Service is the Core

IoTs slogans have already been shouted out for a long time, and there is a deep understanding that big data analysis will bring about a new wave of industrial revolution. However, many enterprises only do big data for its own sake, and this has caused a number of problems. The technologies are stiff, while the applications are varied. The important point is what kind of services are your big data analyses capable of providing? What kind of problems can be solved using big data?

PTC Senior Associate Wang Chen-zheng said, “Big data analysis makes use of more objective methods to predict the future. Speaking with data is both the most dazzling and the most difficult aspect.”

Its value cannot be measured by technology alone or the level of competitive technology. On the contrary, providing suitable, convenient, and easy to use services that are directed at customers' requirements is of key importance.

To differentiate between product applications and manufacturing, the goals for most factories are to meet demand by making manufacturing processes more accurate, increasing efficiency, and making them easier to use. However, the appropriate services intended for product applications are relatively difficult.

From the standpoint of the commercial sector, consumers' preferences often change, and finding ways to use big data analysis as models presents a certain degree of difficulty. Furthermore, under the changing conditions of consumers’ new demands, real-time data analysis will become extremely important.

Integration is the Key to IoT

IoT applications are very diverse, and behind each service, several types of technology are covered, such as communications and sensing. This also causes companies to frequently be faced with complex issues during the process of development, and big data analysis is in demand to integrate the various levels of information.

Figure 2: That PTC's ThingWorx IoT Platform Strategically Combines and Integrates Multiple Iot Technology IoTnologies and provides "All in One" Solutions. (SOURCE: PTC)

Wang Chen-zheng stated that IoT's winning point is not high and low technology, but instead integration is its biggest highlight. This not only includes “hardware and software integration” of key technologies, but also includes “heterogeneous information” in the domains of applications.

Wang Chen-zheng believes that the IoT world is frankly-speaking a very integrated world. In the Internet generation of the past, there was communication between applications, but today the emphasis is on communication between things. Consequently, finding ways of quickly transmitting messages between things via intermediate bridging platforms has become a major key.

Having been awarded as the leading IoT manufacturer, in terms of IoT technological integration, PTC provides “all in one” solutions. This method has the advantage of enabling to clients to avoid complicating processes when creating products.

Wang Cheng-zheng also further explained that PTC's ThingWorx IoT platform strategically combines and integrates multiple IoT technologies in a manner that is similar to building blocks. Each type of technology required for IoT is wrapped together level by level in order to save considerable time in comparison with the independent technologies of the past.

IoT's Other Major Challenge

The IoT generation has arrived and industrial domains have also begun to integrate different types of technologies for the start of a new industrial revolution. However, in actuality, big data analysis applications have obvious differences in their industrial and commercial applications. “Differentiation” is the biggest difference between the industrial domain and the commercial domain, and it is also the most difficult element.

Mastering Commonalities in Data Analysis for Each Profession

Striving for the development of industrial automation, ICP DAS stated that mastering the commonalities in big data analysis is an important key. ICP DAS General Manager Robert Chen pointed out that when companies are considering utilizing industrial IoT connected to industrial big data analysis, the optimal method is to seek out an application which is suitable for all the industries to serve as a starting point.

Figure 3 : ICP DAS General Manager Robert Chen believes that mastering the commonalities in big data analysis is an important key. （Source: ICP DAS）

As an example, ICP DAS provides a number of big data analyses for energy. They discovered that regardless of the type of industry, they all needed to be able to carry out effective management and control of energy. Because energy is about cost savings and is a fundamental component of business operations, in particular in the IoT generation which attaches extreme importance to energy conservation issues, finding means of increasing energy efficiency has become the most basic reason for big data analysis is every profession. This is also an important foundation of the IoT generation.

Decentralized Architecture Facilitates Accuracy of Data Transfer

According to statistics, in the year 2020, there will be over 500 billion devices installed that are digitally connected to one another. Among these devices, a large portion will be found in industrial IoT. Countless data collecting machinese with sensing and monitoring capabilities will be mutually connected and produce data. Through each kind of smart analysis, invaluable insights will be provided for industrial manufacturing, increasing effeciency and productivity. Furthermore, massive amounts of data will be derived from these processes and generate huge loads for database analysis.

In order to avoid this kind of situation, decentralized architecture will play a major role in this process and will be one of the core technologies of the future.

Robert Chen took a stop further in explaining that decentralized architecture enables each node to be equipped with a simple “brain,” and data will be collected by each subscribed mechanism and precisely delivered for each terminal requirement or cloud platform, and data will also be transferred when it is required for the backend.

This method will avoid unnecessary “garbage” in huge amounts of data and overloading of the back end. For industrial big data analysis, this structure will assist in the effective processing of data from billions of sensors.