Product data implementation pitfalls: five recurring failures in PIM, PLM and DAM projects and their mitigations

The software is rarely the reason projects fail

PIM, PLM and DAM platforms on the market today handle the core requirements of most organisations. When implementations run late or deliver poor adoption, the root cause usually lies in decisions about data, roles and scope. These decisions belong to the business, and no vendor or integrator can make them on its behalf. The five pitfalls below account for most failed or stalled projects, and each one is visible early if the team looks for it. A short readiness review at the end of the design phase, scored against the warning signs in the table, gives a steering committee an objective basis to pause or proceed.

Unclear ownership and over-customisation start in the design phase

Ownership must be defined per attribute group before any data model is configured. When marketing, product management and supply chain all believe they own the product description, three versions circulate and none is trusted. A RACI per attribute group, agreed by department heads, settles this. The product data quality and governance framework provides the structure.

Over-customisation follows when teams replicate legacy processes in a new platform. Every custom screen, script or workflow must be retested when the vendor releases updates, which on SaaS platforms happens several times a year. Mitigations:

• Write requirements as outcomes, then ask how the standard product meets them.
• Route every deviation from standard through a design authority with a cost estimate.
• Prefer configuration and documented extension points over code changes.

Data migration is the most underestimated workstream

Migration typically consumes an estimated 20 to 30% of implementation effort, while initial plans often allow 10% or less. Legacy data hides duplicates, inconsistent units, free-text attributes and missing translations that only surface during mapping.

Effective teams sequence migration as follows:

1. Profile source data in the first weeks to measure completeness, duplicates and format errors.
2. Decide what not to migrate, such as discontinued products and obsolete attributes.
3. Cleanse at source where possible, so errors do not return through integrations.
4. Run at least two trial loads with business validation before the final cutover.
5. Assign business owners to sign off each data domain.

Missing stewardship erodes quality after go-live

Data quality declines without permanent owners, often within months of launch. Projects fund consultants and developers, then disband the team at go-live and expect business users to maintain standards alongside their main jobs.

Plan stewardship as an operating role. As an estimate, one data steward per 10,000 to 30,000 actively managed SKUs is a common starting point, adjusted for attribute depth, languages and regulatory scope. Give stewards measurable targets such as channel completeness, time to publish and error rates on syndicated feeds. New compliance requirements, such as the packaging data model for PPWR, add attributes that need clear owners as well.

Big bang rollouts concentrate risk in one date

A single go-live for all categories, channels and markets concentrates every risk into one weekend. Defects multiply, user support is overwhelmed and rollback becomes impractical.

Phased delivery reduces this risk. Start with one product category or one channel that has clear value and manageable data, go live in roughly 4 to 6 months as an estimate, then extend scope in releases of eight to twelve weeks. Each release improves the data model and trains users before the next wave. Phasing also supports the funding logic in the business case for a product content platform, because value starts arriving after the first release. A broader view of suitable platforms is available in the vendor directory.