How to scale IoT to meet enterprise needs – A roadmap to guide your scaling strategy
Determine business goals
Setting clear goals from the outset is crucial to deliver value and make the most of your IoT investment. While implementing IoT technology, there are very low barriers to entry, including low cost and low technological complexity.
As a result, companies tend to hop onto the IoT bandwagon without defining specific, and measurable business goals. This can lead to projects that are technically successful but don’t deliver significant or tangible business outcomes.
They quickly peter out rather than forming the foundation for a long term, scalable strategy.
To develop a successful IoT strategy, your business focus needs to shift from proof of concept to proof of value. You must select an advanced IoT platform that is stable and secure, to ensure the technology doesn’t just work but delivers meaningful results.
You must also evaluate whether an IoT use case can demonstrably save costs or increase revenue. Implementing a short test run of a particular use case over several weeks is an effective way to test these values, helping to mitigate any bottlenecks ahead of time.
Achieve consistent data management
An increase in devices in use across the enterprise necessitates the need for effective data management. Constant monitoring is required to ensure that the network is operating to the expected standards.
Organizations should ask themselves if their existing network and infrastructure can sustainably handle massive data volumes. Further still, they should be asking themselves what storage solution would be the best — cloud, data center, data stored at the edge, or a hybrid model.
And then again, organizations need to determine the levels of access to the data, retention requirements, and legal concerns associated with the data. Foresight is needed too as existing infrastructures have to be flexible enough to support data processing efforts over the upcoming years.
Reimagine IoT device management
IoT device management is the ability to remotely access, diagnose and manage the functionality of your deployed IoT devices. There are several key factors that illustrate why this capability is critical.
These factors include the size of your network, whether the devices are physically accessible and how widely dispersed your devices are, geographically. Regardless of their number or location, you will need to check on those devices and periodically update their capabilities and intelligence with firmware updates, or download security patches to make sure they are up-to-date, secure and in compliance.
Managing the Internet of Things is quite similar to managing an ant colony. Akin to an ant, each device in a network has a mission. Connected device management is crucial to access and maintain devices that perform tasks and data reporting in mission critical applications.
To enable administrators to maintain devices proactively, deployments in the Internet of Things need to be integrated with IoT application management tools.
Deploy a good IoT device management platform to save time, reduce costs, and enhance security. It can provide the critical monitoring and management tools you need to keep your devices online, up-to-date, and optimized for your specific application requirements.
AIoT – Leverage AI to make your IoT systems intelligent
AIoT involves embedding AI technology with different IoT components. Together, AI and IoT technologies create intelligent, connected systems, where AI functions as a brain to IoT’s body. The IoT devices collect and transmit data from multiple sources to support the learning process involved in AI to carry out automation.
The value of AI in the context of IoT is its ability to wring insights from data. Machine learning, an AI technology brings the ability to automatically identify patterns and detect anomalies in the data that smart sensors and devices generate—information such as temperature, pressure, humidity, air quality, vibration, and sound.
Compared to traditional business intelligence tools, machine learning approaches can make operational predictions up to 20 times earlier and with greater accuracy. Other AI technologies such as speech recognition and computer vision technology can help extract insight from data that used to require human review.
When combined with AI, IoT devices get additional capabilities like learning from user interactions, service providers, and other relevant devices in the network. They are adjustable to new inputs and changes in the environment and execute the tasks without any manual intervention.
The combination of AI and IoT can rapidly increase operational efficiency, span new products and services, reduce unplanned downtime and enhance risk management.
Businesses should also consider the role of edge computing in supporting the deployment of IoT. By moving certain workloads to the edge of the network, devices spend less time on the cloud, react faster to local changes, and operate reliably–even in extended offline periods.
Before diving into the realm of AIoT, ask yourself these basic questions to determine your functional AIoT requirements. Am I using IoT only to monitor? Do I want my operations to react and remediate autonomously with IoT sensor data? Am I looking to achieve predictive maintenance ?
Last thoughts
A reliable technology partner can help you scale your IoT system in a sustainable manner, and maximize your ROI. Figure out custom Industrial IoT solutions to meet your unique business requirements by interacting with an experienced IoT application development company. As an experienced firm offering Industrial IoT Applications, Hakuna Matata can guide you through with its team of experts.
