Asset Mapping Intelligence · Presentation Insight
The Unified Spatial Platform: Building a Map-Centric Asset Management Ecosystem
WEBINAR · AUGUST 20, 2026How local governments can turn GIS from a mapping department into the organization's single source of truth — connecting work orders, sensors, inspections and capital plans to every asset on the map.
Presented by AssetMapping.Events / ConnectMii Events
MODERATOR Tracy Quant, Government Campaign Manager, Asset Management Software | Siemens
PRESENTERS
- Michael Murphy, PMP — Technical Program & Product Management, Asset Management Software | Siemens
- Annie Anand — Business Process Manager, City of Fort Worth
OVERVIEW
This final session of the GIS at the Centre series set out to connect every thread from the previous three: the spatial data foundation, outdoor linear and network infrastructure, and GIS inside the building. The question was no longer whether GIS can support asset management, but what a fully unified, map-centric asset management ecosystem looks like when it is actually running.
The stated vision was direct. Any authorized user — a field technician or a finance director — clicks any asset, anywhere in the jurisdiction, and immediately sees location, condition, history, lifecycle stage, risk, criticality and estimated replacement date. From the map. In one click.
Michael Murphy framed that vision and the engine behind it. Annie Anand of the City of Fort Worth showed that it is not theoretical: Fort Worth has already tied its 311 CRM, work order system, asset information system and weather sensors together in GIS, and is using the result to plan a citywide bond program.
Both speakers arrived at the same conclusion:
The gap between GIS as a map and GIS as a platform is not a technology gap. It is a data strategy gap, an integration gap and an organizational positioning gap.
The second lesson was about sequence. Every advanced capability discussed — AI prioritization, predictive maintenance, digital twins, simulation — depends on a structured, governed, centralized foundation laid first.
AI can only be as intelligent as your data is. — ANNIE ANAND
KEY TAKEAWAYS
✓ GIS is a platform, not a department. When GIS sits at the edge of the org chart, it sits at the edge of every decision.
✓ The three gaps are strategy failures, not technology failures — and each carries a real dollar amount.
✓ A map answers "where is it?" A spatial intelligence platform answers what is happening, what will happen, and what to do.
✓ You build it, you don't buy it. Most agencies already own the pieces; they just aren't talking to one another.
✓ Three connection types keep records current: real-time feeds, scheduled sync and event-driven updates.
✓ Spatial intelligence compounds. Every work order, sensor reading and inspection makes the record more valuable each year.
✓ Structure before intelligence. Data governance and data strategy are the guardrails for database design; AI and predictive analytics fail on unstructured data.
✓ A pretty map with no data behind it has no ROI. Treat data as an asset that must return value on taxpayer investment.
✓ Keep a human in the loop. Fort Worth's AI policy requires human involvement in final decisions.
✓ Build in phases and just start. Foundation → integration and automation → full spatial intelligence. Small, incremental wins count.
PRACTICAL FIELD GUIDE
Making the case for a unified spatial platform?
Use the gap self-assessment, three-phase roadmap and governance checklist later in this Insight to structure the conversation with your leadership.
PRESENTATION ONE
From Map to Platform: The Vision and the Engine
Michael Murphy, PMP — Technical Program & Product Management, Asset Management Software | Siemens
Where the series has been
Murphy opened with a one-line recap of each prior session before tying them together:
| Session | The one-line lesson |
|---|---|
| 1 — Foundations | GIS is the connective tissue for asset management, not just a mapping tool. |
| 2 — Outdoor networks | Linear assets, network infrastructure, cascade failures and hazard exposure. |
| 3 — Indoor GIS | Emergency response, facility condition and indoor/outdoor unity. |
| 4 — The unified platform | A complete asset management implementation — not a pilot, and not a mapping upgrade. |
His stated measure of success for the session: if you leave still thinking of GIS as a mapping tool, the job has not been done.
He offered an analogy for the scope. The smartphone replaced the Walkman, the planner and the Rolodex, and nobody misses carrying them. GIS is a similar Swiss Army knife — and after 25 years of advocating for it, he argued it is not a shiny object that loses value in a few months.
