What is a PIM system? — definition and characteristics
To the point: A PIM system centralises all product information in a single source of truth, enriches it with marketing, compliance, and media data, and distributes it consistently across every sales channel. It closes the gap between the financial master data held in ERP and the enrichment-heavy descriptive data needed for the online shop, marketplace, and print catalogue — and from July 2026 it additionally becomes a regulatory compliance platform.
PIM stands for Product Information Management. The system is the organisational and technical layer between product-data capture and product-data distribution. Unlike the ERP system, which carries transactional master data such as article number, price, and stock, the PIM manages the enrichment-heavy descriptive data: copy, images, technical datasheets, translations, compliance attributes, and marketing assets.
Five characteristics distinguish a professional PIM from an Excel sheet or a product database in the shop:
- Centralised data storage — all product attributes sit in one system, not scattered across product management, marketing, and sales.
- Workflow-driven enrichment — copy, images, and translations are added through defined approval steps, not in ad-hoc email threads.
- Data-quality rules — mandatory fields, value ranges, and consistency checks prevent faulty delivery to channels.
- Multichannel distribution — online shop, marketplace, print catalogue, and partner portals draw their data from the same source.
- Integration with ERP, DAM, and CMS — the PIM pulls master data from the ERP, images from the DAM, and delivers consolidated data to shop front-ends.
The boundary against neighbouring systems matters in the requirements specification: ERP systems carry the financial master data, master data management (MDM) systems harmonise cross-domain data objects, DAM systems manage media assets. The PIM is the product-oriented enrichment and distribution layer.
Our take: In the Mittelstand the PIM is not an extra software layer but the consolidation of what today sits scattered across Excel files, marketing folders, and shop back-ends. Anyone who grasps that buys no tool — they buy order.
Why PIM matters now for the DACH Mittelstand
To the point: EU Regulation 2024/1781 turns the PIM into a regulatory compliance platform from 19 July 2026 — not when the Digital Product Passport goes live, but now, when the data structure is being set up.
Three drivers are pressing on Mittelstand manufacturers right now. First: sales channels keep multiplying. A firm that ten years ago ran an online shop and a print catalogue today serves Amazon, Mercateo, industry-specific B2B marketplaces, dealer-owned web shops, and increasingly AI-driven comparison portals. Manual data consistency across these channels is no longer feasible.
Second: data-quality demands are rising. According to the Bitkom studies on Mittelstand digitalisation, data quality and system integration are three of the most common digitalisation bottlenecks in the Mittelstand. The Bitkom Digital Office Index 2024 reports that two-thirds of Mittelstand firms rate their master-data quality as needing improvement. In practice that means: conversion rates suffer, returns rise, service inquiries explode — all cost items that don't appear on the balance sheet as “PIM deficit”, but are caused there.
Third — and this is the hardest regulatory lever: under EU Regulation 2024/1781 (ESPR) and the Digital Product Passport, in force since 18 July 2024, Mittelstand manufacturers must have their data structures in place by 2026. Full applicability of the ESPR and the central EU DPP register are fixed for 19 July 2026. From that date manufacturers must publish material composition, repairability, and lifecycle data per product in a machine-readable Digital Product Passport. Without a consolidated data source that is not feasible — and the PIM is the natural home for these attributes.
Our take: Channel fragmentation, declining data quality, and ESPR obligation hit the Mittelstand simultaneously in 2026. Anyone who tackles the PIM only when the DPP obligation arrives has burned the lead time — the data model must stand today, not in July 2026.
Practical example: machinery component supplier from Baden-Württemberg, around 450 employees
To the point: In a PIM/ERP integration project with a south German supplier we saw what happens when the synchronisation direction between ERP and PIM is clarified only after tool selection — and which twelve weeks of preparatory work avoid that risk.
A machinery supplier with around 12,000 articles and three sales channels (own shop, two B2B marketplaces) had abandoned a PIM project after nine months because prices kept diverging between ERP and PIM. We were called in for the reset.
The diagnosis was uncomfortably simple: the ERP carried list prices, the PIM carried channel-specific sales prices. Both systems were writing back. Nobody had defined which source led per attribute. We then did what we do first in every PIM/ERP project: an attribute-by-attribute table that records, per data field, which system is the leading source, in which direction synchronisation runs, and who holds the operational responsibility. That table becomes part of the requirements specification, not just of the implementation.
The result after eight months of reset work: consistent prices across all channels, 30 percent fewer manual corrections in sales support, and a data structure that already carries DPP-relevant attributes (material composition, supplier data). The lesson for managing directors: tool selection follows governance clarification, not the other way round.
In a second project with a south German Mittelstand furniture manufacturer (220 employees, four plants) we built up the master-data governance over nine months before putting the PIM out to tender. Conversion rate in the ERP-to-shop interface rose by 34 percent in the first half-year after roll-out; the translation completion rate for the five target markets went from 62 to 94 percent. We attribute the result less to the software than to the fact that responsibilities had been clarified before implementation.
