Preparing Your Data for EAM Migration

Why Your Foundation Determines Success or Failure

By Raghu Vishwanath, Managing Partner | December 2025 | 10 min read

“Our EAM migration budget is $8 million. How much should we allocate for data?”

Most CIOs are surprised by the answer: at least 25% of the total budget—$2 million or more.

“That seems excessive,” they say. “The implementation partner said data migration is included.”

What they learn six months later: data migration and data preparation are completely different. One moves your data. The other makes it worth moving.

The Uncomfortable Truth About EAM Migrations

Every EAM migration follows the same pattern:

Months 1-6: Requirements, configuration, testing (on schedule)
Months 7-12: Data migration (chaos begins)
Months 13-18: Rework, cleanup, delayed go-live (budget destroyed)
Post go-live: Users complain system doesn’t work (it’s the data)

Organizations spend millions on new EAM platforms—SAP S/4HANA, Oracle Cloud EAM, IBM Maximo—believing modern technology will solve their operational challenges.

It won’t. Not if you migrate your problems along with your data.

The new system will be faster, prettier, and more expensive. But if your data is polluted, you’ve just automated chaos more efficiently.

What Actually Happens During EAM Migrations

Let’s be honest about the typical migration process:

What Organizations Plan For:
  • System selection and vendor negotiations
  • Infrastructure and architecture design
  • Configuration and customization
  • Integration with other systems
  • User training and change management
  • Data migration (listed as single line item)
What Actually Consumes Time and Budget:

Everything above goes relatively smoothly. Then data migration begins, and the project derails:

Week 1: Extract data from legacy system
Week 2: Discover 40% duplicate rate
Week 3: Emergency meetings about data quality
Week 4: Scope additional data cleansing work
Weeks 5-20: Iterative cleanup, validation, re-extraction
Week 21: First migration attempt (fails validation)
Weeks 22-30: More cleanup, testing, re-migration
Week 31: Go-live delayed 6 months

The implementation partner’s “included data migration” meant moving data from Point A to Point B. It didn’t include making the data clean, complete, or usable.

Why Clean Data Foundation Matters

Your new EAM system is only as good as the data inside it.

With clean data foundation:

  • Equipment records are accurate and complete
  • Parts have proper specifications and classifications
  • Work order histories reveal maintenance patterns
  • Inventory levels reflect reality
  • Vendor relationships are consolidated
  • Users trust the system
  • ROI materializes as promised

With polluted data:

  • Technicians can’t find the parts they need
  • Preventive maintenance schedules are wrong
  • Procurement buys duplicates
  • Inventory valuations are fiction
  • Reports are unreliable
  • Users bypass the system
  • Millions invested, minimal value delivered

The new system doesn’t fix bad data. It amplifies the consequences.

The Migration Data Challenge

Organizations underestimate what “migration-ready data” actually requires:

Challenge 1: Legacy System Archaeology

You’re not migrating from one clean system. You’re consolidating:

  • 3-5 legacy EAM/CMMS systems
  • 15+ years of accumulated data
  • Multiple data entry standards (or no standards)
  • Retired equipment still in the system
  • Deprecated part numbers
  • Inconsistent classification schemes
  • Missing critical attributes

Each legacy system has its own pollution. Combining them multiplies the chaos.

Challenge 2: The Duplication Disaster

Organizations discover duplicates during migration testing:

Typical duplicate rates we observe:

  • Single-facility operations: 15-25% duplicates
  • Multi-site organizations: 30-40% duplicates
  • Global operations with regional systems: 40-50% duplicates

A manufacturer planning to migrate 200,000 parts might actually have:

  • 120,000 unique parts
  • 80,000 duplicates (40% of catalog)

Migrating duplicates doesn’t just waste effort—it permanently pollutes your new system.

Challenge 3: The Completeness Gap

Legacy systems allow incomplete records. New systems shouldn’t.

Common completeness issues:

  • 60-70% missing manufacturer part numbers
  • 40-50% missing equipment criticality ratings
  • 30-40% missing proper classification
  • 50-60% missing technical specifications
  • 20-30% missing equipment-part relationships

Incomplete data means your new system can’t deliver promised functionality.

