In the dog-eat-dog world of digital commerce, a merchant may be selling the best products, have the smartest looking website, and even the most competitive pricing. However, in a world where the customer is much more savvy, more demanding, and more willing to compare sellers’ offerings than previously, merchants live or die on the product information they provide. If this information is founded on poor-quality product data, it creates a good deal more damage than many merchants realise. It slows product onboarding, breaks search filters, causes problems with listings, confuses and annoys customers, increases returns, and inevitably degrades the company’s potential for revenue generation.
This quality problem is characterised by a steady accumulation of missing values, inconsistent data formats, outdated or obsolete product records, all underpinned by clearly inadequate data governance.
Below, we highlight the five most common product data quality issues, explain why they happen, and show you how to fix them in a way which sustains the quality standards you need to remain competitive.
1. Incomplete product data
Missing product information is by far the most common issue as well as the most visible. Products go live without information on dimensions, or materials, or compatibility details, or hi-res images, or even key technical specs.
That results in:
- Suppressed marketplace listings
- Poor search and filter performance
- Customer uncertainty and hesitation
- A higher rate of product returns
- The obligation for internal teams to manually rework bad or missing data
The solution is to define category-specific completeness rules. For instance, a sofa, a drill, and a medical consumable would have very different mandatory fields. You then need to enforce those rules at the point of entry. Product information shouldn’t be able to progress through workflow or be published to channels until the required attributes are present (and correct!).
Completeness needs to be measured, not assumed. Using dashboards and readiness scores makes gaps in information easier to visualise and easier to prioritise.
2. Inconsistent values for product attribute
Inconsistency of information inexorably breaks the chain of discoverability. For instance, the same concept ends up recorded in multiple ways: Think “Blue”, “blue”, “Navy”, “BLU”. Units are mixed. Materials are abbreviated differently. These factors make filters unreliable and comparing products becomes a lot harder for the customer than they will tolerate.
The reasons these inconsistencies happen are:
- Free-text entry[1] is allowed
- Different suppliers use different terminology
- There are no shared standards within the organisation
- Legacy product records were never audited and cleaned properly
The fix for this issue is to make sure you’re using controlled vocabularies and normalised formats. You need to define approved values for attributes such as colour, material, finish, and size. Additionally, standardise units of measure and apply validation rules so that users don’t have the option to improvise around the model. Finally, keep cleaning legacy data methodically (on a regular basis, not once), starting with your highest-impact categories.
In a sales ecosystem characterised more and more by omnichannel shopping, consistency is what makes product data usable at scale and across multiple channels.
3. Incorrect or outdated information
Incorrect data is worse than missing data because it actively misleads buyers. It most commonly emerges as old specifications, wrong dimensions, outdated certifications, obsolete images, or discontinued variants. All these degrade consumer trust in your credibility as a seller, as well as generating entirely avoidable returns.
This problem is usually caused by weak (or a lack of) ownership. It leads to:
- Failure to capture supplier changes
- Teams having to copy-paste across systems
- Updates happening in one place but not in another
- Lack of a commonly-agreed cadence for reviewing data quality
The solution is robust data governance. Accountability means assigning ownership (in fact, named owners) for critical attributes and product areas. You should also put change workflows in place so that any updates can be regularly reviewed and tracked. You’re working on several systems, so make sure to properly integrate ERP, PIM, DAM, and your eCommerce systems to minimise the need for manual rekeying. Finally, you must schedule regular audits of high-risk and high-value categories.
In fact, product data inaccuracy is more about not having operational procedures in place to manage the data than it is the data in itself.
4. Poor taxonomy and category structure
When a merchant’s taxonomy is weak, customers feel the pain well before internal teams do. Products appear in the wrong categories, filters don’t appear to make sense, and navigating to the item(s) you need becomes a lot harder than it should be. Search Engine Optimisation is affected too, because the structure of your taxonomy isn’t a true reflection of how your target customers actually search.
The red flags include:
- Overlapping product categories
- Duplicate subcategories
- Confusing parent-child relationships among products
- Inconsistent grouping across different sales channels
To put things right, you firstly need to redesign the structure around customer logic, not that of internal convenience, or based on supplier exports. Use insights on search behaviour, analytics, and channel requirements to shape your product hierarchy. Then, remove any duplicate categories, eliminate overlaps, simplify wherever possible, and finally, again, governance! Assign clear ownership for any future changes.
A strong taxonomy is the key which you establish for opening up findability, filtering, and, ultimately, a higher conversion due to an enhanced CX.
5. Duplicate and fragmented product records
Multiple versions of SKU data, alongside fragmented product records, will always create chaos. When the same SKU exists under slightly different names, supplier codes, or regional records, who knows what to use? It becomes a toss-up. It leads to variants being modelled inconsistently, while stock levels, pricing, and product descriptions drift away from reality.
This chaos causes:
- Conflicting information
- Duplicated listings
- Confusion over inventory
- Excessive time and effort wasted (with associated costs)
- A substandard customer experience
The solution is a governed single source of truth. You need to use unique identifiers consistently, whether GTINs, MPNs, or internal product IDs. Additionally, implement matching and merging rules in order to detect likely duplicates. Finally, remodel your parent-child relationships properly so product variants inherit shared attributes rather than the information having to be recreated from scratch.
Duplicate records are an annoyance for your teams but more importantly, they’re a sign that the product catalogue lacks effective control.
Final words: Cure the the causes, not just the symptoms
The good news is that these five issues are predictable. You can fix them systematically if the organisation is prepared to free itself of the ‘‘one-off, big-bang clean-up’ mindset and effectively address the underlying structural causes.
An effective corrective sequence isn’t complicated:
- stabilise your data model and quality standards
- standardise onboarding and validation procedures
- Implement and enforce a data governance framework (with associated ownership)
It’s here where PIM, supplier onboarding controls, and structured workflows have the most beneficial outcome. Your goal shouldn’t simply be “cleaner data this month.” What you really need is a catalogue that stays accurate, usable, and channel-ready over the longer term.
Next steps
If these issues sound familiar, the problem is probably bigger than a spreadsheet tidy-up. Get in touch with us today at Start with Data for a discovery call about fixing the root causes of product data quality problems. We can support you in developing a stronger operating model to drastically reduce rework, enhance channel performance, and drive the growth you’re aiming for.