Implementing a Product Information Management (PIM) system is not just an IT job. It’s a transformation that changes how your organisation manages, enriches, and shares product data across every channel.
Yet even with the best tools, success depends on how you plan and execute. Many teams rush straight into configuration without clear goals, governance, or ownership. The result is often confusion and wasted effort.
This guide explains each stage of a modern PIM project — from discovery to deployment — and shows how governance, agile methods, and AI can help you turn implementation into a foundation for growth.
Discovery: defining strategy and scope
Discovery is where every successful PIM project begins. It’s the phase that sets direction and defines success.
The focus here is on aligning PIM goals with business outcomes such as:
- Faster time-to-market
- Higher data quality and completeness
- Consistency across every sales and marketing channel
Teams also create governance structures and clarify who owns what. By the end of discovery, you should have:
- A clear business case linked to measurable ROI
- A defined data governance framework and ownership model
- A full map of legacy data systems and sources
- Agreed KPIs and quality standards
Start with Data often runs a data maturity assessment at this stage to uncover gaps in people, process, and technology before moving into design.
Design: turning strategy into a blueprint
Design translates strategy into an actionable plan. It defines how product data flows through your organisation and who manages each stage.
In 2026, the design phase focuses on three dimensions:
Data governance
Rules, validation checks, and approval workflows built directly into the PIM to maintain accuracy and accountability.
Business process design
Efficient, automated workflows that connect teams and reduce manual handling.
Technology architecture
Composable, API-driven systems that can evolve alongside your business.
When these three areas work together, your PIM becomes more than a system of record. It becomes a framework for control and collaboration.
Data modelling: creating structure and meaning
Data modelling defines how product information is organised and how customers experience it.
In earlier years, this was static and manual. Now, it’s dynamic and AI-supported. A strong model defines attributes, hierarchies, and relationships that make it easier for customers to search, compare, and buy.
AI tools can now:
- Suggest attribute mappings and taxonomy improvements
- Find duplicates or redundant fields
- Predict which missing data will most affect sales performance
The result is a living data model that grows with your catalogue and adapts as your product lines and customers evolve.
Build: configuring for agility and collaboration
The build phase turns your blueprint into reality. Using agile sprint cycles, functionality is configured, tested, and refined.
Modern builds focus on four priorities:
- Automating workflows and approval steps
- Integrating with ERP, DAM, and eCommerce platforms
- Managing user permissions and data security
- Enabling AI-driven validation and enrichment
Throughout this phase, your Product Owner works closely with Start with Data’s team to prioritise features and align sprint outcomes with business value. Regular reviews keep progress visible and decisions quick.
By the end of this phase, the PIM is functional, stable, and ready for migration.
Migration: bringing legacy data into the future
Migration is one of the most sensitive parts of any PIM project. In 2026, automation tools like SKULaunch have made it faster, more accurate, and far less risky.
The process typically includes profiling, cleansing, and mapping legacy data into the new model. SKULaunch helps fill missing attributes, identify inconsistencies, and locate supporting assets such as certifications or documents that were previously buried in spreadsheets or PDFs.
The goal is not simply to move data but to improve it. Each record should emerge cleaner, richer, and more consistent than before.
Deployment and hypercare: ensuring stability
Deployment marks the point where your new PIM goes live. Hypercare immediately follows to ensure stability, adoption, and early issue resolution.
During this phase, the focus is on:
- Monitoring performance and data accuracy in real time
- Providing user training and support documentation
- Tracking KPIs such as data completeness and time-to-publish
Start with Data typically provides dedicated post-launch support for the first month to ensure the system is stable and teams are confident in daily use.
Measuring success: what good looks like
Success in a PIM project is not measured by the launch date. It’s measured by the outcomes that follow.
Businesses often look for:
- Reduced manual data entry and faster onboarding
- Higher product data completeness and accuracy
- Quicker publishing and improved channel consistency
- Stronger governance and wider team adoption
When these results are visible, your PIM has moved beyond implementation and become part of your organisation’s growth engine.
Final thoughts
A PIM project is not just a software rollout. It’s an organisational transformation that touches people, processes, and technology.
Key takeaways
- Start with business objectives, not system features
- Embed governance and quality rules from the start
- Use AI to support, not replace, human expertise
- Treat migration and hypercare as chances to improve data quality
Transform your PIM journey with Start with Data
At Start with Data, we’ve guided dozens of organisations through every stage of PIM implementation, from discovery to deployment and beyond.
Our consultants blend technical expertise with practical change management. We make sure your PIM becomes a strategic advantage, not a cost centre.
If you’re preparing for a PIM project, get in touch. We’ll help you design, configure, and launch a solution that delivers measurable business value from day one.
PIM project readiness checklist
- Have you defined measurable business outcomes for your PIM?
- Is your data governance framework in place before design and build begin?
- Are your legacy data sources mapped and assessed?
- Does your PIM architecture support composability and AI integration?
- Have you planned for post-go-live monitoring and hypercare?
If you’re unsure about any of these, it’s time for a discovery conversation.
FAQs: understanding your PIM project
1. What’s the most common cause of PIM project failure?
Starting with technology instead of strategy. The most successful projects begin by defining governance, ownership, and business objectives before selecting or configuring tools.
2. How does AI change PIM implementation?
AI speeds up attribute mapping, detects data gaps, and predicts data-quality issues. It cuts manual work while improving accuracy and consistency.3. How long does a typical PIM project take?
Most projects take between 3 and 6 months from discovery to deployment, depending on data complexity and internal readiness.