In Data We Trust?

Data analytics are critical to the concept of IIoT to find those hidden gems in the mountains of data that we diligently collect. But what if we don’t have trust in what the data indicates? Asset condition monitoring and management applications have been the initial IIoT target, but are the benefits being realized? Reliance on data may represent a cultural change in the standard operating procedure of an enterprise that requires leadership, change management, and plenty of patience.

In a recent Uptime magazine article, Burt Hurlock, CEO Azima DLI suppliers of condition monitoring solutions, compares the situation to the self-driving car. He asks, would you be the first to buy a self-driving car? That’s a definite NO for this blogger. Despite the availability of experience data available, Hurlock surmises the lag in adoption of asset condition monitoring systems is due to the human need to be in control. He’s not the only one writing on this topic. In a recent Information Management opinion piece, Eddie Amos, GM/VP APM Software at GE Digital, states that manufacturing companies are not realizing the productivity gains expected from the digital enterprise.  Amos further states that learning to trust the data is fundamental to achieving digital transformation.

For at least a decade, ARC clients have told us that they have enough data but were lacking the ability to analyze it and therefore to leverage it. A plethora of data analytics solutions are now available to help make sense of the data, but why do we not have confidence in it? Is lack of understanding of how the decision is derived to blame? Or perhaps it is human nature as Hurlock suggested. This analyst believes it is the type of cultural change that requires leadership as well as proper design and implementation, and an understanding on the part of all stakeholders of what is involved. It also takes time.

ARC believes that data will prevail in the end as systems are getting smarter all the time. Like climate change, the scientific data supports it and those that choose to ignore it do so at their own peril. Do you have confidence in your data? We’re interested in hearing from the end user community on this topic and invite you to comment with your thoughts.