Maintenance, Repair & Operations (MRO)
Data Assessment
Before any cleansing work starts, it's worth knowing exactly what's wrong with the data and how bad it actually is — not just assuming. A Data Assessment is a standalone engagement that reports specific findings on your current equipment and spare-parts data, giving you an honest As-Is picture of data quality alongside concrete best-practice recommendations. We run this using a statistical check framework built to measure data accuracy at scale rather than spot-checking a handful of records by hand. For asset-intensive operations — manufacturers, ERP implementers, and anyone managing a large spare-parts catalog — this assessment is usually the step that turns "our data feels messy" into a specific, prioritized list of what to fix first.
Gap Analysis & Recommendations
Knowing your data has problems isn't the same as knowing which problems matter most. Gap analysis measures your existing data quality against established best practices and identifies exactly where the biggest gaps sit — which fields are incomplete, which categories carry the most duplicates, where classification is missing or inconsistent. From there, we provide specific, actionable recommendations for closing those gaps, rather than a generic "clean everything" plan. This is what lets a cleansing project start with the highest-impact fixes first, instead of spending equal effort everywhere regardless of how much any given fix actually moves the needle.
Item Master Data Classification
Spare-parts and equipment data needs to be classified consistently to be useful for spend analysis, sourcing decisions, and cross-plant reporting. We classify item master data against UNSPSC — a global four-level taxonomy coded as an eight-digit number, with an optional fifth level for added granularity — and we adapt to a customer's in-house taxonomy where one already exists. UNSPSC is the standard most Oil & Gas operations already classify against, which makes it a natural fit for asset-heavy industries generally. Getting equipment consistently classified is what turns a spare-parts catalog from a flat list into something you can actually filter, compare and report against by category.
Attribute Value Extraction
Equipment descriptions in older MRO catalogs are often compressed into cryptic shorthand — something like "SS BALL VLV 2IN 150#" packs material, item type, size and pressure class into a string a search engine can't meaningfully parse. Attribute value extraction pulls these values out of the raw description and populates them as structured, searchable fields, matched against the template appropriate to that item type. Once size, class, material and type exist as their own fields instead of being buried in abbreviated text, your team can filter and cross-reference equipment records in ways that simply weren't possible when everything lived in one unstructured description line.
Duplicate Equipment Records
Spare-parts catalogs accumulate duplicate entries just as easily as any other item master — the same part gets entered more than once by different people, at different plants, worded differently each time. We identify duplicates by comparing noun, modifier, manufacturer name, part number and other attributes together, rather than relying on any single field to catch a match. This multi-factor approach catches duplicates that a simple exact-text search would miss entirely, which matters more in MRO data than almost anywhere else — a missed duplicate here doesn't just clutter a report, it can mean ordering a spare part you already have sitting on a shelf.
Description Standardization & Generation
Standardizing existing descriptions is only part of the job — we also generate new, properly structured descriptions from scratch where the source data is too far gone to clean up piece by piece, following whatever naming rules and format your organization actually uses. This rule-based generation means the output isn't a generic rewrite; it's built to your own conventions, so a maintenance technician searching your system finds what they're looking for using the terms your team already uses day to day, instead of hunting through inconsistent abbreviations left over from years of ad hoc data entry.