Illustration: © IoT For All

Gartner predicts that by 2023, there will be 49 million 5G IoT endpoint units installed, yet today, most companies are just beginning to grapple with what this means for their data systems. As the promise of 5G continues to drive massive growth for IoT, enterprises must put systems in place to support the data created by all these connected devices.

Building these systems requires a new approach to database design. The approaches we’re using now simply won’t be able to handle the sheer volume of data or deliver its full value.

What do you need to do now to prepare your data system? Below are six steps to take.

Ensure Your Data Modeling Approach Is Suited for IoT Deployments

Relational data models aren’t going away anytime soon, but they aren’t designed to handle the requirements of IoT. A time-series data model is better suited to manage IoT data sets, where events have to be written based on time then analyzed based on when they happened. This type of database provides better performance for IoT applications that take and consume time-series data. IoT devices need this, so adding time-series database models sets a data foundation that can support them.

Put Systems in Place to Review Your Data in Real Time

Connected devices generate data constantly, and this data has to be sent and received. It also often has to be acted upon rapidly, which means you need the ability to review data in real time. The best way to do this is to add streaming data to support your IoT applications. For example, sensor data can be streamed for analysis and any needed real-time response. In a medical setting, a patient can wear a heart monitor at home that sends signals to the healthcare provider and alert the provider if the rhythm changes. Real-time sensors and data flows can be used to protect people or goods from potential harm. Only a streaming architecture that supports input from multiple sources of data, simultaneously, can offer this.

Only Keep the Data You Need

While IoT generates a ton of data, some of it loses its usefulness very quickly and doesn’t need to be kept. For example, data that’s collected and useful during a project may not need to be kept once the project is complete. Storing unnecessary data is costly and consumes resources. IoT data will need to be evaluated for its value and relevance and assigned to an appropriate level of retention. You’ll need databases that support a data-tiering functionality where data that loses relevance can be stored at different tiers to lower storage costs. At the appropriate time, this data can be moved to long-term storage or deleted.

Plan for Scale

More enterprises are turning to hybrid cloud to take advantage of the cost-effectiveness of the public cloud, while still retaining control over their own future. IoT will demand an ability to scale as data volumes grow, and hybrid cloud can enhance IoT performance by keeping data processing closer to where the data is created, while still being managed in a centralized repository.

No one can really predict how the increase in IoT devices will truly impact data volumes, but we do know that being able to scale as needed will ensure the enterprise can keep pace with the demand. Being able to scale data out horizontally is also more cost-effective, and for now, hybrid cloud is the best solution to an uncertain data future.

Automate Data Replication

Replicating data ensures you don’t lose it, but it’s challenging from an operations perspective and also for compliance in some industries. Data replication transfers data between nodes. By automating this process, IoT edge devices can easily replicate their data to a central repository. Using the same approach to data across all nodes, from individual devices to edge data centers to the central database, simplifies this further and makes sure applications will run the same way over everything.

Add Analytics

Lastly, IoT will need robust analytics. The more data there is, the more you’ll need to make sense of that data’s value. Analysis helps you uncover usage patterns and spot weaknesses in devices. Real-time analytic tools that integrate with your data system will help you leverage IoT data in a 5G world.

Taking these six steps will create a solid foundation for a fast-approaching 5G IoT future. By ensuring that your data systems are compatible and supportive of 5G and of the data created and analyzed by IoT devices, your enterprise will be able to leverage the advantages of IoT and deliver the experiences today’s modern customer expects.

Written by Patrick Callaghan

Written by Patrick Callaghan, Enterprise Architect and Strategic Business Advisor, DataStax

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