A utility provider does not run a single plant; it runs hundreds of kilometres of network scattered across the field - pipes, cables, pumping stations, substations and nodes that age at different rates and fail at the least convenient moment. As long as those assets live in spreadsheets, PDF maps and the heads of experienced staff, every outage starts with the question “what do we actually have out there?”. Digital asset management (EAM) turns that question into a ready answer.
In our experience the hard part is not the system itself but wiring it into the reality of the field: into SCADA, into GIS, into crews working in gloves and rain. We do not have a ready utilities case study and we will not pretend otherwise. What we do have is what decides these projects: engineers who have built industrial systems, automation integrations and apps for networked devices - and whom we place as an extension of your team or as a dedicated team.
Why do utilities need digital network asset management?
Because the scale and age of the infrastructure have outgrown the tools people use to keep track of it. A spreadsheet will not warn you that a given main has already failed three times this year, and a manual work-order flow loses the service history at exactly the moment you need it most.
Digital asset management brings this into one place and gives the technical decision-maker three things at once: visibility into the state of the network, control over maintenance costs, and a data-backed basis for investment decisions - the difference between firefighting and deliberately stewarding assets meant to serve for decades more.
What is an asset register and how do you manage the asset lifecycle?
The foundation of any EAM is the asset register - an ordered record of everything you manage. We design it to mirror the real hierarchy of the network: from the main, through sections and nodes, down to a single valve or meter. Every object gets an identity, a location, technical parameters and its own history.
On top of that register we lay the asset lifecycle - from commissioning, through operation and successive repairs, to replacement and decommissioning. That way you see not only what you own but what condition it is in. In the data layer we are strict about a few things:
- consistent numbering and classification, so the same valve does not exist in three systems under three names,
- relationships between assets (what feeds what, what depends on what), because a failure is rarely local,
- event and cost history pinned to a specific object, not to a generic bucket.
How do you plan preventive and predictive network maintenance?
A solid register opens the door to planning that gets ahead of the failure. Preventive maintenance runs on schedules and run-times: the system generates inspection orders on its own, tracks deadlines and will not let a legally required check slip.
Predictive maintenance reacts to the real state of the equipment rather than the calendar. We build it on sensor data and failure history: rising pump vibration, a pressure drop on a section or an unusual energy draw become a signal to schedule an intervention before something breaks. We wire these rules into the work-order module, so a detected symptom turns straight into a task for a crew, with parts and a date.
How do you manage outages and service interruptions (OMS)?
This is where the system proves its worth. An Outage Management System (OMS) takes a report - from a sensor, the call centre or crews in the field - and carries it through to restoration. We design that flow so the dispatcher sees, in one view, the location of the fault, its likely extent and the affected customers.
The OMS logic groups reports into a single event, suggests the probable fault location from network topology, and supports switching decisions that narrow the interruption. We also automate what should happen by itself: escalations, customer notifications and reporting of restoration times - the indicators a utility is held to by its regulator.
How do you map the network and infrastructure in GIS?
You cannot truly understand a network asset without a map, which is why the GIS layer is not an add-on here but a foundation. We join the asset register with a geographic information system so that every object has its place in the field and the dispatcher and the crew look at the same reality. On the map, the fault, the valve to close and the nearest crew stop being three separate pieces of information.
It also lets you analyse the network spatially: trace isolation zones, plan service routes, or overlay failure data onto the age and material of each section - and sensible decisions about the order of replacements grow out of that kind of map.
How do you integrate EAM with SCADA, IoT and telemetry?
An asset management system does not live in a vacuum. Its value grows when it sees the network in real time, and that means integrating with the automation and metering layer - where experience we genuinely have comes in: industrial and Industry 4.0 projects, embedded work and apps for networked devices.
- From SCADA we pull states and alarms, so an event on an object lands straight in the register and in the OMS.
- From telemetry and IoT sensors we collect pressure, flow, temperature or draw readings that feed predictive maintenance.
- With billing and metering systems we link customer data, so an interruption immediately knows whom it affects.
We build these integrations to be resilient: with data buffering, quality checks on readings and a clear line between what controls the network and what describes it. We wrote about joining devices and cloud in the same way in our piece on apps for connected products and IoT.
Where does an AI layer for failure prediction fit in?
Once the network data is tidy and flowing regularly, there is room for an AI layer. Predictive models can score the failure risk of a section from its age, material, history and operating conditions, and time-series analysis catches an anomaly in telemetry earlier than a fixed alarm threshold. This is decision support, not magic: AI suggests where to look first, while a human still owns the call.
Our AI and Data competence is real - within the RITS Group it is developed by our AI/LLM/data teams, and we have delivered GenAI, RAG and LLM projects. We will not pretend, though, that we have a prediction rollout on a water network behind us. We treat this layer as an optional, later stage that only makes sense once the data foundation already stands.
Which skills do we bring to an EAM project?
We shape the team around the actual work, not “just in case”. On network asset management projects we typically fill the roles that let a team take real ownership of the solution:
- an architect and backend developers building the core, the data model and the APIs,
- integration specialists wiring EAM to SCADA, GIS and telemetry,
- data and AI engineers when prediction and analytics come into play,
- DevOps owning deployments, cloud and reliability.
We verify every skill before the start and scale the team to the rhythm of the project. As a nearshore partner from Poland we work in a close time zone, so our people join your code reviews and way of working without a long ramp-up.
How do we do it at RITS?
We do it in a model that does not force you to build an IT department from scratch. When you have your own team and it is short of hands or a specific specialisation, we reinforce it with our engineers. When you need the whole thing turnkey, we assemble a dedicated team that takes on development and maintenance. For a brand-new system, a custom software project with a clear scope works well. We bring experience from industrial projects and automation integrations - the skills a good EAM rests on. We laid out the differences between these models in our article on IT engagement models.
Where do you start with network asset management?
Start from the most painful piece, not from one giant system at once - usually tidying up the asset register or getting the outage flow under control. Pick one, build it properly, connect it to the data you already have, and only then add the next layers: GIS, telemetry and, last, prediction. Step by step like this delivers value fast and does not freeze your budget for years.
Getting your network assets in order, or fighting outages with ageing tools? Let’s talk - we’ll match a team and an architecture to your network.