The three gaps
Murphy restated the three failures the series has been building to close:
| Gap | What it looks like |
|---|---|
| Data strategy gap | GIS is visualization only, not the authoritative record. The same asset lives in four systems with four different, non-persistent IDs. |
| Integration gap | No real-time sync. Closed work orders never update back to GIS. IoT readings go nowhere. |
| Organizational positioning gap | GIS is a department, not a platform. At the edge of the org chart means at the edge of every decision. |
Two examples carried from earlier sessions made the cost concrete:
The 2 a.m. water main break. Crews spend 40 minutes to an hour hunting for and tracing the right paper map. With GIS at their fingertips, the same task takes about five minutes.
The retiring switch-flipper. An employee flipped a switch every morning for 30 years to keep a machine running and never told his replacement. On his first day of retirement, the machine went down. Murphy suspects this happens more often than anyone knows.
These are not technology failures by any stretch of the imagination. These are strategy failures. And every single one of them has a real dollar amount attached to it. — MICHAEL MURPHY
Annie Anand confirmed that the gaps were familiar from two decades across multiple municipalities, including repeated requests to "scan this person's brain" before a retirement.
His reframe on hearing that: recognizing these problems is good news. They are common, which means the case for fixing them is shared.
The vision: beyond the map
Any authorized user, any asset, one click: location, condition, history, lifecycle, risk, criticality, replacement date. That's the floor. That's not the ceiling. — MICHAEL MURPHY
The shift he described is from GIS as a "go look something up" tool to GIS as the system that is constantly watching, connecting and reasoning.
| A map answers | A spatial intelligence platform answers |
|---|---|
| Where is it? | What is happening? What will happen? What do we have to do? |
Critically, it is not a product you buy. It is a platform built on top of what an agency already has. In many cases every tool needed is already in place; the pieces simply are not connected.
The engine: live, connected, compounding
Three connection types make the platform function:
| Connection type | Example | What it achieves |
|---|---|---|
| Real-time feeds | IoT sensor pushes a pressure anomaly | Record auto-updates, risk is flagged, a work order can be triggered |
| Scheduled sync | CMMS and inspection data sync on a cadence | The record is current by design, not by memory |
| Event-driven updates | Valve operated, repair completed, condition changed | Near-real-time updating as field crews change the source data |
He singled out the word compounding. Every work order, sensor reading, inspection and capital project makes the asset record smarter. The platform is worth more every year it runs, not less. Advances in AI, he noted, create further room to automate.
He also acknowledged the practical reality: data is not static, and changing a schema is not easy — but it is possible, and it is part of the path.
Interdependencies at scale
Calling back to sessions two and three, Murphy described modeling interdependencies across the whole portfolio at jurisdiction scale:
- Spatial query for collateral impact. Identify every asset within 500 feet of a road project before work begins.
- Cross-department coordination. Catch the water main that should be replaced with the repaving, instead of digging up a freshly paved street two weeks later.
- See consequences before committing. Including the cost of not funding something — the territory of simulation, digital twins and 3D models, all possible with today's tools.
Every example has a dollar figure. That's your ammunition. That's your case to take to the decision maker, the CFO, the person signing the checks. — MICHAEL MURPHY
The Lego principle
Murphy described building the platform like Lego: lay the first "golden brick" — for him, usually data quality — and build on top of it one piece at a time. You do not have to build the whole set at once, and every new piece delivers more value to stakeholders.
Databases, not spreadsheets
A long-time mentor of Murphy's held a personal vendetta against spreadsheets and proved the point by building data pipelines and querying databases to deliver answers a spreadsheet simply could not. The immediate value proposition for decision-makers: data kept in a database is federated and available to every user.
We want to stop being a GIS department. We want it to become the organization's single source of truth. — MICHAEL MURPHY
Audit trails
Murphy noted he was building an audit trail for database changes that week. His example of the risk: someone moves a high-voltage power line 50 feet on the map and never documents it. The safety and planning consequences are serious, and a centralized platform with change tracking mitigates them.
