Page 1 of 2 next >>

Getting Stuck in the Maturity Curve of IoT

Do you think of your IoT projects in maturity curves? If yes, I applaud you for it. Thinking in maturity curves helps organizations to reduce complexity and to navigate through the unknown. It is a natural behavior and a sound strategy; it is the answer to how to eat an elephant.

Crafting maturity curves for an industry or a technology is normally done by analysts based on insights they derive from speaking to various leading companies in a domain over a longer period of time. Another important observation is that maturity curves are like gravity. If you don’t follow it, you might face, in the end, what I call the “three little piggies” challenge. Before I dive into this analogy of the “three little piggies,” I want to talk a little more about maturity curves.

Maturity curves normally have three to four stages. (I love the ones within the power of three to keep things simple.) Maturity curves are powerful. In my conversations with prospects and customers, I am always trying to map the initiatives they are taking on a maturity curve in my head, as it provides a good sign to which challenges they might currently be facing and what they will be up against next.

A maturity curve I am closely involved in right now is the maturity curve of companies that make smart, connected machines. That curve currently looks as follows: The base includes remote monitoring and gaining insights into equipment use and performance. This is the first stepping stone for equipment manufacturers that want to become smart. Now, the manufacturer field services teams can gain on-demand remote access to reduce field service costs. Next, new insights can be delivered as a report, or on an app, to the companies using the equipment, which allows them to become smart operators. The main benefit?

Increase of uptime and, as such, a nudge in performance. Once this is achieved, the real work starts, and performance management can be tackled seriously.

A popular solution in this respect is the Overall Equipment Effectiveness (OEE) that allows the improvement of equipment performance in earnest and is seen as the pinnacle in Industry 4.0. Once these milestones are reached, it becomes exciting, as disruptive business models are now within reach, such as implementing new business models as equipment as a service, or EaaS.

The challenge with new business models is that they impact the organization beyond technology but also challenge the way you organize finance and sales; but that is a topic for another article.

So far so good, but what about the pitfall? What about the “three little piggies” problem?

If you travel a lot, then you know that there are risks with any journey. A primary risk is that you can get stuck, and this risk is a serious one in your IoT journey. Why? Well, from experience, the issue with IoT is that many companies take an unstructured approach. Think about the Three Little Piggies, a fable about three pigs who build three houses made of different materials. A Big Bad Wolf blows down the first two pigs’ houses because they didn’t seriously prepare and instead used whatever materials that would allow them to build their houses quickly, straw and sticks, respectively. The wolf, however, is unable to destroy the third pig’s house that was made of bricks because the third pig took the time to strategically plan and design his house, thus ensuring that the structure was sound.

In IoT, I see many companies who have taken an approach to their initiatives that is similar to the three little piggies “straw and sticks” analogy. Four to five years ago, IoT was hot and sexy and very much a developer gig. At the same time, businesses were weary of large implementation projects, as many failed miserably years before with big ERP implementations. So, agile had become all the fashion; “Learn fast or fail fast” was the motto. Only small funds were released to teams who wanted to innovate. Consequently, most of the developers turned to technology that was either freely available or known to them. Their options were either to open up the toolboxes available from cloud providers such as AWS and Microsoft Azure without exploring the market more deeply, or try to build everything themselves from scratch.

The focus of most of those teams was on connectivity: Can we connect and stream some data into a database? Can we add some free reporting tools on top? And, voila! Most of these initial projects proved to be so successful that many of the companies started to plan rollout based on these architectures and technologies. It reminded me of the saying from the 90s: “Hey it compiles, let’s ship it.”

Page 1 of 2 next >>


Subscribe to Big Data Quarterly E-Edition