The Internet of Things is an important consideration for forward-thinking companies, with an estimated 29 billion connected devices in operation by 2022 and more than 75 billion Internet of Things devices in use worldwide by 2025.
According to Shivnath Babu (chief technology officer, Unravel Data), the IoT devices in use offer enterprises a wealth of data that can be used for powerful insights. This is only going to increase in the future. As enterprises use more smart devices and generate more data, centralised cloud systems are essential to ensure these insights are being used smartly. DataOps is facing significant challenges due to the IoT’s proliferation.
Difficulties handling data
There are many types and quantities of data associated with IoT devices. IoT devices can collect data such as customer sales, mileage driven, GPS coordinates and humidity. They also provide information about the number of people present, vehicle speed, temperature, air quality, and other details. Many businesses are finding it difficult to handle the sheer volume and complexity of IoT data and are experiencing inefficiencies in their data processing. This is especially true for app-driven services, which rely on real time streaming.
Personalised streaming apps like Spark, Kudu and Flink are required to handle the large data requirements of cloud-delivered services. However, stream traffic data analysis and the generation of statistical features require complex monitoring techniques that consume a lot of resources.
Analysts can use multiple detection methods to analyze the data simultaneously, but this can lead to complexity and performance issues. This is especially true when applications span multiple systems (e.g. Interacting with Spark for computation, YARN to resource allocation and scheduling, HDFS or S3 data access, or Kafka or Flink streaming. These deployments may become more complicated if they include independent programs that can be used to repeat data processing or generate feature in multiple applications.
IoT explosion
Current data management tools and processes don’t have the capacity to create the cloud infrastructure required to support the rapid growth of IoT devices. Many businesses recognize the need to integrate AI and ML in order to manage the challenges presented by IoT devices.
These integrations enhance the data team’s ability to make sense of all the data. They enable intelligent data operations, which reduce the manual sorting of data. This allows data to be routed faster, keeps up with business requirements and maintains the real-time element in their dataops.
These situations can cause streaming applications to lag in processing data in real time. It can also be difficult for complex systems to determine the root cause. A data deployment that uses machine learning and artificial Intelligence (AI) is more likely to deliver the reliability, predictability, and performance required than other options.
Machine learning algorithms are essential for enabling continuous and efficient data collection from IoT devices. They allow application execution to be inspected, identify potential failures, and generate recommendations for improving performance and resource use. These processes also have the added benefit of allowing organisations to enjoy lower costs while ensuring greater reliability.
Take into consideration each use
It is important to examine each use case in detail and identify the IoT problem it is solving. IT teams can make faster progress in implementing the solutions by understanding the environment and the problems it creates for their organisation. Delivering an IoT-based deployment requires that the data team is augmented with automation in order to manage the complexity.