Big results aren't always big things
He cautioned against overbuilding. No one needs to show up Monday and build a complicated GIS laboratory producing features nobody asked for. Centralizing data, getting it out of spreadsheets and federating it is itself a big result that people will recognize.
PRESENTATION TWO
Fort Worth in Practice: A Centralized Geodatabase at City Scale
Annie Anand — Business Process Manager, City of Fort Worth
Anand joined as the practitioner voice: someone who has taken the unified platform from idea to working system in a city of roughly one million people with 13 departments.
The power of one system
Her framing was simple: imagine everybody in the same system, with the same data, looking at it at the same time. No scrambling for spreadsheets. No chasing a file on someone's C drive. One authoritative, backed-up, accurate source of truth.
Then add geo to the database. Every asset, where it is, how much work has been done on it, how much has been spent or invested in it, and how old it is — with a click.
That's a system that we're dreaming about. And that dream is not far away. Fort Worth's already done it. — ANNIE ANAND
What Fort Worth has connected
| System | Role in the platform |
|---|---|
| CRM / 311 | Resident complaints and service requests flow in |
| Work order system | Work performed against each asset |
| Asset information system | The authoritative asset record |
| Weather sensors | Environmental conditions in near real time |
| GIS | The layer that ties all of the above together |
The payoff is for executives and decision-makers, who can depend on that combined system to make million-dollar decisions for a city of a million people. Her caveat: it is only easy if the foundation is built correctly.
Starting from industry standards
For GIS teams building the foundation, Anand recommended starting with Esri's industry data models — stormwater or whichever asset type applies. They provide a strong jump start on what the database should contain, and Fort Worth adopted industry standards for each of its asset types.
Fort Worth's data spans roughly 20 years. It did not happen overnight, and it is never finished; the city keeps upgrading to meet current needs. Twenty years ago, she was personally in the field mapping every inlet — inlet on the ground, inlet on the map. The work has since moved from locating assets to bringing location intelligence into decision-making.
The catalyst: Bond 2026
Asked by Murphy whether a single event convinced stakeholders to centralize, Anand pointed to the city's 2026 bond program as the latest example.
- You cannot plan in a vacuum. The pavement team needs to know what water is doing, what stormwater is doing, and what regional agencies like TxDOT are doing.
- Coordination extends beyond the city. Neighboring agencies, grant funding, project status and timelines all have to be understood together, because street reconstruction, storm drain repair and detention basin work take years.
- Clustering investment requires centralized data. Targeting investment in specific areas — and calling the result a coherent investment program — is impossible until water, stormwater and pavement data live in one place.
Governance and strategy are the guardrails
Anand stressed that structured data does not happen by magic. It requires both a data governance framework and a data strategy, which together act as guardrails for database design: how feature classes are named, what each includes, and how attribution is handled.
Without them, a database becomes rows and columns with no organization — the same problems as spreadsheets, just in a bigger container.
I look at our data as an asset, and there has to be an ROI of the data that we're creating. If it has no structure, we're talking AI, we're talking predictive analytics — it will not work. — ANNIE ANAND
Her team within Transportation and Public Works invests significant time creating data. If that data turns out not to be useful, she considers it a waste of taxpayer dollars. Data standardization is a must, and tools exist to help standardize GIS data.
Project coordination across 13 departments
On whether Fort Worth had seen cascading failures, Anand said failures are inevitable in real work, and the role of people with system and data knowledge is to reduce their impact. The larger need, in her words the "need of the hour," is project coordination.
Does one department talk to another? Only if you force them, lure them in, or show them a return on investment for their data and their time. A foundation like the one Murphy described, she argued, delivers all of the above. Fort Worth is actively bridging that gap through a dedicated project coordination effort.
The "pretty map" problem
Anand shared a live challenge. While building the five-year pavement plan, her director asked her to overlay the water department's planned work so streets would not be dug up twice. The watershed map she received looked good — but it was only drawings. There was no data behind it.
She had to spend extra time digitizing that work, bring it into the GIS database, and serve it for analysis to the pavement team.
