Overcoming the Top 3 Obstacles of Internet of Things (IoT) Applications at Enterprise Scale

CIOs around the world are embracing Internet of Things (IoT) ecosystems in their IT infrastructure to modernize their technology stack.

Many organizations that were early adopters of the IoT are reaping the benefits of the Enterprise of Things (EoT) to scale their business. Industry veterans are taking an innovation-driven approach to explore more opportunities from IoT and its positive influence on business outcomes. However, what hinders the adoption of the Internet of Things in the Informatique infrastructure are the inherent challenges it imposes.

Here are some challenges of an enterprise-scale IoT ecosystem:

The IoT generates a huge amount of unstructured data

One of the major challenges in adopting the Internet of Things is the need for highly structured data. Systems need precisely defined categories to segment data collection. Companies that fail to define clear categories to process data will end up in a data mess. It can be difficult to evaluate unstructured data and gain actionable insights. Data scientists should consider analyzing data based on its quantity, type, and speed. Moreover, it is crucial to ingest clean data into the Data Lakehouse for effective data analysis.

But the challenge is in many places, the data entry points are manual and there is a potential risk of human error. This degrades the machines ability to read data accurately. Therefore, CIOs should consider the amount, type, and speed of data as their primary consideration when integrating the Internet of Things into their IT infrastructure. Companies can hire skilled data scientists or outsource data management to vendors to model the data.

Read also : Reshaping enterprise infrastructure for a future-ready business

The need for real-time data analysis

Modern businesses need devices that can process data in real time, even when the connection is lost, for businesses to scale. This is another significant challenge when adopting the Internet of Things in IT infrastructure.

Integrating edge computing into the technology stack is an effective way to overcome IoT challenges by bridging the gap between data processing and analysis of information generated by Internet of Things devices. .

CIOs can prioritize and reroute device data based on business needs. Rather than ingesting all the data from the device, companies can ingest only a small amount of data into the main site, which they use for the long term or for further analysis.

Embedding advanced artificial intelligence (AI) and machine learning (ML) tools into IT infrastructure will help organizations anticipate potential roadblocks in designing a response plan. This approach allows companies to improve product quality and implement predictive maintenance to minimize data flow interruptions. DataOps may consider designing, testing, and deploying ML models for IoT predictive maintenance. CIOs should consider implementing the best workflows and tools based on containers, Kubernetes, agile development, AI/ML, and automation to effectively leverage internet of things in the IT infrastructure.

Complexities and security challenges that come with the Internet of Things

IoT ecosystems must have different devices of different types that must be seamlessly integrated to have efficient information flow. Often, assets share the same architecture, but devices from different original equipment manufacturers (OEMs) have distinct architectures and operating systems. This is another major challenge because when devices interact with each other, the code sent by one will not be interpreted by the other. The implementation of a common interface is necessary to decrypt the commands and execute the converted commands on the next Internet of Things device.

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