Combining Geospatial and Nongeospatial Data for Better Reliability and Customer Experience

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A Revelation in Data Management

John Snow’s 1854 Broad Street cholera map is often called “the visualization that changed the world of data.” By placing cholera deaths and water pumps on the same map, he linked people, infrastructure, and events through location, and in doing so he transformed public health and modern epidemiology. He took something every Londoner thought they understood – water, streets, and daily life – and revealed a lethal pattern nobody had yet recognized: that cholera was spread through infected drinking water.

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Today, utilities sit on a similar, though a less harrowing, opportunity. You have customer records, asset registers, maintenance logs, usage histories, and workforce data, most of it trapped in tables and reports that tell only part of the story. The missing ingredient is often geography. When you align these records to the map, as Snow did, you create a powerful lens for understanding reliability, risk, performance, and customer experience in ways that rows and columns alone don't reveal on their own.

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From cholera clusters to critical hot spots

Snow’s breakthrough came from answering a simple but spatially framed question: “Where are these deaths happening, relative to the city’s water infrastructure?” Once he plotted each case and each pump, a cluster emerged around the Broad Street pump and pointed to a likely source.

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For a utility, the equivalent question might be, “Where are our customer problems happening, relative to our network?” You already have:

Customer service, billing, and service call records
Asset locations and premises, including poles, lines, transformers, substations, and meters
Maintenance and service failure histories
Worker and crew assignments, locations, and skills
Usage information at various points in the network

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Each of these on its own is useful. Combined and tied to a location or asset on the map, they become a modern Broad Street map for your grid, creating a way to see how events cluster around specific assets, circuits, neighborhoods, and corporations or industrial buildings.

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Building a spatial foundation

How do disparate records become spatially aware? Or, rather, how do you make disparate records spatially aware? Let’s take a look.

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Give every asset a clear spatial identity.

Poles, transformers, lines, meters and substations need accurate coordinates and unique IDs to serve as your Primary Key. Those IDs should be consistent between your Geographic Information System (GIS) and your asset management system so any record referencing that asset can be joined back to a mapped feature.

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Anchor customers to the network.

Customer accounts are typically defined by addresses, premises, and meters, not maps. By geocoding service or billing addresses and linking each premise to its serving transformer, feeder, or circuit, customer events inherit a direct spatial relationship to the network.

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Turn events into discrete data points.

Service tickets, outage calls, billing disputes, and complaints all happen somewhere. When they reference a customer, an asset, or an address, you can give them a location: the exact asset, the customer’s location, or the nearest network feature. Now they are events on the map, not just lines in a log.

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Link workforce and work history to place.

Work orders, crew assignments, and time sheets usually reference job IDs and assets. When those assets live in GIS, each job gets a corresponding spot or segment on the map. Over time, you can visualize where people actually do the work, not just how many hours they book.

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With these foundations in place, your operational data stops being just transactional and becomes spatial. Every asset, customer, event, and work order fit into a shared geographic frame, much like a modern city map of health events in epidemiology.

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Customer experience on the map

In traditional Business Intelligence, customer issues are often summarized as counts by region or by product. Spatial analysis lets you move beyond that and see how issues cluster around parts of your network so you can prioritize interventions where they matter most.

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Imagine a map where each customer call about an outage, power quality concern, or high‑bill complaint is a dot tagged with:

Type of issue
Time and duration
Affected asset or circuit
Customer segment (for example, residential, small business, critical loads)

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When these dots are overlaid on the network, patterns emerge:

A single transformer with a disproportionate number of power quality complaints
A stretch of overhead line that generates frequent, short outages
A neighborhood where high‑bill complaints spike every summer, aligned with older infrastructure or poor voltage regulation

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This is the pattern of your customers’ experiences by location, and we can draw the parallels to Snow’s cholera clusters around a particular pump. Instead of simply reporting “X complaints this month,” you can say, “This cluster of complaints ties to this segment of the network, under these conditions, and affects these specific customers.” That makes it much easier to justify targeted investments.