People do not follow standards and guidelines, and these are areas where we miss the boat a bit. But if you have a platform, it's easy to bring those back in. — ANNIE ANAND
Her reframe on institutional knowledge: the city engineer can leave, and it's fine — the five-year plan is in GIS. That is what "scanning people's brains" looks like in practice.
AI as a partner, with guardrails
Fort Worth is already working with AI under a formal AI strategy and policy. Its central guardrail: a human is involved in every final decision. It is not machines deciding where the money goes.
Anand described the city as careful stewards of tax dollars, careful in how it serves residents, and committed to data security — while expecting AI to increase efficiency and return value on the data already collected.
Digital twins and the "hot corridor"
Fort Worth is, in her words, ready for simulation because the foundation was laid right:
- IoT and street sensors are already integrated. Real-time or near-real-time data is flowing from a designated "hot corridor" where the city is testing a range of sensors.
- Climate and road conditions are combined. Climate data is coordinated with road surface impacts.
- Safety data is merged in. Conditions are combined with Vision Zero crash data to see which can be monitored and controlled.
- Residents can see what is coming. Designing highways and bridges inside a digital twin lets residents visualize projects instead of relying on verbal descriptions — which Anand said has made a significant impact.
If your foundation is right, building blocks come very easy. You can make big things happen quickly. — ANNIE ANAND
PRESENTATION THREE
The Complete Implementation: Capabilities, Use Cases and the Path Forward
Michael Murphy and Annie Anand — joint discussion
Four capabilities, each with a result
Once the foundation is in place, Murphy described the "exciting document" that follows the "boring document" of practical justification:
| Capability | The result |
|---|---|
| AI-assisted spatial prioritization | Fewer emergency repairs; the right technician or subject matter expert routed to the right job |
| Predictive maintenance by location | Capital dollars go to the assets that need them first, not the ones next on a schedule |
| Climate risk overlays | Capital plans built for the infrastructure's whole remaining life and its real environmental exposure |
| Digital twin | Consequences simulated before dollars are committed |
On AI prioritization, Murphy used a deliberately hyperbolic example: if a nuclear facility in your jurisdiction signals trouble, you do not send the nearest technician if that person is an audiovisual specialist. A subject matter expert database combined with proximity-based scheduling and routing ensures the right person reaches the right job. He also described AI actuating IoT devices directly from a central command center — with GIS as that command center.
On predictive maintenance, the contrast was with a 30-year plan where a street is repaved because its date arrived. Predictive allocation is intelligent — and that intelligence compounds with use.
On climate risk, his example: a valve in a climate-controlled room and an identical valve in a flood zone occasionally swamped with salt water are effectively different pieces of equipment. Environmental context turns data into a coherent plan from point A to point B.
On digital twins, he called the paper map humanity's first simulation, and in-car navigation a reminder of how much a better model of the world changes outcomes. For municipalities managing campuses of hundreds of buildings, constructing a new building in a digital twin before spending a dollar puts money where it matters most.
Murphy added that a digital twin also serves citizens directly. A construction schematic means little to most residents; a clear visual does. Taken further, an exposed audit trail could let residents see where their dollars went.
It's not science fiction. It's real. It's being implemented today by actual practitioners. — MICHAEL MURPHY
Use cases with dollar figures
| Use case | Before | After |
|---|---|---|
| Water main break | Filing cabinets of paper maps while water damages surrounding infrastructure and washes out the road | A single click prevents much of the collateral damage |
| Replacement planning | Two weeks of data gathering on a $340,000 replacement | About 10 seconds |
| Coordinated projects | The same street dug up repeatedly | Coordinated work across departments |
| Prevented failures | Scheduled crews discover a problem on their next visit | Pressure sensors flag a drop about 72 hours earlier, and the right crew is routed |
None of these are about a prettier map. — MICHAEL MURPHY
The same model scales beyond government to private industry — and, Murphy admitted, to his own property, which he manages with GIS for fun.
