To face future challenges related to large-scale IoT, organizations need an optimized storage infrastructure for the constantly growing inflows of Big Data.
As a top contributor to the massive volumes of data created across the globe, IoT needs leaner approaches to data management and enhanced sensibility towards data privacy and data governance. Today’s unprecedented levels of heterogeneity, volume, and connectivity call for IoT data management and analytics strategies that consider scale, data gravity, and integration.
What drives the need for data management within an organization? Below are some answers:
IoT data management can help you understand and utilize patterns that are then incorporated in the decision-making cycle to result in enhanced product design and development. Thanks to data management strategies, businesses can detect errors, analyze performance, and gain fast access to metrics. All of these offer insights into product use that, in turn, help to spot areas needing development and improve existing product versions. As an outcome, insights feeding into product development lead to a better user experience.
Data management in IoT also applies to wear-and-tear predictions for your connected infrastructure and assets. Ultimately, IoT data management helps you look at equipment life cycles and make maintenance plans. You can detect if devices and/or users are operating outside of established patterns and make predictions based on that data. Combining the insights from both user and device behavior will help you conduct predictive analytics and identify areas where your product needs rethinking.
Today’s IoT data management market is geared toward streamlining and effectiveness. In a consumer IoT setting, data management enables better insight into how users engage with your products. Thanks to these insights, you are better equipped to make the right decisions. In observing how users interact with your offering, you can assess preferred features, keep track of engagement histories, and foreclose deterioration.
In an industrial IoT setting, data management helps you keep hold of a multitude of individual devices. When operating as parts of a system, these devices may start to deviate from established behavior patterns. Data management techniques are not simply about harvesting data from individual devices but also about the transmission, storing and organizing of (sometimes massive volumes of) IoT data. Thanks to IoT data management, you can detect problems early on and validate the performance of the overall system.
• Data volume. To face future challenges related to large-scale IoT, organizations need an optimized storage infrastructure for the constantly growing inflows of Big Data.
• Time sensitivity real-time vs. batch processing. Incoming IoT device data has to be (re)organized at the storage facility in real time. The current alternative to this approach is batch processing, which brings its own challenges.
• Heterogeneity (no data structures standards). You can harvest and stream data with the help of different protocols and standards.
• Data flow controls. Keeping track of data transformations is essential if you want to achieve transparency and a clean data flow. You can deal with this task using dynamic SQL, metadata logging, or graphical pipe representations.
• Metadata management. Network health and streaming optimization also have to be addressed. Keeping track of the properties of data sources such as machines, the factory environment, or device data is also important.
• Data quality, transform for usability. Missing data at the storage facility continues to be an issue today. To remedy this, a maximally transparent process should be implemented, and quality management must be automated. This requires a combination of metadata management and data flow controls.
• Creating large data histories. In the data management IoT world, you need to keep track of time series and tags corresponding to data processes. Automating historization and versioning should be a standard.
• Data auditability. In many cases, data has a business value or is collected to solve a problem. It is easier to deal with the storage mechanism if you already have a predictive model motivated by a business question.
Let’s have a look at the top data management requirements.
Given the massive volumes of data generated by the Internet of Things, organizations have to enable the rapid and seamless increase of data volumes. Current infrastructures need to be able to scale without difficulty and do so on a global scale. Grappling with issues such as dwindling storage size and costs should remain a thing of the past—the pace of innovation and the speed of IoT data collection make it impossible to afford scenarios in which storage and costs are concerns. Rather, the data lifecycle has to be re-imagined beyond infrastructural constraints.
Organizations should ask themselves if their existing network, IoT device management techniques, 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. Finally, the IoT device management software should be robust enough to accommocate an expanding IoT ecosystem.
Volume creates value. As the volume of IoT data grows, it acquires data gravity. Growing data volumes enable other applications or functions to generate value out of the data. These applications, in turn, contribute to the generation of even more data volumes.
Massive volumes of data generate greater insight. So the greater the volume of the data, the greater its inherent value in terms of actionable intelligence. This is why organizations need to be able to sustain stable infrastructures that can securely harvest, manage, and glean insights from this data.
The concept of interconnectivity permeates everything that has to do with an IoT system and defines its value. We have wired and wireless devices, processors and storage, services, platforms and applications that transform connection into value. In other words: The value of IoT lies in its high connectivity.
This entails communication and shareability on a variety of levels, starting with the sensor network in edge computing scenarios, the transport of sensor data, and the transformation of the data into valuable insight within the context of a specific IoT project.
So before we start talking about artificial intelligence and best practices in machine learning to increase operational efficiency, we need to get all the way down to the level of the individual devices, the sensors in the field, the wireless sensor network with its unique setup.
Integrating data sources at the edge is key to any IoT data management solution. This involves real-time processing of operational data, secure integration among components and connections within an IoT environment, as well as the security precautions enabling organizations to connect, harvest, share, and manage their data across the entire IoT network.
So what is the best course of action here? For one thing, you can efficiently address the issues of scalability, data gravity, and integration via a global IoT platform with an integrated cloud data studio for IoT analytics. This will enable you to connect, manage, scale, and deploy within a built-in IoT infrastructure.
Rather than setting up your IoT infrastructure apiece, you gain access to a secure IoT data collection and device management platform with integrated IoT application development and device orchestration suites. Complex data analytics can be handled within a cloud IoT data management platform with data science capabilities.
