With the continuous promotion of the Internet of Things technology,
data integration has become one of the biggest obstacles facing the adoption of the Internet of Things. Solving this challenge may require technology leaders to rethink and transform their traditional IT infrastructure.
The Internet of Things (IoT) is rapidly becoming a technological necessity for modern enterprises. Aware of this, companies are stepping up their efforts to implement and expand IoT networks. However, although companies are increasingly investing in IoT initiatives, they are also facing multiple challenges associated with adopting the technology.
The most important issues for IoT adoption include the need for high-capacity communication networks, the security implications of using a large number of smart devices, and
data integration issues in the IoT. The emergence of technologies such as 5G communication may solve the problem of high-bandwidth communication requirements, which will enable large-capacity data transmission on the Internet of Things network.
Similarly, the use of technologies such as blockchain may help ensure the security of IoT networks. However, the challenges posed by the
data integration requirements of the Internet of Things have not yet been clearly resolved.
Since the advent of the Internet of Things, one of its main attractions has been the ability to provide remote control and visibility into enterprise business processes. The Internet of Things is also seen as facilitating the end-to-end integration of various business units and processes, which can improve coordination between these entities and thereby improve business performance.
However, business and technology leaders are increasingly emphasizing the fact that the true value of the Internet of Things lies in the data it generates. In order to use the data for any practical purpose, it is important to organize data from different sources. As we all know, organizing data or data integration in the Internet of Things may be more challenging than early big data analysis.
You may ask, why do you want to organize or integrate IoT data? Simply put, data integration is very necessary for the enterprise and the main decision makers of the enterprise. In this way, you can fully understand what is happening in the entire organization and its environment.
Different parts of the enterprise—through IoT sensors and other data collection devices—continue to collect valuable business data in different forms and on an unprecedented scale. These data can be used for decision making, which in turn drives daily operations.
In short, the collected data can be used to enable companies to better respond to emergencies. In addition, by integrating all the information collected by the sensor device network into a single body of information, companies can have a general understanding of the entire organization and its performance in terms of established long-term goals.
Having such insights allows business leaders to formulate business strategies more accurately and effectively. This analysis also helps to set more realistic long-term goals and helps to proactively solve looming problems. Integrating IoT data can also facilitate traceability activities such as reporting and auditing.
Explore IoT data integration challenges
The utility of IoT data integration mainly comes from the massive data collected, the diversity of the data, and the accuracy of the collected data. However, the ever-increasing number of connected devices makes it difficult for companies to track all the data flowing in from different directions.
In addition, all the data collected from the endpoint will also bring a lot of impurities, duplicate information and other types of problems, making the data difficult to use. Therefore, before compiling all the data into a single repository (such as a data warehouse), it is critical to clean up the data and make it available. This increases the need to invest in specialized tools, processes, and personnel to perform data cleansing and structuring.
In addition, the diversity of IoT equipment suppliers and the potential incompatibility of their products with other suppliers are also worrying. Because a large number and a variety of terminal devices are required to complete the Internet of Things network, enterprises will eventually use equipment produced by different vendors, which leads to incompatibility and security issues.
Therefore, standardizing the data collected by different devices has also become a challenge. Since different sensor devices can perform different calibrations, the accuracy of the results collected by different sensors (manufacturers) will be different. This may compromise the reliability of data analysis.
To overcome these and other challenges of data integration in the Internet of Things, companies must proactively identify them and their impact. Then, they should design solutions by using multiple methods, namely through the right strategies, practices, and techniques.
By combining new strategies, practices and tools, companies can solve data integration problems in the Internet of Things. They should design a scalable IoT integration system and embed it in their IoT adoption plan. Here are several ways companies can overcome the challenges of IoT data integration:
▲Have a clear IoT data integration strategy
As mentioned earlier, companies should develop strategies for IoT data integration before embarking on their IoT journey. For this, they must understand the scope, impact, anticipated challenges and opportunities, and potential solutions of their IoT projects. Anticipating challenges in the early stages and preparing for data integration challenges will prevent the creation of data silos that may lead to missed opportunities for numbers in the future.
Setting goals and consistent strategies for IoT projects will enable companies to integrate data into the core IoT architecture. A data integration strategy helps to formulate consistent steps, such as the development of communication requirements, that is, the communication mode required between different devices and components of an IoT network. It also helps to determine the need for edge computing in different parts of the IoT environment. This ensures that the central data warehouse will not be overwhelmed by unnecessary data that has little or no strategic utility.
▲Using API for IoT communication
The Internet of Things network is mainly composed of smart devices that need to communicate with each other. Using an application programming interface (API) is the simplest but most effective solution for device-to-device communication and the integration of the entire network and data. When data is transmitted from the edge to the data center, the use of application programming interfaces and other widely compatible middleware can help identify any differences in data quality. Enterprises should use API as the main tool for IoT data integration.
▲Using an integrated platform
Enterprises can use the cloud platform to unify the operation of the entire network on one platform. There are many platform as a service (PaaS) vendors that provide solutions for large-scale IoT implementations. Companies can also benefit from the emerging service segment of the integration platform, a service that specializes in large-scale integration. Companies seeking to use IoT data should not ignore the importance of data integration in the IoT.
This is because although the automation, visibility, and control provided by the Internet of Things technology can undoubtedly improve business operations in the short term, real gaining a competitive advantage means using the data sets generated by the Internet of Things to create real influence.
