Cleaning and enrichment are the two underlying forces which can determine whether you can manage your product catalogue like a smooth-running store, or something more akin to a hastily set up car boot sale. Cleaning fixes what’s broken; Enrichment adds what’s missing. If you’re investing in a PIM solution, or upgrading your product data processes, understanding the differences between the two will save you a lot of time, money, and perpetual internal debates about which of the multiple versions ‘the good’ data really is.
The short version
- Data cleaning improves the reliability of the product data you already have.
- Data enrichment increases the usefulness and commercial impact of that data.
The two forces are complementary, not interchangeable and they need to be tackled in the right order.
Data cleaning: making your catalogue trustworthy
Cleaning (or cleansing) is the boring but essential work that turns disorganised information into an asset you can truly rely on to generate sales.
For a product catalogue, cleansing data usually involves:
- De-duplication: removing multiple versions of the same SKU data
- Standardisation: aligning units, formats and naming conventions (such as cm vs inches, or “Red” vs “red” vs “Crimson”)
- Error correction: fixing typos, inaccurate values, and mismatched fields
- Structural fixes: mapping legacy or supplier field names into your current product attribute model
- Baseline completeness: ensuring critical fields for your core channels aren’t left blank (and are completed with accurate information!)
There’s a practical and immediate business value to cleansing. Clean data reduces rework, improves internal confidence, and prevents embarrassing (and damaging) customer-facing mistakes like incorrect specs, wrong info about variants, or inconsistent pricing across channels).
It’s how you prevent your catalogue from silently sabotaging your best efforts.
Data enrichment: making your catalogue a sales machines
Once you have a solid foundation of quality data, you can move on to what makes your data competitive. Enrichment generally involves:
- Richer technical detail for filtering and comparison.
- Channel-ready copy which explains benefits, not just features
- SEO improvements in product titles, descriptions, and metadata
- Digital assets like high-quality images, video, manuals, compliance certification, or 3D models
- Contextual attributes like best-use cases, compatibility information, or provable claims on sustainability
This is where your catalogue is no longer just “accurate enough.” It is actively persuasive. Enrichment significantly enhances search engine discovery, generates higher-quality product experiences, and could also significantly reduce product returns by answering those key customer queries before they become post-purchase complaints.
Sequence matters (a lot)
Trying to enrich dirty data is a bit like building a smart extension onto your house when it’s got dry rot. Sure, it’s do-able, but you’ll feel the pain when it comes to solving the real problem.
If you skip the cleansing phase first, you’ll be running the risk of:
- Enriching duplicate records multiple times
- Pushing inconsistent values across more channels
- Generating AI content from unreliable attributes
- Inflating costs and approvals with entirely avoidable manual reworking
The healthier flow is simple:
- Cleanse for accuracy, consistency, and uniqueness.
- Enrich for completeness, experience, and growth.
How PIM supports both disciplines
A versatile modern-day PIM system is the natural home for these workflows because it possesses the combination of structure, governance, and distribution to deliver what you need.
How PIM supports the cleansing side
- Rigorously-controlled vocabularies and picklists
- Validation rules for thresholds regarding acceptable quality data
- Governance of product taxonomy and attributes
- Workflows to deal with exceptions and approvals
- Audit trails so all changes to data are traceable
How PIM supports the enrichment side
- Creation of category-specific attribute templates
- Linking and management of digital assets
- Conformity with channel rules and syndication protocols
- Established and governed content workflows for marketing and eCommerce teams
- Completeness scoring to prioritise what data quality issues needs attention first
Set all this up right and your teams won’t have to continue solving the same data problems for the rest of their professional lives. After all, it’s the system which can do more of the grunt work and heavy lifting, while your people focus on high-value judgement calls and projects (which is what you’re paying them for!)
Where AI fits in
AI has already proved itself to be especially useful for both cleansing and enrichment:
AI for cleaning
- automated attribute mapping from supplier formats
- unit conversion and value normalisation
- anomaly detection (weights, prices, dimensions that look wrong)
- duplicate and near-duplicate identification
AI for enrichment
- suggesting missing attributes based on similar products
- generating first-draft product copy and metadata
- supporting translation and localisation
- recommending content variants by channel
For anyone considering GenAI within product data, never forget the adage flipped on its head: QIQO (Quality In equals Quality Out). If the base product attributes in your catalogue are inconsistent, you’ll simply be asking AI to scale the mess faster than your team members can…and it will, unless instructed otherwise!
A simple ownership model for busy teams
This distinction also helps with role and budget allocation:
- Cleaning ownership often sits within data governance, operations, IT, or product data teams
- Enrichment is typically driven by marketing, eCommerce, and category teams
Just to be clear, we’re not saying that means separate silos. It means clarity of responsibilities and elimination of confusion or ambiguity over what “fixing the data” genuinely means.
A pragmatic starting point
You don’t need to modernise everything at once. A sensible and pragmatic approach is to:
- Prioritise high-revenue or high-volume categories
- Clean critical attributes tied to search, compliance, and fulfilment
- Enrich for those channels which matter most to your strategy
- Expand cleansing and enrichment in waves, not as an unfeasible ‘big bang’
This keeps it realistic, with high momentum but avoiding the classic transformation trap of big ambitions paired with insufficient resource allocation.
Final words
If your product catalogue is hard to trust or scale, the issue is usually weak data cleansing and unfocused data enrichment.
Most businesses already have product data. What’s missing is data that has been properly cleansed, standardised, and structured before enrichment begins. Without that foundation, teams repeat manual fixes, struggle to get value from PIM, and put AI outputs at risk.
At Start with Data, we deliver product data cleansing and product data enrichment as hands-on execution. We clean and structure existing data, then enrich it with the attributes and content that drive search, conversion, and compliance.
If you’re preparing for a PIM, running one already, or trying to make AI useful rather than risky, the starting point is the same: cleanse first, then enrich.
Get in touch to discuss a focused, phased approach that delivers measurable improvements quickly.