.png)
Your utility has likely been collecting information for decades.
Your SCADA systems capture operational conditions, GIS maps assets, work management systems record maintenance history, financial platforms track costs, and SOPs and manuals document how work should be done.
Your utility may already be data rich, yet still lack the information needed for a particular decision. The challenge is to identify what is missing, access what exists across disconnected systems, and turn that information into insight that supports your daily decisions.
In this blog, we’ll cover the steps you can take to move from simply collecting data to using it effectively in your utility.
Start with a utility outcome
It’s easy to begin a data initiative with the system that’s easiest to access, or the department most ready to participate. But that can create activity without advancing a meaningful priority for your utility.
Instead, begin with the end in mind: What does your utility need to improve to protect public health, meet regulatory requirements, maintain reliable service, or manage costs for customers? You might focus on preparing for workforce transitions, reducing unplanned asset failures, strengthening cybersecurity, improving maintenance decisions, or giving operators faster access to trusted procedures. Connect the use case to the outcome it supports, then work backward to determine which data, systems, and people need to be involved.
This goal-first framing can also guide your decisions about collection and storage, within applicable monitoring and record-retention requirements. Assess whether you have enough information, whether you are collecting more than you need beyond those requirements, and how often the data must be updated for the intended decision. For discretionary records, weigh long-term storage and management costs against their value, while following your utility’s approved retention policies.
Identify one operational or business challenge your utility wants to improve in the next 12 months, then determine what information would be needed to support better decisions around it.
Find out what teams can actually access
Your data may exist without being readily usable. You may find that GIS information is relatively easy to access, while data from financial systems, SCADA, and other operational technology (OT) is much harder to connect. Valuable information may remain scattered throughout your utility in both digital and paper-based formats.
Map where relevant information lives, who owns it, who can access it, and how difficult it is to combine with other sources. Include security in that assessment, especially for SCADA and other operational technology (OT) data. Work with your operations, IT, and cybersecurity teams to protect control networks through appropriate segmentation and approve how data will be transferred and accessed. A replicated copy in a separate, secured environment may support reporting, analytics, or AI, but copying the data is not a substitute for reviewing the security of the connection and the destination.
Choose one priority initiative and trace where the supporting information resides today, who owns it, and what barriers exist to accessing it across teams.
Evaluate data quality in the context of the decision
Rather than asking whether your data is perfect, ask whether it is reliable enough to support the decision at hand. The right level of accuracy, frequency, and completeness depends on the decision you’re trying to support.
For example, you may set different requirements for a long-term capital planning model than for a tool supporting a field response to a pipe break. Apply the same principle to your documents. An AI assistant may help locate information in a standard operating procedure (SOP), but users still need to verify the answer against the current, approved source before acting. If the procedure is outdated, correct the source and strengthen your document-management process rather than expecting the technology to solve the issue on its own.
Pick one upcoming decision and ask whether the information available today is accurate, complete, and current enough to support it. If not, focus your improvement efforts on the gaps that matter most to that decision.
Choose one useful problem and prove the value
You don’t need to connect every system or perfect every dataset before making progress. Choose a focused use case that can create value while revealing what your broader data environment needs.
Before the test begins, agree on how you will judge success and record the starting point. For a procedure-search pilot, for example, you could compare the time needed to find the correct, approved procedure and check whether users can verify its source. Weigh the improvement against implementation costs and staff time, then decide whether to expand, revise, or stop the effort.
Experiment in a safe environment, separate from live control systems, then validate the results and improve the solution through feedback. You might begin by making maintenance procedures easier to find, comparing operational datasets such as SCADA run-time data against work order history, or identifying patterns in asset history. Select a first use case that addresses a recognizable pain point, involves the people who will use the result, and produces an outcome you can evaluate.
Keep human review central. Ask operators and other subject-matter experts to check the data, verify the source documents, and assess the usefulness of the output before it informs operational decisions. Agree on who can approve its use, how users will report errors, and how corrections will be checked before release. Use that feedback to make deliberate improvements to the data, documents, or tool and to build trust over time.
Look for a recurring pain point that affects operators, engineers, or maintenance staff and test whether better access to information could improve the outcome.
Move from data, to insight, to action
Plan for what happens after the pilot, too. Name who will maintain the data and source documents, who will support users, and who will approve changes. Account for training, recurring costs, and the staff time needed to keep the solution useful. Keep the scope manageable for the people who will own it.
You don’t need to solve your entire data landscape at once to become insight-ready. Start with a clear outcome, take an honest look at access and quality, and choose one use case that matters to the people running your utility.
Trinnex can help you identify the right starting point for your water or wastewater utility and bring together the services you need to move forward. Explore Trinnex Data & AI Consulting to see how you can apply a practical, value-driven approach and turn your existing information into better decisions.
