When organizations decide to roll out a new CMMS, most go in excited about automation, preventive maintenance optimization, and better visibility into assets. But here’s the truth IMS Consulting has seen again and again: your CMMS is only as good as the data you put into it.
If the foundation is messy, incomplete, or inconsistent, the system will never deliver the insights, compliance improvements, and cost savings you expect.
Whether you’re implementing Archibus, a standalone CMMS, or integrating CMMS capabilities inside broader IWMS platforms, here’s the essential data you need in place before you even think about going live.
1. Build a Clean, Logical Asset Hierarchy
A well‑structured asset hierarchy is the backbone of every effective CMMS. Without it, work orders won’t route correctly, preventive maintenance becomes guesswork, and reporting turns into chaos.
Your hierarchy should:
- Follow a consistent parent‑child structure (building → floor → room → system → equipment).
- Include clear categories for mechanical, electrical, plumbing, vertical transport, life safety, and specialty systems.
- Reflect reality — meaning assets that no longer exist shouldn’t be included, and assets currently in use shouldn’t be missing.
IMS Consulting Tip:
Most organizations overcomplicate their hierarchies. Start simple, build consistency, then expand.
2. Define Preventive Maintenance Schedules the Right Way
PM schedules are one of the highest‑value outputs of a CMMS — but only if they’re set up correctly.
Before implementation, gather:
- Manufacturer recommendations
- Warranty requirements
- Compliance-driven tasks (e.g., fire/life safety, environmental, healthcare, federal/state mandates)
- Runtime‑based or meter-based triggers
- Seasonal tasks and shutdown/startup procedures
Organize PMs by:
- Frequency (daily, weekly, monthly, quarterly)
- Skill type needed
- Estimated labor hours
- Required materials or parts
Why this matters:
Clean PM data allows your CMMS to generate accurate schedules, reduce downtime, and prevent reactive “fire drill” maintenance.
3. Audit and Standardize Parts & Inventory Data
Spare parts data is almost always one of the biggest problem areas during implementation.
Before you go live, you’ll need to:
- Consolidate part numbers (remove duplicates).
- Standardize naming conventions (e.g., “Filter – HVAC – 20x20x2,” not “filter 20×20”).
- Verify quantities and reorder points.
- Document vendor info, lead times, and pricing.
- Identify parts tied to critical assets and PMs.
IMS Consulting Tip:
Inventory clean-up is time‑consuming — but it has one of the highest ROI payoffs once maintenance teams stop wasting hours hunting for the right parts.
4. Create Standard Categories, Codes & Naming Conventions
One of the biggest sources of CMMS frustration is inconsistent naming. If five techs enter the same pump five different ways, your reporting becomes useless.
Before go‑live, define:
Category Standards
- Asset categories
- Work order types
- Failure codes
- Problem / cause / remedy codes
Naming Standards
Keep it short, searchable, and consistent. Examples:
- EQUIPMENT: “PUMP‑CHW‑03”
- SYSTEM: “HVAC‑AHU‑EastWing”
- LOCATION: “BLDG‑01‑FL02‑RM210”
Work Order Standards
- Description formatting (actionable, clear)
- Priority levels
- SLA expectations
Why this matters:
Clean, standardized naming ensures your CMMS becomes a source of truth — not a digital junk drawer.
5. Validate Your Data Before You Load It
Most failed implementations don’t fail because of software. They fail because data is migrated without being validated.
Run checks on:
- Completeness (no missing asset IDs, locations, categories)
- Accuracy (does the asset actually exist?)
- Consistency (names follow rules)
- Duplication (duplicate assets, parts, vendor entries)
- Logical structure (assets belong to correct systems/locations)
IMS Consulting Tip:
Always perform a “pilot load” — load a small subset first, test it with real workflows, fix issues, then migrate the rest.
Final Thoughts: A Clean Start Leads to a Strong CMMS
A CMMS can transform your maintenance operations, but only if the foundation is solid. Data quality shapes everything:
- PM compliance
- Asset lifecycle decisions
- Technician efficiency
- Regulatory readiness
- Long‑term maintenance strategy
The time you spend preparing data before implementation is the single greatest investment you can make.
IMS Consulting has supported hundreds of CMMS and IWMS deployments, and the clients who take data preparation seriously see dramatically better results — with faster adoption and higher ROI.
About IMS Consulting:
For over a decade, IMS Consulting has been at the forefront of delivering comprehensive services across multiple platforms, including Archibus, ServiceNow, and ESRI, to our diverse clientele in both public and private sectors. As a dedicated small business, we offer personalized attention from experienced and certified consultants. Our experts collaborate closely with clients to gain a deep understanding of their operational processes, identify unique requirements, and uncover opportunities for enhanced management of their infrastructure. We are committed to helping you make informed capital budgeting decisions that yield benefits today and sustainably into the future.
Frequently Asked Questions
Why is CMMS data quality so important before implementation?
Because a CMMS relies entirely on the information fed into it, poor data quality leads to inaccurate work orders, broken preventive maintenance schedules, and unreliable reporting. Clean, structured data ensures the system performs as intended and delivers real operational value.
What data should be prepared first when getting ready for a CMMS?
Start with the essentials: a clear asset hierarchy, accurate equipment details, preventive maintenance schedules, standardized parts and inventory data, and consistent naming conventions. These foundational elements determine how well the CMMS functions from day one.
Can we fix bad data after the CMMS is already live?
Yes—but it’s far more difficult and expensive. Cleaning and restructuring data post‑implementation often requires rework, downtime, and retraining. Preparing high‑quality data before go‑live prevents errors, reduces frustration, and accelerates adoption.