Industrial distributors carry a different kind of product data problem. It is not just the volume – a distributor stocking 500,000 SKUs across cables, pneumatics, tools, bearings, and safety equipment – it is the variety. Every manufacturer ships data in a different format, using different attribute names, different units of measurement, and different interpretations of what a specification means. We have run product data and PIM projects with companies including RS Group, APS Industrial, and Maxiparts, and across all of them we see the same seven patterns. Every single time.
Pattern 1: The data was built for a warehouse, not a customer
Most industrial distributors have product data that was designed for an internal stock system:
- Part numbers
- Supplier codes
- Product descriptions cut at 40 characters
- Prices
That information is enough to pick and pack. It is not enough to help an engineer find the right 24V DC motor for a conveyor in a food processing environment, or the correct cable gland for a specific conduit diameter and IP rating.
The underlying data often exists. It sits in:
- Data sheets
- ERP fields that have not been updated in years
- The knowledge of technical staff who have worked with the product range for a long time
The problem is that it has never been structured for customer use. Nobody translated the warehouse record into a buying tool.
The practical starting point is to map what a buyer in each product category actually needs to make a purchasing decision:
- Voltage
- Power rating
- IP rating
- Ambient temperature range
- Mounting configuration
Then audit what you hold against that standard. The gap is almost always larger than teams expect. Our product data services typically start with exactly this kind of structured gap assessment for industrial distributors, before any enrichment or platform investment begins.
Pattern 2: Technical specs are inconsistent across manufacturers
Let’s take a mechanical seal. Manufacturer A describes shaft diametre in millimetres. Manufacturer B uses inches. Manufacturer C calls the same attribute “bore size”. Manufacturer D does not include it at all. This is not unusual – it is standard practice across almost every industrial product category.
When all of that data sits in a single catalogue, you end up with broken product finders, failed faceted search, and buyers who cannot run a cross-manufacturer comparison because the attributes do not align. We have written separately about why missing attributes slow product launches – in industrial distribution, inconsistent attributes stop purchases entirely.
This is a taxonomy and attribution problem before it is a data problem. You need to have a defined attribute schema for each product category:
- Fixed attribute names
- Fixed units
- A controlled vocabulary for multi-select fields
Otherwise, normalising manufacturer data is just moving inconsistencies from one place to another.
Build the schema first. Clean and enrich it. Enrichment without a schema rarely sticks, and we have seen distributors spend twelve months cleaning data that still will not filter correctly because the underlying structure was never agreed.
Pattern 3: Fitment data is missing or unstructured
Parts-to-equipment compatibility is one of the highest-value attributes in industrial distribution. An engineer buying a replacement bearing needs to know that it fits the motor they are servicing. A maintenance buyer sourcing cable glands needs to know they are rated for the enclosure they are fitting them to. Get it wrong and the part comes back.
Most industrial distributor catalogues we assess suffer from one or more of the following:
- They lack fitment data entirely
- They carry legacy fitment tables imported years ago from a manufacturer file and never updated
- They store fitment information in a free-text description field that no search tool can interrogate
At APS Industrial, fitment data sitting in unstructured description fields was invisible to the product finder. Buyers were calling the technical support team to confirm compatibility on orders that should have been self-service. The calls were costing time and eroding confidence in the catalogue.
The fix is structural. Parts-to-equipment relationships need their own data model – not a field in the main product record, but a linked reference that can be maintained separately and searched against. This is a PIM data modelling decision as much as an enrichment task, and it comes up consistently in our PIM consulting work when we are assessing distributor catalogues ahead of an implementation.
Pattern 4: Certifications are stored as marketing copy
For buyers in manufacturing, utilities, and oil and gas, the following are not optional attributes:
- CE marking
- ATEX classification
- IP ratings
- RoHS compliance
- REACH declarations
- UL listings
These are procurement requirements. In regulated environments, a product cannot go to site without verified certification documentation.
What we typically find: certifications appear somewhere in a product description as free text. “CE certified.” “IP65 rated.” There is no structured ATEX zone filter. There is no systematic process for flagging when a certification lapses or a manufacturer withdraws approval.
Nobody is monitoring the data.
This creates two separate problems. First, compliance risk: a buyer sources a product based on a listed certification, the certification has since been withdrawn, and the product goes to site. Second, lost sales: buyers who need to filter by ATEX category or RoHS status will shortlist competitors whose catalogues support that filter, because yours does not.