The path forward: three phases
| Phase | Focus | Where most agencies are |
|---|---|---|
| Phase 1 — Foundation | Spatial data foundation and GIS infrastructure. Lay the golden brick: data quality requirements and schemas. | Mostly in place already, though often not connected |
| Phase 2 — Integration and automation | Connect systems; automate updates. | Where some agencies are today, and many want to be |
| Phase 3 — Full spatial intelligence | The full power of the platform: AI, prediction, simulation. | The "golden zone" — Fort Worth has completed Phase 2 and is ready to leap here |
Each phase delivers a different bucket of value. Keep demonstrating it, and make sure stakeholders see it.
Three immediate actions Murphy named:
- Audit your integration points.
- Identify your highest-consequence asset classes.
- Have the organizational positioning conversation.
Good asset management should not be difficult. The data you need is mostly already there. It's just a matter of organizing, orchestrating and coordinating it. — MICHAEL MURPHY
Just go do it
Murphy's closing message was direct: do not wait for the perfect opportunity or a sign from the sky. His example from session one — mapping where litter is found in a park to decide where another trash can belongs — is small, incremental value. The same principle applies to every asset type.
Anand's key points for asset management
Anand closed with the lessons she has learned through experience:
- A single asset ID and an authoritative asset record, with real-time updates.
- Less network tracing through old spreadsheets — go straight to the data.
- Less dependence on institutional knowledge. The digital system carries the logic and the history.
- Better capital planning, because everyone is looking at the same data in the same system at the same time.
- Stronger cross-departmental coordination, because you have a GIS platform.
FIELD GUIDE
The practical guidance that emerged across the discussion and Q&A, consolidated. Print this section.
Gap self-assessment — where are you today?
☐ Data strategy. Is GIS the authoritative record for your assets, or only a visualization of records held elsewhere?
☐ Persistent IDs. Does each asset carry one persistent ID across every system — GIS, CMMS, finance, inspection?
☐ Write-back. When a work order closes, does the GIS record update without manual re-entry?
☐ Sensor use. Do IoT readings update records, flag risk or trigger work orders — or do they stop at a dashboard?
☐ Positioning. Is GIS treated as an enterprise platform or a service department?
☐ Institutional knowledge. Could your operation continue if your longest-tenured employee retired tomorrow?
Foundation checklist — before you integrate
☐ Adopt industry data models (for example, Esri's asset-type models) rather than designing every schema from scratch.
☐ Write a data governance framework. Naming conventions for feature classes, required fields and attribution rules.
☐ Write a data strategy. What data you create, why, and what decision it supports.
☐ Require data behind every drawing. A map delivered by another department should arrive as structured features, not graphics.
☐ Move operational data out of spreadsheets and into a governed, federated database.
☐ Establish an audit trail for edits to asset geometry and attributes.
☐ Set an AI policy with human involvement in final decisions and clear data security expectations.
Integration design — the three connection types
| Connection | Best for | Design question to answer |
|---|---|---|
| Real-time feed | IoT sensors, pressure, flow, weather | What threshold flags risk or triggers a work order? |
| Scheduled sync | CMMS, inspections, condition assessments | What cadence keeps the record "current by design"? |
| Event-driven update | Valve operations, completed repairs, condition changes | Which field events must write back immediately? |
Building the business case
- Attach a dollar figure to every example. Response time, avoided collateral damage, avoided re-excavation, hours of data gathering eliminated.
- Lead with coordination. Show departments the return on their data and their time.
- Name the retirement risk. Identify the knowledge that exists only in people's heads.
- Show a small win early. Centralizing one dataset and federating it is a visible result.
- Keep the rebuttals in your pocket. Legacy systems in place for 15 years will draw pushback; defensible data points answer it.
A three-phase roadmap
Phase 1 — Foundation. Inventory what you already have. Set data quality requirements. Adopt schemas and industry data models. Assign persistent asset IDs.
Phase 2 — Integration and automation. Connect CRM/311, work orders, asset information and sensors to GIS. Automate write-back. Run spatial queries for project coordination, such as every asset within 500 feet of a planned project.
Phase 3 — Full spatial intelligence. Add AI-assisted prioritization with a human in the loop, predictive maintenance by location, climate risk overlays in capital planning, and digital twin simulation of investments before funds are committed.
