New Report Cuts Through the Hype of Digital Twins

Digital twins have received much attention over the past few years, but the subject suffers from the hype of overzealous marketers keen to badge their Internet of Things (IoT)-related products with a secret sauce that separates their solution from a crowded marketplace. Technology marketing rapidly becomes a zero-sum game. Now that the blockchain hype bubble has burst, marketers have turned to artificial intelligence (AI) for their latest patina for product differentiation. It is hard to find any software that isn’t promoted as “powered by AI,” and many IoT vendors will throw in “digital twin” for good measure. 

Over-hyping technology adversely affects customers, vendors, and the market. The disappointment of purchasing technology that fails to deliver on its promises can have a deep, negative impact on that technology’s market development.

Digital Twins Are Over-Hyped

Gartner’s 2018 hype cycle for emerging technologies places digital twins at the “Peak of Inflated Expectations” and estimates that digital twins are 5 to 10 years away from the “Plateau of Productivity”—otherwise known as operational technology. But, read any article (okay, most) that attempts to define a digital twin, and it will tell you that digital twins have been in operation for decades. And Gartner thinks so too:

“Gartner defines a digital twin as a software design pattern that represents a physical object with the objective of understanding the asset’s state, responding to changes, improving business operations and adding value.” In other words, a digital twin is anything that involves sensing equipment deployed on assets, with a bit of analytics thrown in. 

So how does old technology take 5 to 10 years to enter operations, given that almost every single industrial company in the world has an operational digital twin? I have no idea. I guess it makes someone a lot of money.

Digital Twin Thinking Is More Important Than Any Single Technology

My latest report dispels certain digital twin myths. Central to my argument is that digital twins are more an approach to data management than a single technology. It’s clear there is little consensus regarding what constitutes a digital twin, but that’s okay. At the most basic level, a digital twin can be a single datum regarding the health of one asset and that asset’s location. From this one data point, a digital twin can evolve to be a highly complex 4D model using cutting edge analytics on a rich dataset from across the enterprise.

There are six maturity dimensions discussed in the report, which can be developed at different speeds depending on each company’s specific needs:

• Hierarchy
  

• Richness of data
  

• Visualization
  

• Accessibility
  

• Data latency
  

• Intelligence 
  

A data management strategy is not a product and therefore not a cash generator, which may explain vendors’ reluctance to call digital twins what they are. However, it is important that users take a pragmatic approach to a digital twin strategy, because the approach is so important to digital transformation. Using a single source of truth for network assets lays the foundation for advanced analytics use cases that are not possible without in-depth visibility into the power grid. The market needs more circumspection and a lot less marketing noise. Keep a look out for Guidehouse Insights’ report, Digital Twins and AI Deliver the Energy System of Tomorrow, and a follow-up blog that will discuss how analytics and digital twin thinking combine to deliver value.