In my last post, I detailed seven things that lead to successful IoT projects. Use cases were first because they are a powerful tool that quickly inform an organization as to what management wants to achieve with an IoT project. They are a very effective way of modeling software systems; allowing organizations to build a mutual vision of the problem at hand, bridging the gap between subject matter experts (who understand the problem), and IT teams (who understand how to build a solution).
Still, a use case can go astray if management doesn’t take the time to understand the implications. Take the example of predictive maintenance. Many companies I have met considered this as the most important outcome of an IoT project.
At the end of 2016 I met with a customer who said: “We want to do predictive maintenance on our refinery installations.” I asked if they had historic data on their machines available. I asked if they had a data scientist that had already created some models that could be used to create a basic predictive maintenance project.
All answers were categorical “no’s” or “not yet.” Still, they insisted on having a predictive maintenance program in place within three months. Needless to say, there is still nothing in place.
Why? Two reasons. One, it is important to understand that use cases don’t live in isolation. Use cases need to be communicated to and adapted by the organization. The second reason is that use cases have to adhere to the maturity curve; the use case complexity has to be aligned with the maturity of the organization. If any use case is too advanced for the organization, the learning curve becomes too steep and adoption thus too expensive.
An easy way to think about this is to view your organization’s competency on a scale of three stages.
- The data-centric stage: This stage means that your organization can handle data volumes generated by devices at a large scale, along with connectivity and device management issues. It can store the data generated by those devices continuously, in a reliable way.
- The process-centric stage: Once the data stage is passed you might want to alter existing processes to take advantage of the insights generated by the data, or even create new processes.
- The analytics-centric stage. In this phase data scientists analyze the data and create new advanced analytic models like machine learning and AI to create predictions and actions that automatically optimize the decisions and processes.
If you thinking of going after a use case that requires advanced analytics then consider the fact that Rome wasn’t built in one day, so neither will your IoT infrastructure. Take the time for your organization to adopt the stages and start reaping benefits from day one.
My next post will discuss habit #2 – work in multi-disciplinary teams.
See you next time!