"The characteristics of IoT architectures mean that information management practitioners must swiftly become adept at managing many pieces of information spread over a wider and more diverse landscape of platforms than ever before," Heudecker adds.
Organising and managing highly distributed data is by itself a significant challenge; but ensuring the distribution and consistency of business rules are applied to the data, and monitoring the execution of those business rules, adds additional layers of complexity.
Steps for Success
Embrace hybrid architectures:
“IoT solutions will generally involve a combination of platforms, with data and process on that data being located "on-device" and in traditional on-premises and cloud-based environments,” Heudecker adds.
“It is therefore important to avoid forcing analytics and information management solutions into a monolithic or "one size fits all" deployment model.”
Plan for disaggregation and resiliency:
“Workloads must be able to be broken into components that can run anywhere,” Heudecker says.
“This means that the implementation of existing business logic for processing data, and current choices of data management infrastructure tools, may not be suitable for IoT requirements — driving organisations to re-architect or modernise those capabilities.
Focus on monitoring and manageability:
“IoT architectures will often be the opposite of monolithic, increasing the challenges of monitoring and managing distributed data and its consumption,” Heudecker adds.
“As a result, information infrastructure capabilities that can be located and managed anywhere should be deployed.”