It may seem that performing manual product data fixes feels as inevitable as the sun rising every morning: a supplier spreadsheet arrives in a messy or incomplete state, a marketplace feed fails, or a launch is blocked by missing attributes. Someone opens Excel, patches values, exports, uploads, and repeats…ad infinitum.
Our article explains precisely why that workflow indicates a structural failure in product data management. We look at how it stealthily drives up costs and intensifies risk factors, along with what practical measures you can put in place to stop manual work compounding across every SKU, channel, and seasonal campaign.
What’s actually broken (and why it keeps happening)
The data failure: Basically, your repair work is happening outside the system of record. The spreadsheet becomes a parallel truth with its own formats, rules, and
exceptions. Attribute definitions start to drift. Unit formats diverge. Parent/child variants get edited inconsistently. Worst of all, there’s no durable audit trail.
The operational consequence: you get locked into an export–fix–import loop just to keep channels alive. Work queues become exercises in troubleshooting. An unintended consequence is that a few of your people actually become bottlenecks because only they know which spreadsheets to touch.
The commercial and risk impact: you pay in multiples – once in labour costs, and then in rejects, delays, returns, and compliance exposure when regulated attributes (such as materials composition, safety and compliance documentation, origin) are wrong, incomplete, or out of date.
This sorry state of affairs persists because product data governance is either absent or unenforced: The symptoms include:
- Unclear data ownership
- Too many data entry points
- Weak supplier onboarding protocols
- Validation rules which don’t block bad-quality product records from moving forward
The hidden labour costs which don’t get measured
Spreadsheet patch-ups don’t show up as a budget line. They emerge as lost capacity across merchandisers, eCommerce operations, marketing initiatives, and category teamwork.
And it’s not as if it’s a “five-minute fix”. It’s five minutes multiplied by:
- every feed refresh
- every new supplier file
- every channel template
- every seasonal range change
- every rework cycle after an overwrite
The killer factor: most manual fixes don’t update the source (whether it’s ERP, PIM, or MDM). That means the next sync overwrites the ‘fixed’ value, and the team has to fix it again. That’s not productivity. On the contrary, it’s rework dressed up as a necessary part of the daily process.
Manual work compounds error, not quality
Manual editing introduces an error multiplier:
- Inconsistent formats: Even basic examples like10x10x10 vs 10 x 10 x 10, or cm vs mm, or Navy vs Navy Blue.
- Chaotic version control: multiple copies circulate, and nobody can prove which is the current and definitive version (the ‘Single Source of Truth’).
- Absence of auditability: you’re unable to answer “who changed this, why, and according to which rule?”. That matters greatly when it comes to areas like compliance, products being delisted, and managing an avalanche of customer queries.
Even when the spreadsheet ‘fix’ ostensibly works, it often breaks your downstream logic: That is, the processes which wholly rely on consistent structure:
- Automated attribute mapping
- Variant relationships
- Category rules
- Marketplace data requirements
The scalability wall is operational, not theoretical
Manual fixes will scale in a linear way, but your catalogue complexity scales a lot faster than this piecemeal linear repair work. If we’re talking about 500 SKUs, the spreadsheet approach can limp along…just about. However, At 50,000+ SKUs, it naturally becomes a permanent constraint on operational efficiency (processes and workflows) and effectiveness (enriched, high-quality CX). Then:
- launches slip behind because approval gates rely on humans reviewing inconsistent data
- channels reject listings because required attributes vary by category and partner
- internal teams stop trusting the data and end up build their own offline (and often siloed) versions of ‘the truth’
At this point, you can’t simply hire your way out of the problems. All you end up doing is expanding the manual surface area, as well as the potential for inconsistency.
What ‘systemic’ should look like (stabilise, standardise, enforce)
You don’t eliminate spreadsheets by banning them. Rather, you need to create a system where they simply become unnecessary.
1) Stabilise: ‘stop the bleeding’
- Define the system of record (usually a Product Information Management (PIM) platform) and stop any more ‘fixing in the export’
- Insert a quarantine step into the procedure for inbound supplier files. Don’t allow direct edits to live outputs
- Track all incidents: For instance, you can list: The top 20 rejection reasons, top 20 missing attributes, top 20 overwrite sources
2) Standardise: make data usable
- Build an enforceable attribute model: At minimum, definitions, allowed values, units, conditional requirements (by category/channel)
- Create supplier templates which align with your schema (not theirs): Such as field names, formats, units, variant rules
- Establish enrichment workflows: Clearly delineate who completes which attributes, and when (before syndication, not after rejection)
3) Enforce: Stop bad data from moving forward
- Implement validation rules and hard gates in PIM: That means product records cannot progress until they meet your completeness/format thresholds.
- Add audit trails and approvals for particularly sensitive fields like price, compliance attributes, hazardous materials or materials origin).
Use supplier data onboarding with structured ingestion (as in: mapping, validation, gap detection) so that any issues are pushed upstream instead of landing on your team’s desks. This is where a tool like SKULaunch excels: It standardises supplier spreadsheets into your governed schema before they pollute the catalogue.
What this looks like in practice – a use case
When a business replaces the unsustainable heroics of spreadsheet-wrangling with governed workflows, the benefits are measurable: fewer rework cycles, faster onboarding, and more consistent content across channels.
As an example, Start with Data’s work with MKM Building Supplies focused on schema and taxonomy improvements and large-scale enrichment across ~20,000 SKUs, We achieved the desired uptick in attribute completeness and consistency, removing the failures of digital customer journeys and low conversion rate, both previously held hostage by patchy data.
Next step: make the “manual tax” visible
If ‘make do and mend’ spreadsheet fixes are keeping your catalogue operationally alive, you already have all the evidence you need – it’s just not in one place. Get in touch with us today at Start with Data to set up your Data Assessment. We’ll support you in quantifying where manual intervention is happening, which fixes are repeatable, what’s being overwritten, and which validation and governance controls will remove this commercially disruptive work permanently.