Key Challenges in Adopting IoT
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IoT is an ecosystem of multiple technologies and their interplay at numerous levels. In contrast to implementing a CRM or an online store for a retailer or creating a data analytics engine for an organization, IoT provides a strategic value to an organization, with only eventual financial outcomes. While it has kindled the imagination of businesses big and small and developers from all corners of the world, the potential impact to their growth in terms of profitability or market cap is not immediately obvious.
IoT, therefore, entails a long road to mass adoption with some very real challenges. Let’s explore a few of them here:
1. Lack of standardization across interfaces, protocols and services
Every industry domain brings its own proprietary protocols used for data communication. These protocols cannot be changed as the devices, machinery, equipment and core infrastructure is endemic to the domain itself. For instance, most oil drilling and submersible pumps use OPC-UA as a protocol for data transmission. Many of the IoT platforms out there do not support OPC-UA out of the box. Similarly, on the integration side, many ERP systems do not support open APIs and integrating an IoT platform with such legacy systems needs custom connectors. Largely, the wide spectrum of device types, protocols and communication methods make it hard to bring standardization across the full range of IoT solutions.
2. Delusional Return of Investment(RoI)
Investment on IoT is essential for any organization to exist and operate in today’s highly competitive world. Not just technology, but almost all industries including telecommunications, healthcare, manufacturing, travel or hospitality are rapidly transforming to adapt to the new connected ecosystem. The difficulty lies in realizing the return of investment.
The promise of hyper connectivity, transaction at the speed of thought, highly automated ecosystem and seamless integration of all systems, is not easy to deliver. The devil lies in the details of implementation. Therefore, the business value of any IoT initiative must be ascertained before venturing into making investments on IoT, else the RoI largely remains delusional.
3. Inability to integrate with multiple enterprise systems
The business value of any IoT solution depends on its integration with multiple enterprise systems. This integration brings in reference data to the platform and throws out processed telemetry data, events and alarms. There can be other domain specific integrations like that with a commerce engine for smart retail or building management systems for smart buildings or emergency services in case of connected cars etc.
While it all seems feasible during the architecture phase, the complexity of integration becomes evident in the implementation phase. Each enterprise and external system that needs to be integrated has its own mechanism of sharing data, proprietary protocols and internal data structures. It's humongous to retrofit an IoT system into an existing IT landscape.
4. Scalability or Portability
Scalability is measured in numerous ways ranging from the number of device connections, to number of integration points and also the number of end users that connect to the visualization portal. With IoT platforms offering managed services, the Service Level Agreements(SLAs) of scalability becomes the responsibility of the underlying platform like AWS, Azure and GCP.
With this freedom comes a caveat. The portability of the IoT solution gets affected. An IoT project using underlying Azure services cannot easily be ported to AWS for instance. The whole solution becomes vendor locked. Unless the organization is married to a particular platform or technology stack, such portability is the cost of having a platform managed scalability of the solution.
5. Expectations on High Availability
Highly available IoT systems with availability requirements beyond the platform SLAs need intensive design. Architecting a solution, with say, an availability requirement of 99.99% using platform components which themselves are only 99.95% available, necessitates ingenious design, beyond the best practices of the platform. For some systems, it could mean an active-active, multi-AZ and multi-region deployment of all services and components with continuous data synchronization and very high levels of redundancy. In reality, this challenge is formidable in the face of the limited toolset available through cloud platforms.
6. Data Security and Privacy
This is, by far, the most critical requirement of every IT system today whether dealing with IoT or not. Some security functions needed in IoT solutions are encryption of data in transit and at rest, endpoint security, infrastructure security, firewalls, OS hardening, code signatures, identity & access management etc. While many platforms offer these services, the lack of which is a non-starter for any IoT solution.
Data residency is another mandate for most organizations. It covers not just data persistence but also processing and sub-processing of data. Any platform worth considering for an Enterprise IoT solution must offer all functions of persistence and processing within the boundary of a country, the organization is operating in. Many countries across the world have strong regulatory compliance around data residency.
Finally, data privacy is another challenging requirement for most IoT solutions. Data anonymization and tokenization are some of the techniques to protect Person Identifiable Information(PII) and Highly Sensitive Person Identifiable Information(HSPII). It's certainly a huge challenge in adopting IoT to situations where systems can access PII and therefore needs to comply with privacy regulations.