5 Signs Your EAM Data is Destroying Value (And What to Do About It)
By Raghu Vishwanath, Managing Partner | August 2025 | 8 min read
You invested millions in your EAM system. You hired consultants, trained users, and went through months of implementation. Leadership expected improved maintenance efficiency, reduced downtime, and better asset performance.
Instead, you got expensive chaos.
The problem isn’t your EAM platform. SAP, Oracle, Maximo—these are powerful systems. The problem is the data powering them.
After two decades working with asset-intensive organizations across manufacturing, energy, and utilities, I’ve seen the same pattern repeatedly: excellent technology undermined by terrible data.
Here are the five unmistakable signs your EAM data is destroying value—and what you can do about each one.
Sign 1: Your Maintenance Technicians Spend More Time Searching Than Fixing
What it looks like:
Your maintenance tech gets a work order: “Replace bearing on Pump 347B.”
Simple task, right? Except:
- The part description says “BEARING” (which bearing? there are 47 types)
- No manufacturer part number in the system
- Three similar parts with slightly different descriptions
- No way to know which is correct without physically inspecting the pump
- 45 minutes wasted searching before any actual work begins
Multiply this by hundreds of work orders per week, and you understand why maintenance productivity is abysmal.
Why it happens:
Decades of inconsistent data entry. Different technicians, different facilities, different naming conventions. No standards. No validation. Just chaos accumulating in your EAM database.
The real cost:
Research shows maintenance technicians are productive only 25% of the time. The other 75%? Searching for information, clarifying work orders, hunting for parts, and dealing with data problems.
If you have 50 technicians at $75/hour fully loaded cost, that’s $7.5 million annually in wasted labor. And that doesn’t count the equipment downtime while they’re searching.
What to do:
You need three things:
- Baseline data cleansing to fix existing part descriptions
- Standardized naming conventions that everyone follows
- Automated validation that prevents bad data from entering the system in the first place
This isn’t a training problem. It’s an engineering problem that requires proper data architecture.
Sign 2: Procurement Keeps Buying Parts You Already Have
What it looks like:
Your warehouse manager discovers 23 identical bearings sitting on different shelves under different part numbers. Each was an “emergency purchase” because the system said you didn’t have inventory.
Meanwhile, critical spares you actually need aren’t stocked because procurement doesn’t trust the inventory data.
Why it happens:
Duplicate parts proliferate because:
- Same part entered with different descriptions (“BEARING-6205” vs “6205 BEARING” vs “Bearing 6205”)
- Different facilities use different part numbering schemes
- Mergers/acquisitions brought multiple catalogs together without consolidation
- No deduplication process catches these during data entry
Research shows 30-40% of MRO part records in typical EAM systems are duplicates. That means nearly half your catalog is noise.
The real cost:
Every duplicate purchase wastes money. But the bigger cost is:
- Excess inventory carrying costs (capital tied up in redundant parts)
- Warehouse space wasted on duplicates
- Lost volume discounts (buying small quantities of many duplicates instead of large quantities of one)
- Emergency purchasing premiums (expedited shipping because you can’t find existing stock)
- Write-offs when obsolete duplicates are discovered years later
One major manufacturer we worked with discovered 50,000+ duplicate parts representing millions in wasted procurement spend and excess inventory.
What to do:
Start with a comprehensive duplicate identification and consolidation project. But don’t stop there—implement governance systems that prevent new duplicates from being created.
The key is validation at the point of entry: before a new part gets added to your EAM, the system should check for existing similar parts and flag potential duplicates.
Sign 3: You Can't Get Accurate Reports or Make Data-Driven Decisions
What it looks like:
Leadership asks: “What’s our total MRO spend by category?”
Simple question. But the answer requires:
- Manual data cleanup (because categories are inconsistent)
- Spreadsheet gymnastics (because data is fragmented)
- Educated guessing (because some data is just wrong)
- Three weeks of analyst time
- A final report with so many caveats it’s basically useless
Why it happens:
Your EAM data was never designed for analysis. It was created for transactions: create work order, issue parts, close work order. Done.
Nobody thought about:
- Consistent categorization for reporting
- Standardized attributes for analysis
- Data relationships for understanding patterns
- Quality controls for ensuring accuracy
The real cost:
You’re flying blind. You can’t:
- Identify cost reduction opportunities (which parts are driving spend?)
- Optimize inventory levels (what should you stock more/less of?)
- Negotiate better vendor contracts (what’s your true volume with each supplier?)
- Implement predictive maintenance (requires clean, structured historical data)
- Make strategic decisions (leadership doesn’t trust the data)
Organizations waste millions on consultants and BI tools trying to analyze fundamentally broken data. It’s like trying to build insights on quicksand.
What to do:
Before investing in analytics tools or hiring data scientists, fix the data foundation:
- Standardize categorization using industry taxonomies (UNSPSC, eCl@ss)
- Enrich with critical attributes (manufacturer, part number, specifications)
- Establish data relationships (parts to equipment, equipment to locations)
- Implement quality scoring so you know which data is trustworthy
Only then will your reports be worth the paper they’re printed on.
Sign 4: Your EAM Implementation "Never Delivered Promised ROI"
What it looks like:
Three years after go-live, leadership is frustrated. The business case promised:
- 20% reduction in maintenance costs
- 30% improvement in equipment uptime
- 15% decrease in inventory carrying costs
Actual results? Marginal improvements at best. The system works, technically. But it’s not delivering value.
Why it happens:
The consultants focused on:
- System configuration
- Process design
- User training
- Workflow optimization
What they ignored:
- Data quality
- Catalog completeness
- Master data governance
- Data migration strategy
You migrated garbage data from legacy systems into a shiny new EAM platform. Garbage in, garbage out—just faster now.
