then-store-it approach lets enterprises efficiently manage, distribute, and track real-time IoT data, basically providing the Interoperability for Things for the Internet of Things.

Barry Strauss is the head of marketing at Talksum, a leader in high-speed data processing and management solutions whose mission is to develop new ways of processing, routing, and managing data. He holds a BA degree from the University of Missouri.