“Pretty Good” Data Isn’t Good Enough for the Utility Network

When utility GIS managers are asked about their data quality, the answer is almost always the same: “Pretty good.”

For the Geometric Network, that was probably true. The Geometric Network was forgiving. It let you get away with edge snapping instead of vertex connection, auto-created net junctions where you needed them, and didn’t care if materials were unknown or devices were stacked on top of each other.

The Utility Network is different. It’s not just modeling how your network looks on a map. It models how it actually works. That requires a fundamentally higher level of data precision — and the problems your Geometric Network has been carrying quietly for years will surface the moment you migrate.

Three Categories of Data Problems That Derail Migrations

After hundreds of thousands of hours migrating and managing data for utilities serving more than 20 million meters across North America, SSP identified three categories of data problems that consistently create migration risk — regardless of utility size or commodity type.

Problem Categories

Existing Asset Information – GIS data that doesn’t match what’s in the field: missing features, bad geometry, inaccurate attribution.
Business Process & System Integration – Data is fragmented across GIS, WMS, and CIS/Billing, which doesn’t align or link correctly.
Post-Migration Data Reality – Known issues carried through migration with relaxed topology rules — creating dirty areas that erode system performance and user trust.

Know What to Fix Before You Commit to a Timeline

Not every data problem needs to be fixed before migration. Some can be addressed during the migration process. Others can wait. The guide includes a complete Data Cleanup Timing Matrix covering eight common issues — including unknown materials, stacked points, vertex gaps, mid-span devices, and geometry errors — with guidance on why each one matters, how to fix it, and when.

Most utilities underestimate data cleanup effort by 30 to 50 percent. They budget for migration but not for the data work that has to happen first. Halfway through, they discover the real scope and either extend the timeline or launch with known problems that cost them for years.

Plan for data cleanup as a separate program with its own budget and timeline. Don’t treat it as a migration sub-task.

Get the Complete Planning Guide

Download the full guide to get the Data Cleanup Timing Matrix, the prioritization framework for critical vs. deferrable fixes, and a planning checklist for scoping data work correctly.