Monday-morning actions
- Audit your integration points.
- Identify your highest-consequence asset classes.
- Start the positioning conversation with leadership.
- Pick one small, visible win — and deliver it.
AUDIENCE POLLS
Percentages were not read aloud during the session except where noted. Figures below are taken from the final poll report for the August 20, 2026 session.
| Question | Answer | % of Votes |
|---|---|---|
| Your organization type? | Local Government | 59% |
| Industry | 14% | |
| State Government | 12% | |
| Other | 10% | |
| Academia | 6% | |
| Federal Government | 0% | |
| Where are you located? | United States | 90% |
| Africa | 4% | |
| Asia | 4% | |
| Canada | 2% | |
| South/Central America | 0% | |
| Europe | 0% | |
| Your business sector? | Land/Public Administration/Planning | 25% |
| Public Works/Utilities | 25% | |
| Other | 20% | |
| Transportation | 12% | |
| Public Information | 4% | |
| Emergency/Public Safety | 4% | |
| Sustainability | 2% | |
| Economic Development/Budget | 0% | |
| Your municipality population size? | Over 100,000 | 41% |
| 50,000–100,000 | 20% | |
| Under 25,000 | 18% | |
| 25,000–50,000 | 10% |
| Question | Answer | % of Votes |
|---|---|---|
| Which gaps is your team struggling with? | All of the above | 51% |
| Integration Gap: systems don't talk to each other | 28% | |
| Data Strategy Gap: GIS — no authoritative spatial record | 15% | |
| Org Positioning Gap: GIS is a department, not a platform | 6% | |
| What do you think would most help your community? | Predictive Maintenance Informed by Location | 55% |
| The Digital Twin of Your Jurisdiction | 30% | |
| AI-Assisted Spatial Prioritization | 12% | |
| Climate Risk Overlays in Capital Planning | 2% |
Reading the results. Murphy said he expected an even split across the four results. Instead, 55% of the audience chose predictive maintenance informed by location, with 30% choosing a digital twin of their jurisdiction. Moderator Tracy Quant noted predictive maintenance is a natural starting point because it takes work agencies already do and enhances it quickly with richer data. On the gaps question, a majority (51%) selected all three gaps at once, and the integration gap led the individual answers at 28%.
FULL Q&A
Questions came from the live audience and from the presenters. Several audience questions went unanswered when the session ran over time and will be answered offline.
Q. (Audience) Annie, are you effectively building the foundation of Fort Worth's digital twin today? Do you see the next step being the ability to simulate projects — for example, seeing the impact of a road, water or stormwater investment across multiple departments before the city commits the dollars?
ANNIE ANAND: We're ready for it — because the foundation was laid right. Fort Worth already has integrations with IoT and sensors in the streets, including a designated "hot corridor" where the city is testing a range of sensors and bringing data in real time or near real time. That data is coordinated with climate data to see impacts on road surfaces, and merged with Vision Zero crash data to identify conditions the city can monitor and then control.
Her emphasis stayed on sequence: if the foundation is right, the building blocks come easily and big things can happen quickly.
Q. (Audience) With AI now able to work across work orders, GIS, inspection histories and operating procedures, is there an opportunity to capture institutional knowledge before it walks out the door, so it is available for the next generation?
MICHAEL MURPHY: Optimistically, yes — but there is no silver bullet. No one can guarantee institutional knowledge won't leave with a retiring employee. What an agency can do is mitigate it, so that at most one piece leaves rather than everything. AI is not a person, but it can retain and learn over time, which is the same compounding value discussed throughout the session. How that knowledge is extracted and used is the agency's decision.
He admitted he was an AI skeptic at first and saw it as a shiny object. It has since increased his efficiency and productivity enough to free time for research and development rather than routine ticket work.
ANNIE ANAND (earlier in the session): Institutional knowledge is protected when plans live in the system. If the city engineer leaves, the five-year plan is still in GIS.
Q. (Michael Murphy) Annie, in your experience at Fort Worth, have you seen a cascading failure — and were you able to catch it before it snowballed, or did you have to manage it after the fact?