Our take: Two cases, the same pattern — where synchronisation direction and master-data governance were clarified upfront, the PIM carried from day one. Where they weren't, the rework cost double the time and double the trust.
How we approach PIM methodologically
To the point: We approach PIM projects in four phases: governance before tool, specification before tender, pilot before full roll-out, data-quality measurement from day one. Methodological clarity decides, not the tool.
In our vendor-neutral ERP and systems consulting we look at PIM not in isolation but as part of the master-data and sales architecture. Methodologically a four-step approach has proven itself in our work:
- Governance and roles model — who maintains, who approves, who is accountable. Before any software discussion.
- Attribute and synchronisation matrix — per data field: leading source, synchronisation direction, operational responsibility. Becomes part of the requirements specification.
- Vendor-neutral selection — requirements mapped against several vendors without bias, with a five-year total-cost-of-ownership frame.
- Pilot with measurable data quality — one product group, one region, defined KPIs. Only then full roll-out.
How we apply our selection methodology in practice: In this phase we use a data-driven approach to match requirements structurally against the strengths of the relevant vendors in the DACH market. The outcome is a defensible selection decision, not a gut call.
Our take: The key difference from other advisory offerings: we recommend vendor-independently. From our experience the effort of a vendor-neutral selection pays off — otherwise the firm buys the consultant's favourite solution, not the right solution for its business model.
Common mistakes in PIM implementations
To the point: Four failure patterns appear repeatedly in Mittelstand PIM projects. Three of them are organisational (governance, ERP synchronisation, pilot approach), one is methodological (data-quality measurement). None of the four has its root in the software itself.
- Mistake 1: tool before governance. The firm selects a PIM because one vendor was particularly persuasive. The governance questions get shifted into the implementation phase — and block it there. From Dreher practice: in more than half of our reset projects, the primary problem.
- Mistake 2: ERP synchronisation undefined. As in the practical example above: prices, supplier data, or technical attributes diverge between ERP and PIM. Fix: attribute-by-attribute table in the requirements specification. Methodologically this is an architecture question, not a software question.
- Mistake 3: big-bang roll-out without a pilot. The PIM goes live for all product groups, all channels, and all languages at once. Data-quality issues only surface during mass migration — and are expensive to fix then. We recommend a pilot product group of two to four weeks.
- Mistake 4: no data-quality measurement. Without KPIs (completeness rate, translation rate, image availability) project success is a matter of opinion. The Forrester TEI study on Akeneo PIM documents measurable effects on time-to-market and return rate — these KPIs can be tracked in the DACH Mittelstand too, if reporting is planned from day one.
Our take: The mistakes are rarely technical. They are organisational and methodological — and that is exactly where the leverage of vendor-neutral advisory sits. Correction in hindsight typically costs six to twelve additional project months, in which user trust erodes.
Frequently Asked Questions
The ERP carries the financially relevant master data — article number, price, stock, supplier data. The PIM manages the enrichment-heavy descriptive data — marketing copy, images, translations, technical datasheets, compliance attributes. The two systems work together: the PIM pulls master data from the ERP and enriches it for the sales channels. Without a clear synchronisation direction per attribute, conflicts emerge — that clarification belongs in the requirements specification.
From our experience with Mittelstand manufacturers a serious PIM implementation takes six to twelve months — provided master-data governance is clarified upfront. Without that clarification the duration often doubles, because responsibility questions get renegotiated during implementation. We recommend four to eight weeks of governance preparation, then four to six months of pilot, then a phased roll-out.
Total cost of ownership varies considerably by vendor, licensing model, number of users, and integration effort. Realistic ranges for Mittelstand manufacturers with 5,000 to 50,000 articles sit between €80,000 and €400,000 over five years, including implementation. Implementation dominates total cost, not the licence. We calculate TCO over five years, not over the first licence year.
The Digital Product Passport under the ESPR regulation becomes mandatory for several product categories from 19 July 2026. A PIM selected today should be able to take material composition, supplier attributes, repairability index, and lifecycle data as structured attribute classes. From the Dreher perspective this requirement belongs in every current PIM specification — even if the firm has no DPP obligation today. Getting the data structure right once is significantly cheaper than retrofitting it two years later.
Next steps
Before selecting a PIM system, a two- to four-week preparatory step on governance, ERP synchronisation, and DPP data model is usually worthwhile. Skipping that step means buying software into an undefined responsibility structure and risking a reset project after twelve to eighteen months. We work vendor-independently and assess requirements along the master-data, ERP, and sales architecture — not along a vendor preference. Complementary to the PIM discussion we recommend our wiki entries on master data and ERP systems. The first conversation is free and leads to an honest assessment — not to a proposal weighed down with method overhead.
