How to Manage Large Data Sets from IoT Devices


As the technological era continues to evolve, the amount of data that are collected daily is growing at an ever-changing rate. The ability to utilize a large amount of data helps companies conduct high-level analytics that comes with a myriad of benefits. For example, big data is a method that allows companies to store and analyze large amounts of information on their customers (and potential ones) which improves marketing efforts and the overall user experience.

With that being said, it’s important that businesses understand every potential benefit that data collection brings and; furthermore, how to properly manage said data. For processes based on IoT data, this includes knowing how to handle data all the way down to a granular level. This involves using real-time data on a per-item basis for efficient execution of operational processes.

In this piece, we will discuss how IoT data can be derived and used not just for analytical but also for operational purposes. Particularly, given that the latter allows for real-time insights and facilitates real-time operations and data sharing among different parties.

IoT data analytics – why are they important?

The Internet of Things (IoT) has the potential to take data to places that it has never seen before. Especially with the emergence of inexpensive IoT devices, such as sensors, software, and transmission technologies (among others), the different ways to use data are truly limitless. The ideal software will help manage large amounts of data coming from connected sensors seamlessly, so it’s important to know what to look out for to find the best option.

Data Volume

In terms of volume, the ideal software will have the ability to pull out insights from millions of IoT devices through an advanced-level analysis. This ability is more commonly known within the industry, so many software platforms will offer it.

Less commonly known; however, is the ability to complete processes on a per-item basis. To efficiently manage data in this way, the following process should take place:

Step 1: IoT Data Collection – gathering appropriate information to be used later analysis
Step 2: Thorough Analysis – to describe, illustrate, and evaluate data that will be used to draw an accurate conclusion from
Step 3: Provide recommendations – summarizing the main findings and turning them into recommendations
Step 4: Implementation and repeat – this is the execution phase where all gathered information is used for a purpose.

Then, the same process is repeated.

Analytical data coming from a large number of devices is a great way to optimize processes, but operational data coming in real-time from particular objects can also be a vital player. A real-world application of this would be through digital marathons where attendees run on sophisticated treadmills with a pre-determined route. Attendees know which participants are pushing ahead of them, and the treadmills will adjust their incline based on where a person is on their route. This is an example of utilizing operational data in real-time and how, in this case, having access only to analytical data alone would not allow for the same features. In fact, analytical data would only allow you to analyze history.

Data Structure and Storage

With operational data comes the ability to run ongoing processes and the potential to make on-the-spot decisions. As long as the primary purpose of this data is to build real-time processes, it can also enrich analytical systems, so analysts do not have to rely on outdated information that may (or may not) be the most reliable.

security is vital especially while collecting large amount of data
Source: Unsplash

Through processes like data normalization, even a large amount of data can be reorganized inside of a database to further queries and analysis. Data normalization is a great way to reduce redundancies and help manage data integrity; it is especially important when it comes to managing operational data.

Shared processes where multiple partners work together are better organized when they’re managed within a platform that can handle real-time data exchange and allow for dynamic and independent modification of shared objects. This is because these objects typically involve structure and content that is not affixed at any given time, and the same kind of object can have lots of differences.

How do you effectively manage large amounts of data from IoT devices?

Sharing operational IoT data from devices, especially those that use multiple sensors owned by different partners, can benefit different actors for a variety of reasons. End users, manufacturers, service providers, and other partners, who want to add ecosystems created around such devices can all find a diverse set of advantages here.

Typically, IoT sensors use MQTT and CoAP protocols to transfer data that provide passages for asynchronous communication and interoperability. This is because IoT devices frequently connect over unreliable networks and operate on batteries, so they need a way to communicate even with limited network bandwidth while using minimum power.

In fact, data storage is the main concern for many manufacturers, and traditional databases are just not the ideal solution. With IoT data being spread across a multitude of devices worldwide, traditional databases are not equipped to properly handle every single one. Also, objects that are prone to change and are owned by multiple partners providing their pieces of data are usually not well-supported by typical relational database.

A platform like Trusted Twin is a great alternative wherever shared processes around IoT devices are in place because it allows storing real-time data from IoT devices and acts as a data sharing layer for collaborating partners involved in these processes.

Constant access to operational IoT data

The IoT data that is produced by each device plays an important role in building shared processes among different actors. By accessing data in real-time, external partners can communicate and share accurate information seamlessly.

In the example of Trusted Twin, data collected around objects that can be accessed by multiple actors is essential for building shared processes. Using digital twins (i.e., a virtual representation of an object), processes are tied together between organizations and accounts through identities, ledgers, and documents brought in by these businesses.

In a production chain, real-time access to data coming from IoT devices can help detect any cracks (or bottlenecks) in that system. Having a platform to share that data allows better coordination between multiple partners, such as engineers, quality departments, or external service providers. Based on the patterns coming from analytical data, real-time data also allows to predict failures and react to them on time.

Navigating operational and analytics data

Analytical data is considered “non-real-time” data, and processing times do not matter. Therefore, we typically work to gather every ounce of data that we can and use whatever is relevant to our needs.

On the contrary, operational data (which is “real-time”) is where only relevant information needs to be available immediately to facilitate real-time processes. Especially in the processes involving multiple unassociated partners, it is crucial to make sure that each collaborating partner has access only to the data scope relevant to their role in the process.

Prioritize security

It’s important that platforms place emphasis on security protocols, especially today when millions of dollars have been compromised in cyber-attacks.

IoT data collection needs to happen with high security standards in place
Source: Unsplash

By using platforms like Trusted Twin, a sense of security is maintained knowing that a breach or attack does not mean an entire system is compromised. It’s particularly important in shared processes. Thanks to TrustedTwin’s advanced identity and role management, the attack does not affect data that belongs to other partners in the ecosystem and a compromised partner might be quickly isolated.

When managing IoT data analytics, it is important that data is transmitted safely and securely to minimize the risk of these attacks. This is obtained through secure protocols and proper encryption. In addition, international security standards such as those set up by ISO27001 and SOC2 should be followed to ensure proper protection.

Pay attention to uptime

Uptime is especially important when dealing with any cloud-based service.

But as long as non-real-time data systems going down might be inconvenient or even impact the bottom-line, the uptime for shared processes using real-time data is critical. Imagine one partner providing software that controls autonomous machinery who is cut off from the location data suddenly. Lack of availability can directly lead to said chaos if not a serious incident.

Opportunities from operational IoT data management

Using and sharing operational data, partners can build ecosystems set up for success. External partners have the luxury of receiving data for consistent and seamless communication efforts. End users, manufacturers, and service providers alike can all reap a diverse set of benefits from operational data obtained from IoT devices.

Traditional databases are simply not enough to store and manipulate in real-time data coming from a large number of devices. This is why comprehensive software services are particularly ideal for storing and sharing data from thousands to millions of IoT devices simultaneously, contributing to shared processes among different partners in the ecosystem.

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