The future possibilities of the Industrial Internet of Things (IIoT) are endless. The present realities, however, seem more challenging. As we stand on the verge of this new world—with unlimited information coming in from all types of devices—it’s time to take a reality check. What will it really take for the IIoT to come into everyday practice?
This question was recently posed to me at a roundtable discussion during the annual KeyBanc Capital Markets Industrial, Automotive and Transportation Conference. When the moderator asked me to pinpoint the factors preventing the adoption of the IIoT, I outlined three standout reasons why.
#1: A Catalog of Success Stories.
Effectively adopting the IIoT is not a plug and play endeavor. It often takes a comprehensive mental shift on how industrial manufacturing companies think about the way they do business. Everyone knows that change management has never been a cake walk, but this is especially true in an industry operating on razor thin margins.
Regardless, the competitive advantages the IIoT offers can’t be ignored. It opens the opportunity to dramatically improve processes, margins, productivity, and even client interaction (plus satisfaction). It basically gives companies the ability to play a whole new ball game—both operationally and financially. This is all incredibly hard to get your head around though, without real world examples. It’s up to analyst groups and solution providers like Toumetis to develop a catalog of success stories that cover a wide range of industry applications.
#2: Data Security.
When any given industrial object or machine becomes a data transmitter, how can you possibly secure all that information? As long as hackers and malicious forces are out there, this will always be a central question that’ll never be definitively answered.
For all the hype, security concerns are often used as an excuse against adoption with little evidence. Solutions for data security already exist for most IIoT applications, but making people more comfortable with the data encryption and end-to-end security modules that would provide the best protection is another story. To mitigate some of these concerns, the IIoT space needs to start developing universal best practices.
#3: No Data Scientist Required.
Getting a glut of data from any given object is one thing. Making it actually useful is another thing entirely. Once you collect all this information, how do you separate the signal from the noise in the IIoT? That is, without the help of a PhD data scientist?
Let’s face it, most companies don’t (and don’t want to) have data scientists on staff. An IIoT solution that forces them to indefinitely hire an expensive outside consultant will rarely see the light of day. However, by utilizing machine learning with a clear set of algorithms, actionable results and information can be leveraged by an industry expert from inside the company.
By creating a feedback loop that focuses on detecting and categorizing anomalies, IIoT solutions like Cascadence that are rooted in machine learning improve the reliability, approachability, and usefulness of the overall technology. An IIoT application rooted in machine learning only continues to improve and build upon what it’s learned, every day it’s in operation, without the need of an outside data scientist.