Challenge 4: The Standards Problem

Different facilities, different naming conventions:

Example: Same bearing, five descriptions

  • “BEARING 6205”
  • “6205 BALL BEARING”
  • “SKF 6205”
  • “BALL BRG 6205”
  • “6205-2RS BEARING”

Without standardization, your new system becomes a searchability nightmare.

The Foundation-First Migration Approach

The organizations that succeed follow a disciplined sequence:

Phase 1: Assessment and Scoping (Weeks 1-4)

Don’t guess about data quality. Measure it.

Conduct comprehensive assessment:

  • Extract representative sample (10,000+ records)
  • Analyze duplicate rate by category
  • Measure attribute completeness
  • Evaluate classification accuracy
  • Assess naming consistency
  • Identify equipment-part relationship gaps

This reveals actual migration scope and required investment.

Phase 2: Data Cleansing Strategy (Weeks 5-8)

Decide what to migrate, what to archive, what to rebuild.

Not everything deserves migration:

  • Migrate: Active equipment and parts (last 3-5 years activity)
  • Archive: Historical records (compliance/audit trail)
  • Rebuild: Critical data with <50% completeness
  • Discard: Retired equipment, obsolete parts

Clean data strategy reduces migration scope by 30-40%.

Phase 3: Foundation Cleansing (Weeks 9-16)

This is where the investment happens—and pays off.

Execute systematic cleansing:

  • Deduplication: Eliminate 30-50% of catalog
  • Enrichment: Add manufacturer part numbers, specifications
  • Classification: Apply industry-standard taxonomy
  • Standardization: Consistent naming conventions
  • Validation: Equipment-part relationships verified
  • Governance: Quality rules established

Result: Clean, complete, migration-ready data.

Phase 4: Migration Execution (Weeks 17-20)

Now migrate clean data.

Because foundation is clean:

  • Migration scripts run cleanly
  • Validation rules pass
  • Testing reveals system issues, not data issues
  • Users see improvement from day one

Migration of clean data takes 4-6 weeks vs. 20-30 weeks for dirty data.

Phase 5: Post-Migration Governance (Ongoing)

Keep data clean.

Implement governance platform:

  • Validation rules prevent new pollution
  • Automated data quality monitoring
  • User training on data standards
  • Continuous improvement process

This prevents re-pollution of your new system.

Real-World Tale: Two Migration Approaches

These composite examples reflect patterns we’ve observed repeatedly:

Company A: “We’ll Clean As We Go”

Their approach:

  • $7M EAM migration budget
  • $200K allocated for “data migration”
  • Plan: Migrate everything, fix issues in new system

What happened:

  • Extracted 180K records from legacy system
  • Discovered 55K duplicates during migration testing
  • Found 70% missing critical attributes
  • 6-month delay while emergency cleanup happened
  • Additional $2.8M spent on data remediation
  • Go-live with partially clean data
  • Ongoing data quality issues post-migration

Final cost: $10.3M and 18 months
Business case: Failed (ROI negative at 2 years)
User satisfaction: Low (blamed new system)

Company B: “Foundation-First Migration”

Their approach:

  • $8M EAM migration budget
  • $1.8M allocated for pre-migration data preparation
  • Plan: Clean first, migrate clean data

What happened:

  • 12 weeks comprehensive data cleansing pre-migration
  • 50K+ duplicates eliminated before migration
  • 270K clean records with complete attributes
  • Migration completed in 4 weeks (vs. planned 6)
  • Go-live on schedule
  • Clean data + new system = immediate value

Final cost: $8.2M and 14 months (under budget, on schedule)
Business case: Achieved (ROI positive at 18 months)
User satisfaction: High (system performs as promised)

Same scope. Different data strategy. Radically different outcomes.

The Migration Readiness Checklist

Before starting your EAM migration, verify:

Data Quality Metrics:
  • Duplicate rate < 5%
  • Critical attributes >90% complete
  • Proper classification >95%
  • Standardized naming conventions applied
  • Equipment-part relationships validated
  • Supplier data accurate and complete
Data Governance:
  • Data standards documented
  • Ownership and stewardship defined
  • Validation rules established
  • Quality metrics agreed upon
  • Post-migration governance plan ready
Migration Scope:
  • What data will be migrated (not everything)
  • What data will be archived
  • What data will be manually entered post-migration
  • Migration sequence and dependencies mapped
  • Rollback plan if migration fails

If you can’t check all boxes, you’re not ready to migrate.

