Most industrial operators know their data isn't perfect

But the real scale of the problem, and what it's costing, often isn't visible until it's too late. Does any of this sound familiar?

Your procurement team can't order directly from OEMs because part number data is wrong or missing.

You've migrated to a new EAM and the oldproblems came with you.

You have multiple records for the same item — described differently, in different locations, with different prices.

Your EAM system has automation capabilities you can't use because the data isn't consistent enough.

Your maintenance team doesn't trust theequipment data in the CMMS, so they verify everything manually.

You're preparing for a regulatory audit andyou know the data won't stand up to scrutiny.

WHAT WE DO

Data cleansing is the process of finding and fixing errors in the MRO and master data that already exists

... in your EAM or CMMS system. It covers duplicates, incorrect descriptions, wrong field entries, broken references, and any other deviation from the standard your data should meet. Sharecat's approach combines specialist industrial knowledge with proprietary tools — identifying errors that generic providers miss, and fixing them correctly the first time.

40%

of items in a typical industrial EAM system are missing key information, and many more contain outright errors.

WHAT YOU GET

The impact of clean data is felt immediately and compounds over time

Procurement you can automate

With correct manufacturer names and part numbers, your EAM can route purchase orders directly to OEMs. Saving up to 90% of the time and cost in procurement processes.

A global view of your stock

With duplicates removed and items uniquely identified, you can see exactly what you have, where it is, and how much it costs, across every site and facility.

Maintenance systems that work as intended

Your maintenance team can trust what they see in the CMMS. Work orders, equipment records, and spare parts lists are reliable.

A foundation for AI and analytics

Clean data is the prerequisite for predictive maintenance, advanced analytics, and AI-powered tools. Without it, AI-investments cannot deliver their potential.

WHATS INCLUDED

The service includes:

Identification of all duplicates; items described in multiple ways across the system

Unit price and unit of measure validation

Description normalisation; consistent short text and long text formats applied

Reference and link validation; documents, functional locations, tags

Manufacturer name verification and correction against Sharecat Data Services' normalised supplier database

Classification review and correction

Part number validation, distinguishing correct part numbers from model numbers and other incorrect entries

Delivery of cleansed data with a full report of changes made

Our 5-step cleansing process

Our cleansing process, optimised over 30 years, is systematic, transparent, and designed to prioritise the fixes that deliver the greatest operational impact.

1

Configuration and assessment

We review your data structure, standards, and system requirements. We configure our tools to your specific environment and agree on the target state — what 'correct' looks like for your data.

2

Load and validate

We load your data into our proprietary processing platform across SAP, Maximo, IFS, or Oracle environments. Our system compares each record against your defined standards and our internal reference sources, including a database of over one million verified equipment and parts records.

3

Identify and prioritiseerrors

We produce a detailed statistical overview of your data quality — every error, inconsistency, and anomaly, categorised and quantified. We work with you to prioritise the fixes that will deliver the most operational value.