By Tom Paquin

This is part of an ongoing series on the state and standards of service management software. Here are the previous articles in the series:

• What is Service Management Software?
• The Key Capabilities of Service Management Software
• The Attributes of Best-in-Class Service Delivery Software
• The Operational Capabilities of Service
• The Building Blocks of Customer Experience Excellence
• Implementing New Service Software
• The Politics and Potential of Changing Service Software Providers
• What is Servitization?
• What is Outcomes-Based Service?
• Making Service a Competitive Differentiator
• Onboarding New Software Systems

For businesses working with large, complex assets, whether it be in manufacturing, utilities, telecommunications, or simply service operations, there’s a growing necessity to develop a sound strategy for how assets impact your service workflow. As we’ve discussed previously (and like any major business initiative), doing so is not as simple as switching on a lightbulb. There’s naturally a timeline of events that need to occur in a logical sequence in order to initiate or overhaul an asset-centric service practice.

This all starts with IoT.

It Starts with a Good Data Stream

I’ve said this before, but bad data begets bad data. That is why it’s important, within any data model, to have a strong process in place to collect, validate, and process data. Within the context of asset-centric service, this often means that we’re talking about IoT. As with anything that we talk about here, there are plenty of resources, use cases, and best practices that you can call on to support what that looks like in a variety of different contexts.

IoT, largely, functions as a vector through which you analyze things like work history, which will benchmark an asset’s condition against the needs for service previously in order to predict service needs. A frequently overlooked way that IoT can be used is considering an asset within the broader context of your organization, where it organizationally fits within an ecosystem that may not, in some cases, be under your direct control. There’s also, of course, the direct monitoring of data, which can be used to assess asset condition during remote repairs, or simply to identify outages, and understand where, downstream, those outages are happening.

This is, of course, the first step. Next is to consider the software you’re using to manage your assets, and what it’s offering your business. That’s what we’ll discuss next time.