Apr 09, 2017—
In nearly every industry, the Internet of Things is quickly moving from a buzzword to a key technology that impacts an enterprise’s bottom line. In fact, Gartner predicts that endpoints for the IoT will grow at a steady rate of 32.9 percent through 2020. In order for enterprises to get the most out of the IoT, the problem of data integration must be solved.
In other words, it is not enough to have devices spew out data; said data must be gathered and sent to storage or to an application where it can be evolved in the context of the overall business. It is the integration, not the data itself, that truly enables the IoT. Unfortunately, those in charge of IoT programs often know little about the actual integration aspect, and when such programs are built on a public cloud, they can become complicated. This is where responsive data architecture (RDA) comes into play.
Start With the Business Need and Data Storage
IoT planning should always start with the business need, but data collection should not be too far behind that. Quickly figuring out what core data to collect and ways to effectively leverage that information are the basis of IoT programs, and must not be overlooked. Once the type of data is determined, there must be a place to store it.
This is the tricky part, as the IoT has dramatically changed the requirements for processing and managing data. It requires a common architecture that leverages commodity cloud and non-cloud technologies, and that can be easily mapped to existing and emerging technologies. Additionally, there is the issue of latency, or the amount of time it takes to transfer data from the device or sensor to the cloud. This often takes too long to transfer and can complicate the process.
IoT Applications Move to the Cloud’s Edge
The issue of latency can be avoided by building IoT applications at the cloud’s edge. This means that instead of all gathered data being sent back into the cloud, data and applications are built on the edge of the network, which can handle most of the gathering and processing. Edge computing isn’t a new phenomenon, but applications involving the Internet of Things often need to react instantly to generated data, so it continues to evolve and remains a popular option for deploying the IoT.
Build a Physical Architecture for Edge Processing
Edge computing pushes most of the data processing out to the edge of the network, close to the source. This model allows workloads to be divided between the data segment (traditionally residing on a public cloud) and the compute segment (near the IoT device). The goal of edge computing is to process data requiring rapid turnaround so that it can quickly return results to the device.
The architecture also must be able to leverage data-response processing, as well as the cognitive computing that exists at the network edge, and should be automated to be able to augment the rules, policies and behaviors of data. Common security and governance procedures and models must also be leveraged. The resulting benefits will be high-performance data processing, direct behavior interactions and automated learning that will constantly improve the IoT system’s value.
Building the Database Layers
Responsive data architecture relies on four distinct layers: the physical database layer, the virtual database layer, the data response layer and the service/API (application programming interface) layer. An RDA takes existing computing concepts that are trusted and provides a well-structured physical reference framework that developers can use. Each component should be able to work on its own, but leveraging them together offers added value.
As IoT systems are built on public clouds, performance issues consistently stem from the limitations of cloud platforms. With an RDA, these limitations can be removed, and a starting point for building highly effective IoT systems on public clouds can be offered, thereby providing a practical solution to this common problem. RDA ensures that data is quickly transferred from the device or sensor into the hands of key decision makers, meaning there is less time spent waiting for the data and more time taking action with it.
Scott Udell leads the IoT practice for CTP. In that capacity he is responsible for setting CTP’s go-to-market strategy and execution for all things IoT. Prior to CTP, Scott served as COO of Weft, an IoT/M2M Supply Chain company, led business development at IntelliVid, a video analytics company; at OATSystems, a pioneering RFID software company; and at Novell/Cambridge Technology Partners, where he led North American eCommerce practice and global alliances. Scott has also served as the head of delivery at Ness Technologies. He began his career with IBM/Lotus Development Corporation
