Data structure & cleansing: the quiet success factor in IBM Maximo implementations

You can configure beautiful workflows and KPIs, but if your data is messy, Maximo will reflect that mess, louder and faster. As I like to say that it's a mandatory task so do it planned instead of firefighting.

Common problems I see:

• Inconsistent Location/Asset hierarchies (missing parents, mixed naming).

• Duplicates in Items, Vendors, Assets, and People.

• Free-text chaos (different names for the same thing, typo-driven records).

• Mismatched Units of Measure and conversions.

• Broken or missing Domains (status, failure classes, priorities).

Steps to avoid the pain:

✅ Define a data model early (names, keys, codes, required fields).

✅ Design a clean Location & Asset hierarchy first; freeze the naming convention.

✅ Lock down Domains & Lookup values (statuses, priorities, failure codes) before any load.

✅ Standardize item naming (templates like “<Type>-<Spec>-<Size>”).

✅ Build a Data Dictionary + Validation Rules (required fields, patterns, defaults).

✅ Profile legacy data.

✅ Do iterative pilot loads (DEV → TEST → UAT) with measurable Data Quality gates each cycle.

✅ Assign Data Owners.

✅ Train users on master data structure.

✅ No free text where a domain exists.

Clean data isn't an IT task; it’s an implementation strategy. Nail it early and Maximo will do the rest.