Working Backward (Or: What IoT Can Learn From Steve Jobs)

The complexity in the domain of the Internet of Things (IoT) is staggering. Gaining greater insight into your business by learning how operations deep at the heart of the organization are really performing requires a level of analytical proficiency that (until recently) was only found in either very large or very specialized organizations. In order to notch the analytics up to the next level in your organization, you will need to double up on your efforts. All IoT use cases—and especially Industrial IoT—are doomed if the organization fails to get to grips with the increased complexity.

Take, for example, predictive maintenance. This is a use case that is on everyone’s mind, especially if you have any form of assets in your organization that support your primary processes. Ask yourself: How many different assets do you have? Do you have historic data available on those assets? Do you know the normal behavior of those assets? How will you solve the challenges of identifying abnormal behavior?

But before you get bogged down in the details, it is helpful to realize that there is a good approach to address those challenges. It is called “working backward,” an approach made famous by Steve Jobs in the context of CX. (Borrowing from businessman and author Stephen Covey, this is habit number four in my continuing series on the seven “habits” that successful IoT projects have in common.)

The idea behind it is simple. Just as when you were a kid and you figured out that it was easier to solve a maze by starting at the destination and then working your way backward, you will see that it is easier to solve these types of complex cases by tracking back from a desired outcome. If this sounds similar to good detective work, it is!

Let’s take the predictive maintenance case. The first question you must ask is: What outcome is it that any organization would normally like to achieve? This answer is probably already in your mind, but it never hurts to make sure all your colleagues are on the same page. The answer might be as simple as preventing a machine or asset from failing by detecting early signs of problems (and taking an appropriate action).

Now, the tougher questions need to be addressed. This shouldn’t be a challenge if you are working your way through our seven habits because you will have your multi-disciplinary team in place to come up with an answer to the question of what kind of analytical model could give you such an insight. The data scientist in your team might tell you to set up a model to calculate remaining lifetime. The data scientist probably asked you to join a meeting with maintenance guys, and together, you would have a conversation on how, for example, increased vibration, temperature, and energy consumption are good signs for the particular asset you have in mind.

“Fantastic,” you think, “are we there yet?” Well, not completely. You now want the engineers in your team to figure out how that behavior can be measured. The engineers with whom you discuss the devices that could measure vibration might conclude that vibration can be measured if they have sensors that are able to measure acceleration and velocity. Once you have gotten to this point, there is light at the end of the tunnel, as you now can connect the dots end-to-end. You are ready to align all topics on your journey back from the sensor to the original goal of being able to do predictive maintenance. Where acceleration and velocity sensors help you to collect vibration data, the data scientist can then analyze it and fit this data into a remaining life model. This will help you to validate the assumptions of the maintenance guys and allow you to assess potential early warning indicators that are expected in predictive maintenance. (For simplicity’s sake, I left out the fact that an enterprise architect might point out that connection to some administrative case systems might be necessary too, but—hey—he is on your team too, so no sweat!)

The good part of this practical approach is that it works in many situations—not only IoT—and many leaders have advocated it. So when you preach it to your team, you will be comfortable in saying, “Guys this is common knowledge, even Steve Jobs did it” (

For habit one, go here

For habits two and three, go here.


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