Today there’s a lot of discussion around edge computing and its role in helping to bring data analysis and management closer to where devices live (and work). For IoT solution architects, edge computing can deliver significant benefits in reducing latency, eliminating device exposure to Internet threats and controlling cloud costs by dramatically reducing the amount of data transmitted and processed using cloud providers infrastructure.
One area that edge computing is being applied to is IoT data management. In an IoT deployment, edge computing data management can be integrated primarily in two ways:
On the device or sensor itself; and
In the network on routers, gateways or access points (or what we call “network-integrated data management”).
While many OEMs are starting to develop and deliver new machines and sensors that have the ability to do some edge-computing functions and processes (such as limited device data management), many of today’s currently deployed industrial machines and sensors are not able or have limited ability to be adapted for edge computing.
Using a network-integrated approach to edge computing data management enables IoT solution architects to address heterogeneous IoT enterprise data issues by:
· Eliminating data silos. Network-integrated data management provides the pathway for IoT developers to integrate different device and sensor data streams into the heterogeneous enterprise workflow. By allowing for different data streams to be automatically directed to the right business applications at the right time, companies can more quickly act on IoT data analysis and less time on moving data from one point to another.
· Enabling existing machine investments to participate in IoT projects. One of the largest challenges most companies face when considering an IoT project is the cost of upgrading or ripping and replacing existing machines. This often results in many companies choosing to limit their IoT deployments to new machine investments – and subsequently, reducing the advantages that a fully realized IoT strategy can deliver.
With a network-integrated data management solution in place, IoT solution architects can easily integrate both existing machine investments and edge-enabled devices. Also, by integrating a ready-to-use data management solution in the networking infrastructure, adding new machines and sensors to the heterogeneous machine network becomes a configuration exercise – versus one that feels like remodeling the Eiffel Tower.
· Reducing or eliminating the need for custom programming and software development. Even with edge computing-enabled devices, IoT solution architects must spend a lot of time and resources on developing and maintaining custom firmware, APIs and programs to connect machine data to business applications and the cloud. Ready-to-use network-integrated data management solutions, like Machinechat JEDI IoT Data Manager, eliminate the need for coding and allow IoT solution architects to focus more time on transforming IoT dataflows into business productivity solutions.
The bottom line? Incorporating a network-integrated data management approach enables organizations to:
Bridge the gap between OT data and IT applications and the enterprise business workflow.
Extends the reach of your corporate IoT strategy to include existing and future machine and sensor investments.
Reduce the cost of IoT deployment and on-going cloud transactional costs.
Integrate and deploy IoT projects faster – and accelerate their path to IoT ROI.
Want to learn more? Check out our blogs on the four reasons why network-integrated data management makes sense and how to build a data management application. Stay up to date on Machinechat's latest news and updates by signing for our mailing list here.