The real cost:
The failed business case means:
- Lost credibility for the IT/operations team
- Resistance to future initiatives (“Remember what happened with the EAM implementation?”)
- Continued inefficiencies that were supposed to be solved
- Wasted investment in technology that can’t deliver value with bad data
One client told us: “We spent $8 million on EAM implementation. Should have spent $2 million on data and $6 million on the system. Instead we did it backwards.”
What to do:
If you’re planning an EAM implementation or migration:
- Data cleansing FIRST (before, not after, go-live)
- Realistic business case that accounts for data remediation costs
- Executive sponsorship for data quality initiatives
- Governance from day one so data doesn’t degrade immediately
If you’re post-implementation and struggling:
- Audit your data quality (quantify the problem)
- Prioritize critical data domains (start with most valuable assets/parts)
- Fix foundation incrementally (you don’t have to boil the ocean)
- Implement prevention systems (stop making the problem worse)
Sign 5: Every Data Quality Initiative is Temporary
What it looks like:
You’ve been here before. A year ago, you ran a “data cleansing project.” Consultants came in, cleaned up duplicates, standardized descriptions, enriched records.
For three months, everything was great. Then it started degrading. Now, a year later, you’re back to square one.
Leadership asks: “Didn’t we just fix this?”
Yes. But you didn’t fix the root cause—you just treated symptoms.
Why it happens:
Data cleansing without governance is like mopping a floor while the faucet is still running. You can mop all day, but the floor keeps getting wet.
Without systems to prevent bad data at the source, quality degradation is inevitable:
- New users create duplicates (they don’t know better)
- Emergency situations bypass validation (no time for proper data entry)
- Different facilities follow different standards (inconsistency multiplies)
- No one owns data quality (it’s everyone’s problem, so it’s no one’s priority)
The real cost:
Perpetual firefighting. Your team becomes experts at cleaning data instead of engineering solutions. Budget gets spent on recurring remediation instead of permanent fixes.
And leadership loses faith. “We spent $500K on data cleansing last year and $500K this year. When does it end?”
What to do:
Stop doing data cleansing projects. Start building data quality systems.
The difference:
- Project mentality: Clean data → hope it stays clean → repeat when it degrades
- Systems mentality: Clean data → implement prevention → maintain quality automatically
This requires:
- Automated validation at point of entry (bad data can’t get in)
- Standardized workflows that enforce quality (process prevents mistakes)
- Real-time monitoring that catches issues immediately (not quarterly audits)
- Clear ownership of data domains (someone accountable)
This is why we built Ark—to shift from perpetual remediation to permanent prevention.
The Pattern Behind All Five Signs
Notice the common thread?
These aren’t isolated problems. They’re symptoms of the same root cause: your EAM system was implemented without a proper data foundation.
You can’t fix this with:
- More user training (it’s not a knowledge problem)
- Better reports (you’re analyzing bad data)
- Additional consultants (they’ll clean it up temporarily, then leave)
- Another software tool (layering technology on broken data doesn’t help)
You fix this with data engineering—treating data quality as an architectural challenge, not a cleanup project.
What to Do Next
If you recognized your organization in three or more of these signs, you have a data quality crisis that’s costing millions annually.
Here’s how to start fixing it:
Step 1: Quantify the Problem
Don’t guess at the cost. Measure it:
- What % of your parts catalog are duplicates?
- How much time do technicians spend searching vs. working?
- What’s the value of excess inventory from bad data?
- How much emergency purchasing could be avoided?
We offer complimentary data quality assessments that quantify these costs. Worth doing even if you don’t work with us—you need the business case.
Step 2: Get Executive Buy-In
Data quality isn’t an IT problem or an operations problem—it’s a business problem. You need executive sponsorship and adequate budget.
Show leadership the quantified cost of bad data. Then show them the ROI of fixing it permanently.
Step 3: Fix Foundation, Then Govern
Don’t start with governance tools. Start with cleaning your existing data (baseline remediation). Only after you have a clean foundation should you implement governance systems to maintain quality.
Trying to govern dirty data is like trying to organize a landfill. Clean first, organize second.
Step 4: Implement Prevention Systems
This is where most organizations fail. They clean data but don’t prevent future pollution.
You need automated validation, standardized workflows, and real-time monitoring that prevents bad data from entering your EAM system in the first place.
This is the prevention-first approach—and it’s why Ark exists.
The Bottom Line
Your EAM system is only as good as the data powering it.
You can have the best technology, the most sophisticated workflows, and the most comprehensive processes. But if your data is broken, your EAM investment will never deliver value.
The good news? This is fixable. We’ve helped dozens of organizations—from Fortune 500 manufacturers to global energy companies—transform their MRO data from liability to strategic asset.
It starts with recognizing the problem. If you saw your organization in these five signs, you’re already ahead of most.
Want to see exactly what bad data is costing your organization?
We’ll analyze a sample of your MRO data and show you the quantified impact—duplicates, missing data, classification gaps, and estimated annual cost.
No sales pitch. Just clear insights into what’s broken and how to fix it.
About the Author
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.
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A major manufacturer discovered 50,000 duplicate parts with multi-million dollar annual impact. Here’s how duplicates multiply—and what they’re really costing you.
Ready to Fix Your EAM Data Problems?
If you recognized your organization in three or more of these signs, you have a data quality crisis costing millions annually.
The good news? It’s fixable. We’ve helped dozens of organizations transform their MRO data from liability to strategic asset.
Start with a complimentary data quality assessment to quantify exactly what poor data quality is costing you—and see a clear roadmap to fix it permanently.
Bluemind Solutions engineers MRO data solutions for asset-intensive industries. We don’t just consult – we build. From foundation cleansing through ongoing governance, we deliver complete solutions that transform data from liability to strategic asset.