In our earlier post about the four pillars of ADMS readiness, we highlighted the critical areas every utility should evaluate before embarking on a system-wide transformation. Data, processes, systems, and people all play a vital role in shaping your platform’s success.
This article, ‘10 Best Practices,’ dives deeper into the first and arguably most foundational pillar for successful ADMS preparation: data readiness. Leveraging experience from transformational projects, here are ten best practices (BPs) for preparing and refining your utility data for ADMS implementation and enterprise integrations.
BP #1: Level set your data.
Before diving into integration or configuration, take time to assess what data your ADMS will require and how that data is currently structured. Most utilities are dealing with a patchwork of systems that do not historically need to interact, such as GIS, supervisory control and data acquisition (SCADA), customer data, advanced metering infrastructure (AMI), and others. That fragmentation creates blind spots that can derail progress.
With ADMS, these previously siloed systems must now interact in real time. That means consistent identifiers, synchronized updates, and predictable behaviors when data crosses system boundaries. Fields that were once used locally may now become inputs for automated decision-making across your business. Alignment isn’t optional.
BP #2: Understand standards.
As you prepare for ADMS, expect to adjust your existing data to meet new modeling and integration standards. Field types, naming conventions, and classification schemes may need to shift. Not everything in your legacy systems will need to change; you may choose to mitigate these standards by getting creative with your data mapping, or you may need middleware to transform and route data appropriately.
BP #3: Run data exercises.
Many vendors offer validation tools or readiness checkers to help you assess your data posture. Work with your vendor or system integrator to run high-level mapping exercises early. Running these exercises now can help ensure you don't encounter surprises later. If changes are needed, they can be managed as small changes separate from your project or included in your project scope.
If you are still selecting a vendor, consider providing real data for demos. It is one of the quickest ways to uncover vendor compatibility issues or performance concerns before contracts are signed. Be sure to ask how the system handles incomplete data, what cleanup tools are available, and how early data alignment is supported.
BP #4: Align GIS and SCADA.
It is easy to get overwhelmed trying to clean and validate every dataset. The key is to prioritize what is foundational to your ADMS. For most implementations, that means starting with GIS and SCADA. Successful ADMS operations rely on SCADA’s real-time data paired with the structural accuracy of a solid GIS model. Together, along with other essential systems, they form the foundation for analysis and system control.
GIS defines the physical structure of your grid: feeders, switches, substations, and connectivity. If this model is outdated, misaligned, or built on legacy workarounds, core ADMS functions like load flow, switching logic, and fault location will not operate reliably. If your GIS is outdated or misaligned, prioritize updating it first before considering SCADA. SCADA should reflect the physical grid model defined in GIS. A strong foundation reduces rework and ensures SCADA signals are mapped appropriately. Conduct a data health check or run a lightweight assessment to catch any attribute or connectivity issues with your GIS data early.
SCADA delivers real-time device status and telemetry. But if naming conventions vary, phase information is inconsistent, or topology does not match the GIS, the signals get scrambled. Bad signals lead to bad decisions. Operators and engineers need to trust that what they see reflects reality. Trust in the system starts with trust in the data. SCADA doesn’t just consume GIS data, it complements it. Both systems need to be aligned. While SCADA reflects live status, how that status is interpreted depends on how well it matches the GIS topology.
Bringing these systems into alignment requires more than just technical integration. Aligning now will prevent a larger and more complex effort down the road. Focus your data cleanup on key areas that often create friction between the two systems:
Device naming and type classification
Unique identifiers used across systems
Voltage level and phasing consistency
Switch states and default operating conditions
Connectivity and topology agreement between GIS and SCADA
BP #5: Define data ownership.
As you refine the data, clarify who owns what. ADMS is a shared ecosystem. Information Technology (IT), Operational Technology (OT), GIS teams, Customer Operations, Asset Management, Protection & Control Automation, and Engineering all contribute to the data environment and use the data in different ways. An ADMS needs more than just GIS information. To power the high-value ADMS applications, all the different systems must work together and be in sync.
