In some ways, attribute sprawl is rather similar to the dreaded ‘scope creep.’ It’s what happens when your product catalogue gains data fields in inverse proportion to gaining the clarity you seek. Let’s say a customer asks for a new filter. A marketplace mandates another attribute. A campaign needs a temporary badge. Some team member has the bright idea of adding a field “just in case.”
Like a frog in a pan of boiling water, after a few years you have hundreds (in extreme cases, thousands) of attributes, duplicate meanings, inconsistent naming conventions, and templates which are impossible to fully complete.
This isn’t richer product data. It’s a schema that’s bloated to the point where it disables usability, slows down product launches, and makes it much harder to sustain consistent quality in your product data.
The data failure, consequence, and commercial impact
Data failure: Basically, an uncontrolled attribute schema:
- Duplicate or near-duplicate fields
- Unclear definitions
- Inconsistent value lists
- Channel-specific fields
All these and more pollute your core product record.
Operational consequences: onboarding becomes slower, validation becomes inconsistent, and teams default to workarounds because they don’t know which field is correct and definitive.
Commercial impact: filters and search tools degrade, marketplace feeds are non-compliant (or they underperform), and your returns rate goes through the roof because those customers who do complete a purchase are forced into guesswork with incomplete or contradictory information.
Why attribute sprawl happens
It’s not that most businesses choose sprawl. It’s more a case of inheriting it.
- Multiple teams add fields independently. Each team across Marketing, purchasing, eCommerce, compliance, or logistics has legitimate needs. However, in the absence of a single approval path, every requirement gets added as a new attribute.
- Data migrations carry the ‘data debt’ forward. Legacy ERP/PIM fields are imported “for safety,” then nobody remembers to retire them.
- Supplier onboarding creates schema drift. One supplier spreadsheet column somehow becomes a permanent attribute. Rinse and repeat across dozens of suppliers.
- Channel expansion leaks into the data model. Marketplace-only requirements are added to the ‘golden record’ rather than handled as channel override procedures.
- No-one owns the data. If nobody is ultimately accountable for the attribute model, nothing gets consolidated and nothing gets removed because …” well, it’s not my job.”
- Attributes accumulate. Creating them is easy but rationalising their quantity is far less so.
More attributes do not equal better product data
Each attribute you add to the list increases the burden of maintenance. A useful attribute is not only a field. It also needs a set of rules:
- A definition (what it means and what it does not mean)
- Applicability rules (which categories/variants it applies to)
- Format rules (units, data type, validation)
- Controlled vocabulary (permitted values)
- Ownership (who populates and who approves)
- Mappings (how it feeds channels and search)
If you add attributes without that underlying structure, you end up creating empty fields, inconsistent values, and conflicting versions of the truth. That’s why, counter-intuitively, attribute sprawl tends to produce less complete data over time, not more.
How attribute sprawl impacts internal operations
1) Slower onboarding and more draft-state backlog
Bloated templates are practically impossible to complete consistently. With deadlines pressing, teams either skip fields (creating incompleteness) or copy/paste from old products (importing inherited errors). The outcome is products stuck in draft or pre-live states while someone uses guesswork to determine which fields matter and which less so.
2) Higher error rates and ‘attribute collisions’
If you have a list of values like: ‘Colour,’ ‘Primary Colour,’ ‘Colour Name,’ and ‘Color,’ of course there’ll be conflicts. The system can’t reliably know which one is ‘the truth,’ which therefore, to publish. And humans can’t reliably know which version to maintain.
3) Automation and validation become brittle
Rules depend on a stable structure. Attribute sprawl imposes the logic of exceptions: different fields for the same concept, different formats for the same value, inconsistent applicability. Your PIM workflow eventually becomes more challenging to configure and bypassing it is a tactic to avoid headaches!
4) Mapping hell across systems
Every extra attribute increases the volume of integration work among ERP, PIM, eCommerce, marketplaces, DAM, and analytics. In the end, feeds are held together by very fragile transformations which usually break when the next schema change happens.
5) The cost of training, and the risk of relying on the ‘key-person’
New starters cannot learn this model quickly, and it’s not easy to train people when exceptions proliferate and there’s a lack of underlying structure. And then there’s the “PIM whisperer” – the only person with the knowledge (inside his/her/their head) – who effectively becomes the gatekeeper for approvals and rejections. When they’re off work, onboarding slows or even stops.
What your customers experience
Of course, customers don’t get to see your schema, but they do suffer the symptoms:
- Filters exclude products that should match (because the values don’t normalise)
- Confusing spec tables with blanks and contradictory info
- Weak quality of comparative information across similar products
- Search results which miss relevant items due to missing or inconsistent attributes
That uncertainty drives cart abandonment and ups returns. Attribute sprawl stealthily becomes a sizeable commercial problem.
The big fix: Stabilise, standardise, enforce
A lasting solution to this attribute sprawl isn’t feasible if it’s limited to yet another spreadsheet clean-up. The long-term fix is to establish an operating model for attributes.
1) Stabilise: Stop sprawl growing
- Freeze new attribute creation unless there’s an approval process in place
- Separate core vs channel attributes. Marketplace titles, temporary promo flags, and platform-specific formatting should be classified as channel overrides, not as core attribute fields
- Identify critical attributes, meaning the ones that drive discovery, compliance, and returns (per category). Treat these as non-negotiable items.
2) Standardise: Rationalise your model
- Run an attribute audit: Identify duplicates, near-duplicates, unused fields, conflicting definitions, empty-rate by category.
- Define a canonical attribute set[1] per category with clear applicability and rules for variants.
- Establish (and impose) naming conventions and a single controlled vocabulary per attribute (including unit policies).
A lean model is not “fewer fields at any cost.” It is only the fields you can govern and populate consistently.
3) Enforce: Make the model usable on a day-to-day basis
- Implement validation rules and workflow gates in your PIM so incomplete records cannot progress.
- Build a simple request path for new attributes or values: their purpose, category scope, definition, allowed values, channel mapping, and, crucially, owner.
- Actively retire attributes. If you never delete fields, sprawl will happen!
What “good” looks like
Once in place, a controlled attribute model offers your business:
- Faster onboarding because templates are relevant
- Higher completeness because the model is understandable
- Better search and filtering because values are normalised
- Fewer feed problems because mappings are stable
- Lower returns rate because key decision attributes are reliable
This is the whole point, right? Product data management which supports commercial performance rather than generating endless paperwork and cut ‘n paste.
Final words: book a discovery call
If your schema is overweight, your teams are guessing which fields to use or not, and filters or feeds keep letting you down, what you do not need is more attributes. You need a rationalised, enforced attribute operating model.
Contact us today at Start with Data to book a discovery call so we can help you identify the worst collisions, define a lean canonical model, and implement a governance framework to stop attribute sprawl from recurring.