|Most companies today are struggling with what 5G and IoT entail for their data systems / Photo Credit: jamesteohart (via Shutterstock)|
IT service management company Gartner predicted that there will be 49 million 5G IoT endpoint units installed, yet most companies today are grappling with what 5G and IoT entail for their data systems, wrote Patrick Callaghan of IoT news platform IoT for All. As 5G continues to drive growth for IoT, enterprises must deploy systems to support the data created by connected devices. However, developing these systems requires a new approach to database design.
But first, you must ensure that your data modeling approach is suited for IoT deployments. A time-series data model is better as it can provide better performance for IoT applications that take and consume time-series data. The second step is to put systems in place to review data in real-time but adding streaming data to augment your IoT applications. Moreover, real-time sensors and data flows can be used to safeguard people or goods from potential harm. The third step involves keeping the data you need as storing unnecessary data is costly and resource-intensive. You will also need databases that support a data-tiering functionality where irrelevant data can be stored at different tiers at lower costs. This can be deleted or transferred to long-term storage.
Next, you have to plan for scale. No one can predict how the increase in IoT devices will affect data volumes. However, being able to scale as needed will ensure that the company can keep pace with the demand. For now, hybrid cloud is the best solution to this uncertainty. The fifth step is to automate data replication to ensure you don’t lose valuable information. Through data automation, IoT edge devices can replicate their data to a central repository. Lastly, it is best to add analytics to identify usage patterns and find weaknesses in devices. Real-time analytics tools that synergize with your data system will help you harness IoT data in a 5G world.