Begin cross-functional collaboration early. Catalog your key data sources, flag duplicated reports, and identify inconsistencies in how core assets, like feeders or switches, are defined. Data should not mean one thing to GIS and something entirely different to SCADA.
BP #6: Break down symbology silos.
Presentation and usability also matter. What shows up when a user clicks on a device? What symbols or shading are used to represent asset types or status?
Systems often evolve under different departments with their own visual conventions. If you do not align them, you are inviting confusion. Get input from the different teams that edit and view the data and decide on a standard format going forward.
BP #7: Form a data governance group.
This is where data governance becomes critical. Data governance needs to address the foundational business systems to facilitate the data integrity required by ADMS. Maintaining ‘zero data latency’ ensures all changes made to the distribution grid are included in the ADMS’s system optimizations. This maintenance introduces two sets of data governance in the systems of record: one during the design phase and another during the as-builting phase.
Establish a governance group early. Include representation from GIS, SCADA/Control, IT, OT, Engineering, and Customer Operations. Governance should begin early and continue after go-live to evolve standards and address new needs as the system matures. This team defines shared standards and prevents rework later in the project. Focus their effort on a few core decisions:
What data is considered authoritative for each source system?
How should naming, labeling, and visualization be standardized?
Who decides when data conflicts arise across departments?
What conventions will apply across all user groups and applications?
How do decisions align with industry standards currently in place, in progress, or planned within your organization?
Good governance ensures your data serves the needs of the entire organization, not just a single department or legacy system.
BP #8: Prioritize Performance and Plan to Iterate.
Perfect data isn’t required to begin, but your ADMS depends on accurate, high-performing inputs where it matters most. Focus first on the data that powers core functions like load flow, switching logic, and fault location. If that data isn’t trustworthy, the system won’t be either.
Your first import won’t be your last. Even with strong planning, unexpected lessons will emerge along the way. Treat early data loads as a baseline, not a finished product. Build in checkpoints and expect to make adjustments.
ADMS performance improves through iteration. Prioritize what enables validation and operational use today and be ready to refine as the system matures.
BP #9: Skim off the data fat.
Remember that not everything needs to come with you. Your existing data has evolved over time. It reflects years of decisions, shifting priorities, and constraints that shaped how your systems were built and used. Your new system will evolve too, and that perspective matters. Don’t approach this transformation with the mindset of what was is what needs to be. This gives you the opportunity to be more intentional about what you bring forward and how you plan for what comes next.
Avoid the urge to migrate every field, report, and structure from your legacy systems. Focus instead on what is clean, useful, compatible, and relevant. Determine whether your data customizations have a place in your ADMS. Even the most advanced platform will struggle under the weight of unnecessary noise. A digital grid can’t thrive on shaky data.
To evaluate what you need and don’t need, review your data customizations for accuracy, conduct audits, and determine which data is critical to your business processes and which data is just nice to have. Also determine whether your existing reports, key performance indicators, and metrics have a place in your new system. Doing this now will provide better operational efficiencies in your future ADMS.
BP #10: Plan for sustainability.
Clean data at go-live is only part of the story. Make sure your organization is prepared to maintain data quality long after implementation. Again, this will help your utility continually achieve zero data latency and, in turn, maximize the benefits of your ADMS.
Assign long-term data stewards, review and update workflows, and build habits that treat data as a living part of operations, not a one-off project deliverable.
Laying the Data Foundation
ADMS thrives on real-time, interoperable, and trustworthy information. When data is clean, structured, and scoped correctly, you are providing your ADMS with a solid base to build on. Perfect data is not needed to move ahead, but a focused, realistic approach is for a smooth implementation. Without alignment, miscommunications and contradictions can arise, causing slowdowns and potential mistrust in the systems being used.
Migrating to an ADMS is not just a technology upgrade. It is a fundamental shift in how your utility sees, controls, and manages the grid. At the heart of that shift is data.
Contact UDC for more information about ADMS preparation and read about our ADMS Readiness and Consulting services.