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Asset health and maintenance as risk maps

John Snow went looking for the environmental factors with its correlations that revealed how and why cholera deaths happened. Utilities can look at asset risk in the same way. You already have the raw material: age, type, manufacturer, inspection results, condition scores, failure history, and how hard each asset is strained. On paper, it reads like a good asset register. On a map, it turns into something closer to a story.

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Color transformers by maintenance condition and scale them by load. Add symbols or labels for past failures. Then start adding context: where storms hit hardest, where streets flood, where heat settles, where trees lean into the line, and where your most sensitive customers sit. Once those layers stack up, some parts of the grid stop looking routine and start looking precarious.

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In that view, a transformer is no longer a row of data that says “25 years old with two failures.” It becomes “25 years old, running hot, in a low‑lying, leafy corner of town, feeding a hospital.” That composite picture makes it easier to explain why this asset moves to the top of the replacement list, why that corridor deserves an extra inspection pass, and why a particular pocket of the grid belongs in a resilience program.

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Outages and reliability: tracing the spread like a disease

When a disease spreads, epidemiologists do not just note the number of cases; they watch how those cases hop across streets and districts and map the cases to each other. Reliability data can be treated the same way. Outage calls, meter events, network wiring, restoration times, and switching sequences together describe how failures or service calls moves through your grid.

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Put them on a map and a single tree contact turns into a visible chain reaction. A transformer blows, a breaker trips, and suddenly a shape appears that covers several neighborhoods. As switching restores sections, that shape breaks apart and shrinks, leaving behind the same stubborn pockets that always seem to be last back on. Layer in history from past storms and you start to see repeats: familiar spans, familiar configurations, familiar dark areas.

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Now you can point to specific stretches of assets that consistently require service, rather than treating all overhead equally. You can see which areas bounce back quickly and which ones drag, then trace those differences back to design choices, access, or automation. Don't limit yourself to saying “we had outages here.” Show how they start, where they travel, and why they persist.

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Workforce and operations: mapping the human element

Public health maps often show not just patients but also clinics and mobile teams. Utilities can borrow that playbook. Once work orders and crew records live in the same spatial frame as assets and customers, the human side of operations comes into focus.

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You can see where crews actually spend their time, how far they drive, and how long it really takes to reach different parts of the territory. You can see where underground or high‑voltage specialists tend to cluster and whether that lines up with where those skills are needed. You can also see pockets of stubborn work: the neighborhoods that draw repeated visits and the circuits that attract temporary fixes instead of lasting ones.

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Patterns that used to be gut feelings now have evidence to support them, and what were assumed to be “one offs” may become repeat issues that can no longer be ignored. A region with long response times be too far away from a branch. One area’s map is crowded with several service calls while a neighboring zone looks almost quiet. Once you can see literally where your outages, failures, and complaints exist, it becomes much easier to redraw territories, move depots sites, and optimize staffing.

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Closing the loop: your modern Broad Street moment

John Snow’s map did more than solve a mystery; it changed behavior. The Broad Street pump handle was removed, making the tainted water inaccessible, and the neighborhood’s water infrastructure was updated. In the same way, the goal of combining geospatial and nongeospatial utility data is not prettier dashboards; it’s actionable outcomes.

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When a map shows that:

A particular piece of infrastructure is a chronic source of outages for vulnerable customers
Specific assets represent outsized operational and financial risk
Certain neighborhoods bear the brunt of poor reliability or slow response
Workforce deployment does not align with needs or there are repeat service orders

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...it invites concrete action: redesign the infrastructure, prioritize replacements, rebalance crews, or invest in automation. By putting events, assets, customers, and workers on the same map, you shift from counting problems as discrete data points to locating and solving those problems with complete context, just as Snow did on the streets of London.

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Combining Geospatial and Nongeospatial Data for Better Reliability and Customer Experience