What is a PIM data model?

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Uncork better product experiences and deal with ongoing complexity with Product Information Management software built on a foundation of a fully flexible data model.

In today’s omnichannel marketplace, Product Information Management (PIM) has become an invaluable tool. However, simply undertaking PIM implementation is not enough. To truly unlock the power of PIM, enterprises need an advanced system built on an intelligent data model that adheres to principles of data governance and master data management. 

A PIM data model serves as the blueprint that determines how product data is collected, organized, structured, and related within this centralized hub. This is critical, as product data is incredibly complex—and only grows as your business scales. Without a well-architected PIM data model, product info could end up fragmented, inconsistent, and ineffective. 

For these reasons and more, partnering with a trusted PIM software provider is an important step to ensuring your PIM data model meets your business’s needs today and in the future. 

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Components of a PIM data model  

A PIM data model plays a vital role in the organization and management of all product information, including:  

Together, these components form the basis of a holistic PIM data model, capable of adapting to present and future business requirements. With a sound data model in place, product data can be transformed into relevant, meaningful experiences across every touchpoint. 

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Elastic data models 

One of the most important principles in PIM data modeling is elasticity, which refers to the ability to stretch and expand as data grows. Like a rubber band, an elastic PIM data model expands and adapts with product catalogs over time. 

Unlike the premade data models that often accompany out-of-the-box PIM software, a fully extensible, elastic PIM data model can expand to support fluctuating product catalogs and requirements. In any business planning to scale operations, whether it’s through new products, new suppliers, new attributes, or new markets, this foundational flexibility in a data model is essential for PIM implementation.  

For example, consider a brand that starts with a modest product range on an e-commerce site. Their initial data model is scoped to these business needs—but what happens when they acquire new brands, expand into new markets, or simply introduce new product ranges? Weaker or rigid data models might crack under the strain, requiring costly updates or new software entirely to accommodate the new growth. 

In contrast, PIM tools with extensible data models are designed for these growth stretches. This allows you to flexibly scale operations with endless data complexity, from new products and markets to mergers and acquisitions. 

Why handling complexity is essential in today’s landscape 

As the product journey evolves from linear to circular, the complexity of data requirements seems to skyrocket. On top of the raw baseline product data management related to the actual makeup of a product, enterprises must also maintain control of:  

This is by no means an exhaustive list, and yet it already highlights the vast volumes of interconnected product data companies need to manage within their PIM system. 

PIM systems with fully extensible data models are architected to handle enormous data complexity—from the moment of PIM integration to 10 years down the road. This gives brands the confidence to handle today’s omnichannel environment and position them for future growth with ease. 

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Multilingual data management 

For brands and manufacturers selling into various markets, the task of managing multilingual data can be a challenging one. Beyond simply translating word for word, product info needs to be tailored for each consumer culture. This means having the systems in place to efficiently translate data and digital assets to create authentic localized experiences. 

Extensible data models can help ease the complexity of international and cross-cultural presence with built-in flexibility and automation around languages.  

For example, in an advanced PIM platform with a fully extensible data model, rules can be configured to cascade updates across languages automatically. If a product description is edited in English, it can trigger a prompt for the German translator to update accordingly. This creates consistent customer satisfaction worldwide, ensuring the right information reaches the right customers in a way that resonates with them.  

Intelligent data relations with flexible data models 

Another benefit of elastic data models within a PIM system is the ability to build connections and relationships between product data entities. This can unlock:  

With a flexible PIM data model as the foundation, these intelligent data relationships can help uncover key cross-sell opportunities, inform customer improvements, and enhance internal workflow efficiency.  

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inriver: The PIM data model for every industry 

Every industry—and every business in that industry—has unique needs during PIM integration. To that end, inriver provides custom data modeling solutions for companies across several industries, whether retailers, brands, manufacturers, or distributors. 

PIM for retail 

For retailers, PIM data modeling is critical to managing the extensive product taxonomy and digital assets from thousands of supplier brands. This data must be carefully organized and enhanced before it can be accurately syndicated across digital commerce sites, apps, in-store devices, print catalogs, and more.  

With an elastic data model, PIM for retailers means product content can be efficiently onboarded, validated, enriched, and finally dispersed to any channel, at any time. This translates to more contextualized customer experiences, better omnichannel buyer journeys, and a faster time to market. 

PIM for manufacturing 

For manufacturers, PIM data modeling interconnects and streamlines product specifications and other data across a complex product lifecycle. This includes R&D, formulation, packaging design, marketing, inventory, distribution, supply chain logistics, aftersales support, and more. And with increased scrutiny surrounding sustainability, transparent and accountable product data is becoming more critical at every juncture. 

PIM data models optimized for manufacturers offer a holistic view of each product as it progresses from concept to delivery. Further, unified data that flows from a PIM across departments and internal systems streamlines operations. This translates to increased productivity, and at the same time provides data quality-driven insights to fuel further optimization. 

PIM for brands 

For B2C brands selling in-store, on e-commerce channels, or a combination of the two, PIM data modeling powers shopping experiences that convert. An ideal product information management data model provides the critical capabilities required to: 

With an extensible data model integrated into the very foundation of an e-commerce tech stack, brands can focus on creating compelling product experiences that drive revenue. 

PIM for distributors 

For distributors managing massive catalogs of products, a customized PIM data model is not just nice to have—it’s essential. As distributors aggregate inventory from hundreds or thousands of suppliers and brands, all this product data needs to be precisely organized and enhanced for their wholesale customers. 

The expandable nature of PIM data modeling provides the flexibility distributors need as product lines, suppliers, and channels constantly shift and grow. Intelligent data relationships enable automated workflows for bundling products or generating customized catalog views. And with multilingual support, localized experiences can be delivered to global customer bases. 

As competition grows fiercer, customers expect more B2B self-service options and omnichannel access. Distributors need an airtight yet elastic PIM data model, enabling them to serve customers with accuracy and relevance across channels and complex product catalogs. 

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