Wrong Data Is Often Worse Than No Data: The Real Cost of Poor MRO Data Quality

How SharecatData Services Fixes Poor MRO Data Quality in ERP and CMMS Systems

Sharecat DataServices specialises in MRO data cleansing for heavy and complex industries —identifying every error, inconsistency, and anomaly in industrial ERP and CMMSsystems and correcting them systematically. Where wrong data is blockingprocurement, inflating inventory costs, or creating compliance risk, SharecatData Services finds it and fixes it. The process combines proprietary toolswith subject matter experts who understand industrial equipment, OEM namingconventions, and the data standards that apply in oil & gas, energy,utilities, pharmaceutical, and manufacturing. Clients report saving up to 90%of the time and cost previously spent in procurement processes once their ERPsystems have the correct data to operate as intended.

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What Poor MRO Data Quality Looks Like in Industrial ERP and CMMS Systems

Poor data qualityin industrial ERP and CMMS systems tends to appear in recognisable patterns. Anempty field at least signals that information is missing. Wrong data tells thesystem — and the people who rely on it — something that is not true. And whendecisions are made based on that wrong information, the consequences are realand costly.

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• Duplicate descriptions: the same     item described in three different ways — 'needle valve', 'vlv, needle','needle vlv' — creates multiple records for the same physical part. Inventory is fragmented. Reorder points are wrong. Teams buy the same     thing they already have without knowing it exists under a different name  elsewhere in the system.
• Typos and input errors: a  lowercase 'l' where there should be a '1', or a transposed digit in a partnumber. These errors look minor but make automated search and ordering  impossible — the system cannot match the record to the correct OEM item, and manual intervention becomes the only option.

• Model numbers filed as partnumbers: a model number identifies a product family, not a specific orderable item. When it appears in the part number field of an ERP or CMMS record, the system cannot generate a valid purchase order to the OEM, and the procurement team must find the correct part number manually before anything can be bought.
• Incorrect unit prices: items recorded at an average price of less than one currency unit — typically because the quantity unit was wrong at the point of entry — corrupt cost analyses and budget forecasts across the organisation, making inventory     valuations and spend visibility unreliable.
• Broken references: documents referenced in a record that were never uploaded, functional locations that  do not exist, tags that point to the wrong equipment. These errors make maintenance records unreliable and create traceability gaps that are     costly to resolve under audit or regulatory pressure.

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The Business and Financial Impact of Poor Data Quality in Industrial Operations

These errors donot stay contained. One bad record affects purchasing, which affects inventory,which affects maintenance scheduling, which affects operational availability.The further downstream the impact travels, the harder it is to trace back tothe original data error — and the more expensive it becomes to fix.

The scale of theproblem is significant. According to Gartner, poor data quality costsorganisations an average of $12.9 million per year — and in asset-intensiveindustries with large equipment inventories, that figure is typically farhigher. Research across nearly 1,900 senior manufacturing executives found that51% identified data quality as a critical issue in MRO operations, and thatduplicate purchases alone account for 5 to 7% of total MRO spend. In a surveyof industrial operations leaders, 50.8% reported experiencing between 25% and50% of downtime attributable to poor data quality — at a cost of up to $125,000per hour across industrial businesses.

In Sharecat DataServices' experience working with industrial operators, the effects show up inpredictable ways. Procurement teams cannot buy directly from OEMs because partnumber data is wrong or missing — they use intermediaries and pay more. Maintenanceteams spend 30 to 40% of their working day locating and verifying informationthat should already be in the CMMS. Stock takes reveal that items recorded asin stock either do not exist or exist under a different description in adifferent location. Regulatory audits surface data inconsistencies that requireemergency remediation — at a cost that is far higher than planned datacleansing would have been.

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Why Wrong MRO Data Is Harder to Fix Than Missing Data

The distinctionbetween wrong data and missing data is important. A missing field — a blankwhere a part number should be — is visible. Users can see it. Systems flag it.The gap is known, and it can be addressed.

Wrong data isdifferent. A model number in a part number field looks like a populated record.An incorrectly spelled manufacturer name looks like a valid entry. An itemrecorded at the wrong unit price appears to have a price. The system treatsthese records as complete — and so do the people who rely on them. The erroronly becomes apparent when something goes wrong downstream: a purchase orderthat cannot be processed, an inventory analysis that does not reconcile, a costreport that cannot be explained. By that point, the wrong data has alreadypropagated through purchasing, inventory, and maintenance for months or years.

This is why datacleansing in industrial environments requires deep knowledge of the equipment,the systems, and the standards involved. It is not a task that can be performedreliably by a general data provider or by automated tools alone. It requires peoplewho understand what correct data looks like — and can therefore identify whatis wrong.

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Why Proactive MRO Data Cleansing Delivers Better Results Than Reactive Fixes

Data cleansing isnot a one-time project. It is an ongoing operational discipline — and the mostcost-effective time to start is before the errors compound further. Astructured cleansing programme that identifies every error, prioritises thefixes with the highest operational impact, and implements correctionssystematically delivers measurable and lasting results.

Sharecat DataServices' data cleansing service identifies every error in your ERP or CMMSsystem — duplicates, typos, wrong field entries, broken references, incorrectunit prices — and corrects them systematically. Unlike general data providerswho work across unrelated industries, Sharecat Data Services' team understandsindustrial equipment and OEM data structures, which means errors that automatedtools and generalist providers miss are found and fixed. Every cleansingproject includes a before-and-after statistical comparison so the improvementis measurable.

Clean data alsoenables the next steps in digital transformation: data enrichment,AI-readiness, and advanced analytics. Without a clean foundation, thoseinvestments cannot deliver their potential. An ERP or CMMS system populatedwith wrong data will produce wrong outputs regardless of how advanced thesystem is — and AI tools trained on wrong data will amplify errors rather thancorrect them.

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