Common Objections (And Why They're Wrong)

Objection 1: “We’ll clean the data in the new system”

Why this fails:

  • New system has stricter validation rules
  • Can’t import data that fails validation
  • Cleaning in new system = learning new interface while firefighting
  • Users see broken system from day one
  • Data stewards overwhelmed

Reality: Clean before migration is 3-5x faster than clean after.

Objection 2: “Data cleansing is too expensive”

The math:

Option A: Skip data preparation

  • $200K budgeted for migration
  • $2-3M actual cost (delays, rework, ongoing issues)
  • 12-18 month delay
  • Failed business case

Option B: Foundation-first

  • $1.5-2M for pre-migration cleansing
  • Migration on time and budget
  • ROI achieved as planned
  • Users satisfied

Cleansing isn’t expensive. Skipping it is.

Objection 3: “The implementation partner will handle data”

What they actually do:

  • Extract data from legacy system ✓
  • Load data into new system ✓
  • Fix data quality issues ✗
  • Eliminate duplicates ✗
  • Standardize naming ✗
  • Enrich missing attributes ✗

Their scope: data migration (moving data)
Your need: data preparation (making data worth moving)

Different scope. Different skills. Different investment.

What To Do Next

If you’re planning an EAM migration:

Step 1: Assess Current State

Get data quality assessment before budgeting:

  • Actual duplicate count and types
  • Attribute completeness gaps
  • Classification accuracy
  • Naming standardization level
  • Estimated cleansing effort and timeline

Don’t budget migration without knowing data condition.

Step 2: Budget Appropriately

Include data preparation in business case:

  • 20-30% of total budget for data work
  • Separate line items for assessment, cleansing, migration
  • Contingency for unexpected issues
  • Post-migration governance platform

Make them explicit and prioritized.

Step 3: Start Early

Begin data preparation before system selection if possible:

  • Clean data is portable (works with any EAM)
  • Eliminates migration as critical path
  • Reduces risk and timeline

Data preparation is never wasted effort. Clean data has value regardless of which EAM you choose.

Ready to Assess Your Migration Readiness?

We offer complimentary data quality assessments specifically for organizations planning EAM migrations:

  • Comprehensive catalog analysis
  • Duplicate identification and quantification
  • Attribute completeness review
  • Migration scope recommendations
  • Effort and timeline estimates
Get Free Data Assessment →
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About the Author

Raghu Vishwanath

Raghu Vishwanath is Managing Partner at Bluemind Solutions, providing technical and business leadership across Data Engineering and Software Product Engineering.

With over 30 years in software engineering, technical leadership, and strategic account management, Raghu has built expertise solving complex problems across retail, manufacturing, energy, utilities, financial services, hi-tech, and industrial operations. His broad domain coverage and deep expertise in enterprise architecture, platform modernization, and data management provide unique insights into universal organizational challenges.

Raghu’s journey from Software Engineer to Managing Partner reflects evolution from technical leadership to strategic business development and product innovation. He has led complex programs at global technology organizations, managing strategic relationships and building high-performing teams.

At Bluemind, Raghu has transformed the organization from a data services company to a comprehensive Data Engineering and Software Product Engineering firm with two major initiatives: developing Ark—the SaaS platform challenging legacy MRO Master Data Governance products with prevention-first architecture—and building the Software Product Engineering practice that partners with clients on multi-year engagements to develop world-class, market-defining products.

Raghu is recognized for bridging business and IT perspectives, making complex problems solvable. He focuses on genuine partnerships and understanding what clients truly need. His approach combines analytical thinking with pragmatic engineering—addressing root causes rather than symptoms.

Raghu continues advancing technical expertise with recent certifications in AI, machine learning, and graph databases—staying at the forefront of technologies powering modern software solutions and driving innovation in enterprise platforms.