Product Data Automation: Guide for PIM, DAM, ROI & OSKAR

Automation and efficiency gains are discussed in many contexts. Most people immediately think of factory machinery and industrial robots. Through automation, you can produce more of the same product in the same amount of time while lowering the unit cost. However, this usually only applies to high-volume production due to fixed costs. How this relates to product data automation might not be obvious at first glance, so let's break it down step by step.

First, we need to define what product data is and why it is needed. Broadly speaking, product data can be divided into attributes (product properties) and images, documents, or other media files—the so-called digital assets directly linked to a product.

In our TESSA wiki on product data, we have explained product data in detail: This includes physical properties (such as length, width, height, depth, or diameter). Relevant identifiers include product numbers, SKUs, EAN codes, or registration numbers for European databases like EPREL, as well as bills of materials, packaging units, filling quantities, or shelf life. Safety, availability, and inventory data are equally critical. Product data also covers colors, materials, finishes, and physical properties like viscosity, density, load capacity, conductivity, or rotational speeds.

Whether you are a manufacturer or a retailer, the workflows may differ slightly, but the core issue remains the same. Getting this data into and out of databases in an automated manner significantly reduces operational overhead.

Systems that dramatically lower this workload are PIM systems (Product Information Management) like Akeneo. They store attribute values independently of platforms, allowing you to feed them into any required channel. A PIM serves as the central hub for master data management—your single source of truth. This creates the foundation for efficiency: downstream systems are automatically supplied with validated data. If an error occurs, it only needs to be corrected in one central place—the rest happens automatically.

We have also provided a detailed explanation in our TESSA wiki on digital assets. Digital assets refer to media files in digital formats. This generally includes images, audio files, videos, and documents of all types. Images can range from photos and graphics to logos, illustrations, or technical drawings.

In recent years, the number of assets per product model has grown exponentially. Customers expect more photos, videos, and interactive content. Creating and processing these assets manually is far too time-consuming and error-prone. That is why automation starts right at creation and integration within DAM systems (Digital Asset Management). Database-compatible file naming conventions enable automated completeness checks and seamless linking with PIM data. Furthermore, AI applications are increasingly used during asset processing.

Automation doesn't happen by accident; it is the result of a structured approach. The following steps have proven successful in our projects:

Step 1: Audit Your Current Data Landscape

Before introducing any new software, identify where your product data currently resides. In practice, data is often scattered across multiple Excel spreadsheets, the ERP system, supplier catalogs, and the heads of individual employees. Map out which attributes are maintained where, how up-to-date they are, and where duplicates exist.

Step 2: Define Leading Systems and Data Models

Next, determine which system will be the leading source for specific data types. Product attributes belong in a PIM, media files in a DAM, while inventory and pricing remain in the ERP. Define which attributes you actually need and how they should be structured, rather than blindly copying all existing fields.

Step 3: Establish Automated Data Integration

Build interfaces to your upstream systems (ERP, product development, or supplier data). As a retailer, it makes sense to tap into industry product databases or request data in standardized formats like ETIM. If your raw data still arrives as PDF catalogs or price lists, take a look at our article on AI-based data extraction from PDFs.

Step 4: Set Up Automated Data Distribution

Once your data foundation in PIM and DAM is clean and structured, you can automate export to various channels:

• Populating your website or online store

• Creating B2B and B2C catalogs and brochures

• Generating product datasheets

• Exporting product data in industry standards (e.g., ETIM)

• Individual exports including assets for wholesalers and sales partners

• Connecting to marketplaces like Amazon, OTTO, MediaMarkt, or Conrad

• Generating BIM objects

Step 5: Establish Quality Assurance and Monitoring

Automation without control quickly leads to automated errors. Implement automated completeness checks—verifying, for example, whether mandatory attributes and at least one image are present before data is pushed to a target system. Active monitoring alerts you to failed transmissions, saving you from searching for silent errors later.

Step 6: Rollout, Scaling, and Ongoing Optimization

Avoid launching your entire product portfolio all at once. Start with a manageable product line to test your data model and workflows. Once running smoothly, scale across your entire catalog and add further channels.

In practice, a functional automation setup consists of several modular components:

• A PIM system like Akeneo for centralized management of product attributes.

• A DAM system like TESSA DAM for images, videos, and documents.

• A middleware solution like OSKAR for automated data exchange between PIM, DAM, and connected channels.

• Dedicated connectors (such as our custom extensions Connect Tradebyte, Connect Shopware, or Connect Contao) as well as Excel importers for initial data onboarding.

• Standardized formats like ETIM or eClass for industry-wide data consistency.

The right selection of tools depends on your existing tech stack and product range.

When multiple systems need to exchange data automatically, point-to-point connections quickly reach their limits. This is where a middleware like OSKAR steps in. Instead of building custom integrations for every marketplace and export format, data flows from PIM and DAM are routed centrally through OSKAR, which automatically translates them into the required target format.

If you add a new marketplace or sales channel, you don't need to rebuild existing connections—you simply add a single endpoint to the middleware. We explained the differences between middleware, point-to-point integrations, and iPaaS solutions in detail in our article on interfaces in e-commerce.

A practical example: For our client ZEG, OSKAR manages data transfer from the Akeneo PIM to connected downstream systems. Combined with process optimization, this reduced their time-to-market dramatically.

Automation is not an end in itself—it must deliver a clear return on investment. Here are the primary benefits we observe in our client projects:

• Time savings in data onboarding: Instead of spending weeks manually entering new products, a well-connected PIM reduces this effort to a fraction of the time. In our case study on AI-based data extraction from PDF catalogs, weeks of manual typing were reduced to a few afternoons of review and approval.

• Fewer errors and lower return rates: Maintaining attributes in a single location eliminates conflicting product specifications across webshops, catalogs, and marketplaces. This directly reduces your return rate.

• Faster time-to-market: New products or entire collections can be published across all sales channels much faster. At ZEG, this resulted in a 75% reduction in time-to-market.

• Scalability without linear headcount growth: Without automation, product data management costs scale linearly with every new product or channel. An automated PIM, DAM, and middleware architecture allows you to scale your catalog and distribution channels without increasing team size at the same rate.