The Internet of Things (IoT) involves the sharing of data between similar and dissimilar equipment, systems, applications, and even industries. This creates a major challenge in terms of data interoperability because many IoT systems are built with different interfaces and use different technologies. For example: It is often impossible to push sensor data with the required level of precision and accuracy across systems that were not specifically designed to be compatible. In addition, various systems may not have ability to accept high volumes of data at any given time.
Facilitating Data Sharing Between Enterprises
Today, most data exchange in IOT takes place between enterprises and devices. Enterprises create APIs and web services that receive data from devices, either directly or through an intermediate cloud service. The data is then usually stored in a database, where it can be accessed by other systems within the enterprise (such as a Business Intelligence system or a reporting dashboard).
However, this approach to data sharing has some serious limitations. First, it precludes the possibility of sharing data with partners outside of the enterprise – typically because of security concerns. Second, it makes it very hard to share standard data formats with partners: each partner must create its own custom API or web services for receiving data from other partners. This can slow down interoperability among multiple partners in a single industry or ecosystem. Finally, enterprises are limited in their ability to choose the most suitable database for storing different kinds of information – since they are locked into using only one database across all their operations.
In order to overcome these issues, we’ll need new approaches to facilitating data interoperability between enterprises and IOT devices
Protecting Intellectual Property
One of the biggest challenges in the internet of things isn’t technological at all, it’s legal.
The world’s most valuable companies make money by monetizing data, and as new types of devices connected to the internet generate more data about people than ever before, companies are scrambling for a piece of that pie. This has led to a surge in patent applications related to the emerging market for connected devices.
The problem is that many of these patents are vague and overly broad, which is leading to a flood of litigation among companies seeking to protect their intellectual property (IP) rights. These disputes can be costly for all parties involved and slow down innovation in the IoT space.
Making Data Accessible for Business Use
Handling data from a variety of sources, including sensors, devices and applications, is a challenge for many IoT organizations. A critical element to success is the way in which data is packaged and presented. The more flexible the approach to data interoperability, the better positioned an organization will be to meet the needs of its internal customers – whether it’s a request from marketing, sales or engineering.
Increasing the Speed of Data Analysis
In the Internet of Things, data trickles in from a multitude of constantly changing sources. These sources may not be accessible for various reasons, including security and privacy concerns. A single source could be inaccessible because it is behind a firewall or because it is behind a VPN that is not always connected.
Existing approaches to data interoperability are either too restrictive or require too much manual intervention to be practical for most data consumers.
Today, software vendors are releasing new approaches for extracting and integrating data from many different sources. The following examples illustrate how these new approaches can increase the speed of data analysis.
Improving Interoperability Between Devices and IT Infrastructure
Data interoperability between devices and IT infrastructure is a critical aspect of IoT data management. The ability to access, manage, and process data from diverse sources has the potential to improve productivity and generate new insights, but only if enterprises can accomplish this task efficiently.
According to a recent survey by Gartner, over 75% of information and technology leaders are worried about the difficulty associated with data interoperability between IoT devices and enterprise systems.
Finding Opportunities for Operational Improvements
Portals that provide dashboards that collect data and display it in real time or near real time will be the foundation of this requirement. But these dashboards are not likely to be the end point for the data. They will likely be used in conjunction with other systems, such as ERP, WMS, and TMS systems, to help users make better decisions.
The IoT is creating a lot of new data, but there are also ways to use existing operational data in more effective ways. For example, many companies have fleets of vehicles that have on-board computers capable of tracking their location and many other parameters. If this information isn’t already being used to improve operational performance, it should be.
Detecting Anomalies that Indicate Manufacturing Problems
In a manufacturing environment, the goal is to make sure that the factory is running as efficiently as possible. The data from all of this machinery, if properly utilized, can be used to increase production levels and reduce quality issues.
One great way to achieve these two goals is with anomaly detection. Anomaly detection, which is also known by other names like outlier detection, helps find abnormal behavior in data points. For example, if one of the machines starts running at half speed when it usually runs at full speed, that could indicate an issue that needs fixing. If a machine starts producing parts that are slightly smaller than usual, that could also indicate a problem that needs fixing.
Looking at the 7 Approaches for Data interoperability for the Internet of Things, there are some common attributes. Data Interoperability is a key feature of IOT, supporting the innovation that organizations are interested in. IoT standards are being developed, creating potential for data interoperability between devices from different vendors. Expect to see more organizations looking at Data Interoperability in order to increase their business benefits from the Internet of Things.
