Master Data Management
Design and stabilise trusted master data domains for reliable operations and reporting.
Services
Support focused on the areas that most often create friction: trusted master data, practical governance, and the operational processes around them.
Data Governance
Build governance that works in practice: clear ownership, standards, controls, and measurable stewardship outcomes.
Process Improvement
Identify bottlenecks, rework loops, and handoff failures to improve throughput, quality, and team focus.
About
Zen MDM Consultancy provides independent expertise in product master data, PIM, data governance, and the operational processes that underpin them.
I work with organisations to transform fragmented product data into trusted, structured, and commercially valuable information that supports better decisions, stronger operations, and more effective digital commerce.
For projects that benefit from additional specialist knowledge, I collaborate with a trusted network of domain experts to deliver practical, scalable solutions tailored to the organisation’s needs.
Programmes delivered across ERP, PIM, and eCommerce ecosystems, including quality improvement work on datasets with more than 10 million data points.
- Product data transformation and quality improvement
- PIM implementation, optimisation, and recovery
- Data migration, mapping, validation, and practical governance
Book a call
Pick a convenient time and we’ll discuss your MDM, governance, or process improvement goals.
Schedule a discovery call
Frequently asked questions
How can I improve product data quality across millions of records without increasing manual effort?
Start with data profiling to identify systemic issues, then implement rule-based validation and automated pipelines using Python and SQL to clean and standardise data at scale.
How do I fix inconsistent product attributes and taxonomy across ERP, PIM, and eCommerce systems?
Define a canonical data model and standardised taxonomy, then map and transform existing data into that structure so systems stay aligned going forward.
Can you recover or optimise an underperforming PIM implementation?
Yes. That usually means addressing gaps in the data model, workflows, and adoption, then simplifying structures and controls so the platform becomes usable and trusted again.
How do I design a scalable product data governance framework that actually gets used?
Focus on practical governance: clear ownership, validation rules embedded into workflows, and controls that support day-to-day operations rather than sitting in documents.