Architectural Implications of IoT Data

From an architectural perspective, we need to be aware that IoT devices generate massive amounts of data on an ongoing basis. These data sets go to the full data management life cycle; for example, in storing, analyzing, re-building, and archiving.

Took a quick shot while authoring this article in Canberra — Australia

Considering the scalability requirements for digital transformations, we must carefully consider the amount of data produced by IoT devices. The voluminous data in the enterprise require careful performance, scalability, usability, security, and availability measures.

When dealing with IoT in the digital transformation programs, as transformation architects, we must take the responsibility of data management requirements at the program level. We need to simulate the actual workload models based on the functional and non-functional requirements. We also need to consider historical data and future growth as part of the requirements analysis for performance.

Due to potential implications for enterprise and our transformation programs, we must plan data collection via IoT sensors carefully. First, we need to determine the type of physical signals to measure.

Then, we need to identify the number of sensors to be used and the speed of signals for these sensors in our data acquisition plan. Transformation architects need to closely work with the IoT Solution Architects to create stringent governance around data collection plans.

In addition to the challenges of massive data, application usage patterns are also an essential factor for the performance of IoT solutions in the enterprise modernisation and digital transformation initiatives. In particular, the processors and memory of the servers hosting the IoT applications need to be considered carefully using benchmarks.

Using benchmarks for application, data, and infrastructure, we need to create an exclusive IoT performance model and a set of test strategies for our transformation solutions.

The IoT performance model mandates more data storage capacity, faster processes, more memory, and faster network infrastructure. While in the traditional performance models, we mainly consider user simulations, in the IoT Performance models, we also consider the simulation of devices, sensors, and actuators across the enterprise.

From a data management perspective, it is paramount to be aware of data frequency shared amongst devices. We need to consider not only the amount of data produced and processed but also accessed and shared frequently by multiple entities of the IoT ecosystem.

We must be mindful that the monitoring of these devices also creates a tremendous amount of data. If we always add the alerts and other system management functions to keep these devices well-performing and available, we need to have a comprehensive performance model, including the system and service management of the complex IoT ecosystem.

IoT solutions for modernisations and digital transformations span across multiple domains such as data, security, application, network, storage, and integration architectures. An end to end architecting approach coupled with use of powerful tools is essential to deal with substantially growing IoT data.

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