How to Break Down Barriers to Enterprise IoT

The Internet of things can be a potential game-changer in the enterprise, but there are challenges to adoption. Here are some issues to keep in mind.

The edge bridges an important and long-standing gap between information technology and operational technology, where the rich resources available in the cloud are not available directly. One of the greatest enterprise IT challenges will be seamlessly enabling and connecting the things that live on the edge of the network: PCs, handheld devices, automobiles, sensors, cameras and other information-gathering devices of many types.

For things to communicate, they need to be connected and uniquely identifiable. As trillions of things come online, they will require unique IP address identifiers—and this will be very difficult at a global level.

Once a thing is uniquely identified, how will other things or interested parties discover that it exists and what data it possesses? Balancing ease of discovery with security will be a huge challenge.

As trillions of things come online, data production will increase exponentially. This will present bandwidth challenges when it comes to moving or sharing the data. It will also result in storage challenges as enterprises become more interested in data archival and analysis.

With dispersed IoT architectures, enterprise organizations may rely on a mix of WiFi, cellular, radio, satellite or others to connect their things. Enterprises will need to prepare for the likelihood of connectivity outages and manage with onsite storage. It will also be essential to determine when data loss is simply unacceptable.

Some of your things may hold highly valuable data but might be old and closed. How do you open them up in a safe and effective way? This will need to be addressed by all enterprises moving to an IoT deployment.

One of the greatest sources of IoT data that will be pushed to the cloud is devices or things at the edge. Access to edge data is essential to any business that hopes to have a complete view of its operational data.

At the operation or machinery level of things, data points will need to be sampled at sub-millisecond rates for data-change detection. In the past, this high-frequency data would be analyzed, acted on accordingly and thrown away. As we move to making this data available to IoT applications and IT support staff, we will require short-term storage to ensure it can be pushed to other parties when they are available.

Connected devices have long relied on a request-and-response model in which one thing asks another thing for its data. A “push” model will be more preferable within the IoT, by which key data simply flows outbound to a cloud platform.

To alleviate some of the preceding problems, IoT strategies and infrastructure should be focused on an edge-in funnel to a centralized but flexible cloud platform. The cloud and its multitudinous resources will make the required storage and processing power available to handle the zettabytes of data that will be collected, analyzed and archived.

• Audit edge devices and infrastructure.
• Get the IT and OT departments talking—both the data and the people.
• Think about connectivity first—get data from operations to the cloud or on-premises enterprise applications.
• Determine key performance indicators for your IoT implementation.
• Measure, evaluate and pivot as necessary.

The IoT in the enterprise world will require both technologies and people that traditionally have been siloed to work together. The IoT holds great promise, but it requires interoperability—from people and machines—to be successful for the enterprise.