Certifications need to be structured attributes – their own fields, not text embedded in descriptions. Each certification type needs a dedicated field. Expiry and review dates need to be stored somewhere they can be monitored. This is as much a governance decision as a data design decision, which connects directly to Pattern 7 below.
Pattern 5: Datasheets are the source of truth, and nobody can use them
Industrial distributors hold enormous volumes of PDFs:
- Manufacturer datasheets
- Installation guides
- Compliance declarations
- Test certificates
For a distributor with 500,000 products, that can mean two to five million documents. For a significant part of the catalogue, these are the primary source of technical truth.
The problem is that the structured product data locked inside those documents has never been extracted. Most teams default to one of two approaches:
- Manual extraction by technical staff, which is slow, expensive, inconsistently applied, and impossible to sustain at volume
- Skipping the datasheets entirely and trying to source data from manufacturer websites and feeds instead, which introduces a different version of the chaos described in Pattern 6.
AI-assisted extraction has changed what is practical here. It is not a one-click fix – edge cases still need human review, and output quality depends on document structure – but it makes systematic extraction from PDF catalogues feasible at a scale that manual work never was. The distinction between cleaning and enrichment matters here.
Extraction from datasheets is enrichment, and it should be treated as a programme with defined scope and quality standards, not a background task that gets picked up whenever someone has spare time.
Pattern 6: Manufacturer data feeds arrive in dozens of formats
Most industrial distributors receive product data directly from their supply chain:
- Content management portals
- Weekly flat-file drops
- XLSX attachments in emails from sales reps
- EDI feeds
- A structured API (occasionally)
For a distributor managing several hundred suppliers, this means several hundred different formats, several hundred different update frequencies, and several hundred different interpretations of what a complete product record looks like.
At Maxiparts, supplier feeds were arriving in over forty distinct formats across the product range. Attribute coverage varied from complete and accurate through to a part number and a price with nothing else attached. The internal team was spending significant time just processing and normalising incoming data before it could even be assessed for quality.
There is no way to handle this variation manually at volume. The fix is a supplier onboarding framework:
- A defined data specification that suppliers are required to work to
- A standard submission format
- A validation layer that flags non-compliant data before it enters the catalogue
Our supplier data onboarding service builds exactly this kind of infrastructure. The goal is to transfer the work of data normalisation from the internal team to the supply chain, with clear standards that make compliance achievable for suppliers of different sizes and technical capability.
Pattern 7: Nobody owns the data
This is the pattern that allows the other six to persist. Industrial distributors typically have product data distributed across ERP, a legacy catalogue system, spreadsheets maintained by category managers, and a PIM if they have one. In most cases, no single person or team has defined accountability for data quality. Everyone knows the data is a problem but nobody has been given the authority or the brief to fix it.
When we run a catalogue audit at an industrial distributor, we consistently find:
- Attributes that different teams define differently
- No documented data standards anywhere in the business
- Enrichment completed once by an agency and never maintained
- Category managers keeping parallel spreadsheets because the central system does not hold what they need
The spreadsheets then diverge from the system, and within eighteen months the catalogue is back where it started.
This is a governance problem, not a technology problem. A new PIM does not fix it. More enrichment does not fix it. The fix is a data governance model:
- Defined ownership for each data domain
- Documented standards
- A workflow for new product introduction
- A process for managing supplier data
- Someone accountable for maintaining it
The model does not need to be large to be effective – we have helped distributors implement a working governance framework in a matter of weeks – but it has to exist before further data investment makes lasting sense.
We cover this as part of our industrial and manufacturing engagements. Governance is consistently the difference between a catalogue that improves over time and one that returns to its previous state the moment external support ends.
Key takeaways
- Industrial product data problems are structural. Technical spec inconsistency, missing fitment data, and unstructured certifications are symptoms of a catalogue built for internal operations rather than customer use.
- Agree on the attribute schema before cleaning or enriching data. Normalising without a schema moves inconsistencies rather than resolving them.
- Fitment and certification data need dedicated data models, not description fields.
- AI-assisted extraction makes systematic datasheet-to-catalogue work practical at a scale that manual processing cannot sustain.
- Supplier data variation requires a structured onboarding framework. Volume makes manual normalisation unworkable.
- Data governance is the foundation. Without defined ownership and standards, every other investment degrades within twelve to eighteen months.
Book an industrial catalogue audit
If you recognise four or more of these patterns in your catalogue, a structured audit is the right starting point. We assess your current data state against category-specific quality standards, identify the highest-value gaps, and give you a prioritised action plan before you commit to enrichment or a new platform. Find out more on our industrial and manufacturing page, or read more about our product data services.