ANNIE ANAND: Fort Worth has not had to move into mitigation, but failures do occur in any real work. The role of people with system and data knowledge is to reduce their impact. The bigger priority is project coordination: in a city of a million people with 13 departments, departments talk to each other only when there is pressure, incentive or a clear return on their data and time. A strong foundation provides all of those, and the city is actively building its project coordination effort on that basis.
MICHAEL MURPHY: There is real dollar value in that coordination. Savings from cutting superfluous cost can be redirected to other projects.
Q. (Michael Murphy) Was there a single event or catalyst that convinced Fort Worth's stakeholders to centralize and federate data?
ANNIE ANAND: There are many examples. The latest is planning for Bond 2026. The pavement team cannot plan in a vacuum. It needs to understand the water department's work, regional agencies such as TxDOT, grant funding and project timelines. Street reconstruction, storm drain repairs and detention basin work take time, and clustering projects to target investment in specific areas is impossible until water, stormwater and pavement data are centralized. It is a daily need, and the most pressing one as the city plans the 2026 bond.
Q. (Michael Murphy) What does an unstructured approach actually look like — and why does it matter?
ANNIE ANAND: Rows and columns with no organization — the same problems agencies had with spreadsheets, repeated inside a database. Without guardrails and standards, the big-picture questions cannot be answered, and big data analytics, AI and predictive analytics will not work. Creating meaningful data from the start is essential to the ROI of the data, and data that turns out not to be useful is wasted taxpayer money.
Unanswered questions. Moderator Tracy Quant noted that remaining audience questions would be answered offline after the session.
GLOSSARY
Authoritative record — The single, trusted version of an asset's data that all other systems and users rely on.
Single source of truth — An organizational commitment that one governed system holds the definitive version of shared data.
Persistent asset ID — A unique identifier that stays with an asset across every system and over its entire lifecycle.
Unified spatial platform — An integrated environment where GIS connects asset, work, sensor, financial and planning data so any asset can be understood from the map.
Spatial intelligence — Using location and spatial relationships to explain what is happening, predict what will happen, and recommend action.
Compounding spatial intelligence — The growth in an asset record's value as every work order, reading and inspection enriches it over time.
Geodatabase — A database that stores geographic features together with their attributes and relationships.
Federated data — Data made accessible across departments and users from a shared source rather than copied into separate files.
Data governance — The rules, roles and standards that control how data is named, structured, edited and maintained.
Data strategy — The plan defining what data an organization creates, why, and which decisions it supports.
Schema — The structure of a database: its tables or feature classes, fields and rules.
Industry data model — A pre-designed schema for a specific asset type, such as Esri's stormwater model, used as a standard starting point.
Audit trail — A record of who changed what data, when and how.
CMMS — Computerized Maintenance Management System. Software that manages work orders, maintenance and asset history.
BMS — Building Management System. Controls and monitors building equipment such as HVAC, lighting and access.
FCA — Facility Condition Assessment. A structured evaluation of a building's physical condition and needs.
CRM / 311 — Customer relationship management and non-emergency service request systems that capture resident complaints.
IoT — Internet of Things. Networked sensors and devices that report conditions such as pressure, flow or weather.
Real-time feed — A continuous data stream that updates records as readings arrive.
Scheduled sync — Automated synchronization between systems on a set cadence.
Event-driven update — A record update triggered automatically by a specific field or system event.
Interdependency modeling — Representing how assets and systems rely on one another so cascading effects can be analyzed.
Cascading failure — A failure in one asset that triggers failures in connected or nearby assets.
Spatial query — A search based on location, such as every asset within 500 feet of a project.
AI-assisted spatial prioritization — Using AI and location to rank work and route the most suitable, closest personnel.
Predictive maintenance — Scheduling maintenance based on forecast condition or failure risk rather than a fixed calendar.
Climate risk overlay — Environmental hazard data, such as flood exposure, combined with asset data for planning.
Digital twin — A living digital representation of physical infrastructure that supports simulation and decisions.
Human in the loop — A requirement that a person reviews and approves AI-supported decisions.
