The Hidden Cost of Over-Normalised Data Models in ServiceNow

[Abhijeet Upadh2]

ServiceNow architects coming from traditional software backgrounds often bring one habit that quietly hurts the platform: over-normalisation.

On paper, highly normalised data models look elegant. In practice, they create brittle solutions that are hard to report on, hard to secure, and hard for non-technical teams to understand. I’ve seen designs with five related tables just to represent a single business concept—each requiring dot-walking, scripted joins, and performance tuning.

ServiceNow is not a relational database first—it’s a workflow and experience platform. Over-normalisation increases query complexity, slows lists and reports, and makes even simple enhancements risky. The impact usually doesn’t show up in development; it shows up six months later when reporting requests start flooding in.

Community discussions often highlight “slow reports” or “complex ACLs” as isolated problems. In reality, these are usually symptoms of a data model that optimised for theoretical purity rather than operational use.

This doesn’t mean denormalisation everywhere. It means being pragmatic. Ask:

• Who will report on this?

• Who will secure it?

• Who will maintain it after the original team leaves?

Some duplication is acceptable if it improves clarity, performance, and supportability. Architects don’t get rewarded for clever models—they get remembered for stable ones.