Your product catalogue is your commercial equity, in digital form. And it needs nurturing and nourishing. If yours has fallen into being fragmentary, ill-formed, or jumbled, it has a direct impact on the quality of the customer experience you claim to offer. However, there’s no mystery to cleaning your product data so it feeds into a higher-quality experience. Essentially, it’s a structured process which can be managed, measured, monitored, and maintained.
Our guide to this often vexed/vexing area for merchants will walk you through the essential steps so you can implement the solution to inconsistent and poor-quality CX.
Why product data cleaning is mission critical
Data cleaning is not an endless and inconclusive quest for the knights of your IT department. It needs to be a robust safeguard, both operationally and in terms of revenue generation.
A bad product catalogue usually exhibits these five predictable problems:
- Fragmentary data: missing key attributes (like dimensions, materials, safety info).
- Ill-formed data: typos, wrong units, broken formats.
- ‘Noisy’ data: duplicates or overlapping fields that contradict each other.
- Jumbled data: inconsistent values (for instance, colour: Is it “Sage,” “Sage green,” or “Green Sage”?)
- Siloed data: conflicting versions of the same data points tend to be hoarded across departments, buried in the ERP, languishing on unformatted spreadsheets, or scattered around in shared drives.
Moreover, these issues always rear their heads where it causes most harm:
- Customers can’t find or compare products
- Teams waste enormous amounts of time fixing supplier feeds by hand
- Launch schedules for new SKUs get delayed
- The rate of returns rises because customer expectations were set by inaccurate listings
- The risk of non-compliance increases, especially where safety or ESG data is obligatory.
That’s why clean product data is the foundation upon which PIM, PXM, and AI enrichment make multichannel growth feasible.
Six steps to sparkling clean, user- (and end user)-ready data
So, without further ado, here are the six steps you can implement to structure the cleansing process.
Step 1: Audit the catalogue you actually have
You can’t repair what you haven’t measured.
Start by defining your “golden record”: this is the ideal product data template for your business. It should include:
- Mandatory commercial fields (SKU, title, price, category)
- Mandatory technical fields (such as key sector-specific and relevant specs)
- Mandatory compliance fields (like safety marks, or ESG and sustainability attributes where applicable).
- Desired level and type of enrichment (for instance, rich multi-media, long/short/bulleted descriptions, SEO metadata)
Next, profile your product catalogue against six essential quality pillars:
- Completeness – Are all required data fields filled? Nothing missing?
- Accuracy – Do the attributes match the physical product?
- Consistency – Is the data uniform across all channels and partners?
- Timeliness – Is it the most up-to-date, current, and usable version?
- Uniqueness – Are we sure there are absolutely NO duplicates?
- Compliance – Does our data stick to predefined business rules, standardised formats, or specific value ranges?
The practical outputs you gain from step 1:
- Completeness scored by category
- A ranked list of attributes with the highest error rates
- A duplicate shortlist by brand or family
- A map of where data is born (as opposed to where it’s consumed/used)
The audit you conduct thus becomes your business case and your roadmap to quality.
Step 2: Build a robust taxonomy and attribute model
Most product data cleansing projects fail because of weaknesses in the underlying structure – it becomes a bit like sweeping sand in a windstorm. You need to focus on three key actions:
a) Create a logical taxonomy
Your category tree should reflect how customers shop, not how you configured legacy systems.
- Use clear, mutually exclusive categories
- Avoid catch-all “miscellaneous” buckets in your dropdown menus
- Build growth into the design of your taxonomy so new ranges don’t require you to rebuild it
b) Standardise attributes
Decide what is mandatory per category and define:
- Approved attribute names
- Approved units
- Approved value formats
Use controlled vocabularies
- Wherever you can, replace free-text with picklists. In other words, instead of allowing 14 creative ways to say “stainless steel”, give the data a single, governed truth.
- For B2B catalogues, consider alignment with external standards (For instance: GS1, ECLASS, ETIM) where it supports your markets and partners.
Step 3: Cleanse and normalise
Now you can get your scrubbing brushes ready, but with clear rules the data can finally comply with.
These activities usually include:
- Unit normalisation: converting and standardising all measurement systems
- Format alignment: Like dates, currencies, naming conventions.
- Character cleaning: removing hidden characters and inconsistent whitespace
- Field consolidation: eliminating redundant attribute fields
Additionally, it’s at this stage where you should tackle the greatest bugbear for merchants: The incidence of chaos among supplier data feeds
If your suppliers are still feeding you spreadsheets with inconsistent headers and, let’s say, attribute names using “creative licence,” you can implement a future-proofed fix:
- Introduce and impose submission templates
- Use supplier portals wherever possible
- Use your PIM’s AI features for automated mapping and validation before incoming data enters your core system
Step 4: De-duplicate with care
Duplicate records are like those whispering troublemakers at the back of class. They break the logic of your inventory, cause confusion and irritation for customers, and end up cannibalising SEO.
So, what are the best practices?
- Use exact matching for identical SKUs.
- Use fuzzy matching [1]for near-duplicates.
- Select the most complete record as the master version
- Merge valuable differences into the master
- Archive exceptions rather than permanently deleting them (at least, until you are confident of the correct version)
Get this right and just this step alone will dramatically reduce internal confusion and the inconsistency which can plague various channels.
Step 5: Only enrich once the foundation is clean
Cleaning is about creating trustworthy facts. Enrichment is about persuading someone to act on those facts. Once your product data is consistent, you’re ready to move from minimum viable records to high-converting content:
- Add clear information on use-cases
- Improve the range and quality of images, and the accuracy of variant-specific assets
- Include relevant and required documentation: compatibility charts, care guides, or complete technical specs, for instance
- Optimise product titles and descriptions to reflect real, current search behaviour (intent-driven)
This is where AI-powered tools are genuinely useful because clean attributes give AI reliable inputs. These enable you to generate:
- Channel-specific descriptions
- SEO-friendly titles
- Short and long-form copy
- Consistent translations at scale
But never forget the axiom! “Bad data kills good AI.”
Step 6: Put a governance framework in place so your data stays clean
It’s not so much the clean-up which is the hardest part – It’s making sure your data doesn’t suffer from a ‘relapse.’
You can lock down key quality criteria with the following:
- Validation rules: hard stops for missing mandatory fields; soft warnings for anomalies
- Clear ownership: data stewards or category owners with accountability
- Approval workflows: especially for compliance-critical attributes
- Regular health checks: monthly or quarterly, depending on the changeability (or seasonal volatility) of your catalogue
This transforms data-cleansing from a mop-up rescue mission into a routine and structured discipline.
Final words
A clean catalogue isn’t about perfection for the sake of a ‘good look.’ On the contrary – it’s about eliminating friction from every part of your commercial engine.
Follow the structured process we’ve outlined:
- Audit
- Structure
- Cleanse
- De-duplicate
- Enrich
- Govern
You’ll not only be enhancing the quality of your product data, but significantly improving your customers’ confidence, your operational agility, and your capacity to scale…and GROW.
If your catalogue feels like it is held together by crossed fingers and manually reworking spreadsheets (not to mention the heroic effort of internal staff unfortunate enough to be tasked with this), it’s high time you got a more reliable plan together! At Start with Data, we help businesses of all types, sectors, and sizes to audit their product data, design robust taxonomies, cleanse supplier feeds, and implement product data strategies which will ensure a consistently high level of quality long after the first clean-up. Get in touch today and we can discuss in more detail how to plan and implement a practical roadmap from data disorder to control.