With the advent of cheap sensors, ubiquitous connectivity and large volumes of data, the Internet of Things is poised to change the world. We’ve all heard the estimates of billions of dollars and objects that come into play in 2050. However, this is only the visible part of the iceberg. It will be essential to solve the problems associated with “objects” to release the true potential of the IoT, and this largely is to overcome the difficulties related to data.
Whether for a connected home, a portable terminal or an industrial solution, there is often a lag from new data collection to meaningful information that can be explored in detail. This becomes a main barrier to improve IoT productivity and performance.
There are three key elements to overcome these obstacles and tame the IoT.
In terms of data analysis, each question we ask ourselves with the data requires its own graphical presentation and visual perspective. This is especially true for the explosion of data from the sensors that form the basis of IoT. Unfortunately, most of the IoT applications are provided with standardized data views. They meet a set of predetermined questions, deemed worthy of a response by a small group of experts.
To fully exploit the potential of the IoT, tools should be more flexible and should allow users to tailor and adapt the data in different ways, depending on their needs or those of their organization. Ideally, users will be able to have in-depth conversations with their data to uncover all sorts of changes, or even reveal unknown trends.
For example, you may have an IoT application that analyzes the historical data of the activity of an engine, a gas turbine or engine down, and determines the conditions that cause malfunctions and the frequency with which they are likely to occur. But how to know which parts are most vulnerable? What plants have made them? And what is the date of manufacture? Or which suppliers have caused the most problems? The interactivity and the ability to share information is critical to find the answers to these questions.
The challenge in the IoT is not primarily in data storage, but in data management, analysis and integration. You need to merge disparate data to provide actionable information for your business. For example, the sensors integrated into the trucks can help a logistic company determine when a maintenance operation is required. This would avoid possible failures and save thousands or millions of dollars from your pocket.
In fact, the emergence of connected objects does not change anything, because no matter how many of these new devices involved in the systems, the data must be analyzed holistically, whether from a refrigerator, a TV, ca car or a mobile application. The real challenge is all these data must be processed and integrated in real time. Therefore, an integrated database solution always plays a central role in IoT.
It is essential for companies to improve their data infrastructure by choosing one that has the technology to integrate disparate data sources. This goes from traditional relational database to semi-structured data such as xml and JSON. Faced with this upsurge of information, companies will face significant costs for data solutions. The open source database management software could be advantageous, in that they are less expensive, and allow companies to make real savings in cost management in order to allocate these additional budgets in other departments.
We live in a world where it is increasingly unrealistic to have perfect data. Your data, organized as they are, may be stored in a source that you do not have access. They may also not include some key elements that are needed to answer your questions, or be formatted so that their depth analysis becomes complex. The IoT applications suffer from the same drawbacks, especially when there is no consensus on standards and protocols for the management of device interoperability.
However, rather than leave incomplete data, we must use what we have and proceed iteratively to find the right solutions. As the iterations, you may first learn to distinguish acceptable data with the poor quality data. Identifying acceptable data will make you realize what data is generally sufficient to meet your needs to answer your business questions. Also, understand the shortcomings of certain data can help you improve the data collection methods, troubleshoot the data collection and integration processes, and ultimately, help us achieve better IoT solutions.