Vision Zero — A traffic safety strategy aimed at eliminating traffic fatalities and severe injuries.
Bond program — Voter-approved municipal borrowing used to fund a package of capital projects.
SPEAKERS
Tracy Quant — Moderator
Government Campaign Manager, Asset Management Software | Siemens
Tracy Quant leads government campaigns for Siemens Asset Management Software and moderated the final session of the GIS at the Centre series. During the session she announced the company's transition from Brightly Software, a Siemens company, to the Siemens brand after four years. She guided the audience polls and Q&A, connecting the presenters' discussion to the priorities of the local-government professionals attending.
Michael Murphy, PMP
Technical Program & Product Management, Asset Management Software | Siemens · PMP · CDPO · CSM · SCPO
Michael Murphy has spent more than two decades at the intersection of geospatial data, technology and practical problem-solving. His work has consistently centered on using GIS not only to store or display data, but to improve data quality, enable operational workflows and support better decisions. He has also worked outside GIS in algorithm and hardware development for wearable medical devices, and as Senior Program Manager of Data and Analytics for a major global fitness brand. Murphy led all four sessions of the GIS at the Centre series, and in this presentation framed the three gaps, the unified platform vision and the phased path to implementation.
Annie Anand
Business Process Manager, City of Fort Worth
Annie Anand is an urban geographer and GIS professional with more than two decades of experience. As Business Process Manager for the City of Fort Worth, she specializes in location intelligence and systems management supporting technology-driven solutions across transportation and innovation, data analytics, infrastructure and asset management. She is dedicated to making complex geospatial information accessible, improving data sharing and strengthening collaboration among planners, engineers and partner agencies. In this presentation, she shared how Fort Worth integrated its 311, work order, asset and sensor systems in GIS and is using that foundation for its 2026 bond planning.
ABOUT THE ORGANIZATIONS
Asset Management Software | Siemens — series sponsor. Formerly Brightly Software, a Siemens company. As announced during this session, after four years under the Brightly name the business has transitioned fully to the Siemens brand as Siemens Asset Management Software. It provides asset management software for public-sector and other organizations, and made the GIS at the Centre series possible as an educational opportunity.
City of Fort Worth. A Texas city of roughly one million residents with 13 departments. Fort Worth has integrated its CRM/311, work order, asset information and weather sensor systems in GIS, has built on industry-standard data models with roughly 20 years of data, operates under a formal AI strategy and policy, and is using its centralized data to coordinate planning for its 2026 bond program.
RESOURCES
FROM THIS PRESENTATION
Watch the Presentation
Click here to view Archive Video of the GIS at the Centre series
Download the Speaker Presentations
Slides and supporting materials shared during the presentation. Contact the event team for current availability.
GIS at the Centre: Spatially Intelligent Asset Management Ecosystems
The Unified Spatial Platform — Event Page
Presentation overview, speakers and supporting information.
ORGANIZATIONS & TOOLS REFERENCED
Siemens Asset Management Software
Asset management software for local government and facilities.
City of Fort Worth
Practitioner case study: integrated GIS asset management.
Esri ArcGIS Solutions
Industry data models and configurations referenced as a starting point for asset schemas.
TxDOT
Regional agency referenced in Fort Worth's cross-agency project coordination.
PROGRAMS & CONCEPTS REFERENCED
Fort Worth Bond 2026
Capital program cited as the catalyst for cross-department data coordination.
Vision Zero
Traffic safety strategy whose crash data Fort Worth merges with sensor and climate data.
CONTINUE THE SERIES
Asset Mapping Intelligence brings together practical guidance from public-sector practitioners and geospatial specialists working across asset management, infrastructure and location intelligence.
Thank you to Siemens Asset Management Software for sponsoring the GIS at the Centre series, and to the City of Fort Worth for contributing its expertise.
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This report was compiled from the presentation recording and transcript. Quotations have been lightly edited for clarity. Statements are attributed to the speaker who made them and reflect their views rather than those of their employers. Figures such as response times and cost examples reflect what was stated during the presentation. Product and program details are subject to change — confirm current information with the relevant organization.