Data Syndication: How to Achieve Strong Product Listings Across All Target Channels

Challenges in the Product Data World – and How to Overcome Them

Today’s e-commerce landscape is full of challenges but also offers great opportunities – if you recognize and seize them, that is. A key to increased visibility and revenue is high-quality, meaningful product data. What seems obvious at first glance is, for many retailers, a daily challenge: after all, every external data recipient requests product data in different structures, standards, and formats.

One marketplace requires the XML data standard BMEcat 2005, while another expects product data standardized according to the JSON-based ETIM xChange. Retailers quickly ask themselves: who can meet these diverse requirements – and, more importantly, how? The solution is using a specialized, flexible data syndication software.

This article explains what data syndication actually is – and how PIM users can transform and deliver product data tailored to each recipient.

 

The Role of Data Syndication in Product Data Management

To understand what data syndication is, it helps to first take a step back and look at the broader topic of product data management. The first nexoma guest article already covered data onboarding: this refers to the precise import of external data into internal systems (e.g., standardizing and importing numerous product datasets from different suppliers into the ATAMYA PIM).

Data syndication flips this import–export perspective: it is about transforming data from various internal sources (PIM, ERP, DAM, MDM, individual files) to meet the specific requirements of external data recipients (marketplaces, data pools, portals, customers…) and exporting it.

While this sounds logical, it quickly becomes a challenge without clear processes and solutions: multiple product datasets often meet various recipients, each of which requires the data in a different format. The challenges do not stop there, as manually adjusting numerous records in various Excel sheets is extremely time-consuming – and error-prone.

This is where specialized data syndication software comes in. It allows product descriptions, packaging units, prices, datasheets, images, videos, documents, and more to be:

• consolidated from different data sources,
• enriched into high-quality datasets,
• transformed into specific data standards or exchange formats,
• checked for structural consistency, and
• automatically delivered accurately to various target channels.

The term data syndication refers to the structured preparation and delivery of tailored product data – sometimes (depending on the definition) also including the prior consolidation of different source data. In any case, it represents a central part of the entire data transformation process, often also referred to as feed management (product data feeds = structured files distributed to multiple target channels).

 

PIM and Data Syndication: How Do They Work Together?

Product information management in a PIM goes hand in hand with data syndication to reach the respective data destination. In other words: between the PIM and other data sources on one side and the data recipients on the other, a mediator is needed – a data syndication middleware.

Complementing the PIM as the central hub of product information management (single point of truth), such a data syndication software functions as a single point of distribution: PIM data, together with data from other sources (ERP, CSV files, Excel sheets, etc.), is centrally enriched, transformed, and structured for delivery to multiple target channels.

For this data exchange to work smoothly from source to middleware, the PIM must first be connected to the data syndication software. In the case of ATAMYA, this is easily possible via its REST API.

Next, a relationship is established between the source data and the desired target structure using data mapping: individual source fields (e.g., item number, manufacturer, price) are linked to their corresponding target fields. Once all fields are mapped, the desired target format is generated, data is checked, and the final distribution to all recipients takes place.

 

Which Formats and Standards are Used in Which Industries?

Just as industries vary, so do the data formats they use. Besides common exchange formats such as XML, JSON, CSV, and XLSX (Excel), nearly every industry also uses standards tailored to its specific needs. But no worries: the high flexibility of data syndication solutions allows compliance with all these requirements, including customer-specific ones.

Here is a brief overview of some common standards by industry:

• Cross-industry: the German data standard BMEcat is widely used.
• German construction industry: primarily uses the construction industry standard DPB.
• HVAC (German “SHK” – sanitary, heating, and air conditioning) data pools or platforms often use DQR (for products listed in Germany) or BMDG (Building Master Data Guideline) for international listings.
• France: the construction industry standard FAB-DIS is common.

Additionally, there are classifications, meaning standardized categorizations of products, their attributes, and features. These ensure, for example, that color names are consistent across listings. Otherwise, there is a risk that, for example, two retailers will use different terms to describe the same color. Classification standards like ETIM or ECLASS prevent that by providing clear identification codes for attributes such as color. This enables a transparent comparison and clarity of similar or identical products among retailers, platforms, and customers alike.

 

Competitive Advantages of Data Syndication Software

The advantages of data syndication tools show that combining various data sources creates synergies beyond individual datasets. Optimized product data is therefore a real competitive advantage, providing:

• Short time-to-market: data recipients can list already prepared, well-structured product data quickly and easily.
• High visibility: enriched and optimized data is indexed better by shops and search engines than incomplete data.
• Increased sales potential: customers prefer products with appealing, informative descriptions, increasing the likelihood of purchase.

However, product data is not appealing, informative, or enriched by default. Therefore, data syndication solutions specialize in various aspects of data transformation.

This starts with easy integration of multiple data sources – via pre-integrated standard connectors (interfaces to databases, shop systems, or data management systems, including PIM providers like ATAMYA). Distribution of adapted product data to various target channels is automated through standard interfaces to marketplaces, data pools, and more.

Data transformation and generation of various formats is often facilitated by selectable target format templates, such as templates for common standards (various BMEcat variants, ETIM xChange, BMDG, DQR, FAB-DIS, …). AI-supported mapping suggestions and transformation functions make data adaptation flexible. This allows users to, for example, combine multiple short texts into a long product description, convert units, or edit images.

AI-assisted mapping recommendations and data transformation functions make editing data more flexible, allowing you to meet every single requirement without exception. This way, users can combine multiple short texts into one long and coherent product description, recalculated measurement units, or, to give just one more example, edit images.

 

Typical Data Syndication Challenges for Companies

A versatile data syndication solution is only as effective as the user and the underlying data quality allow. If product data in the PIM already lacks a certain minimum quality, subsequent data transformation becomes much harder.

Whether for data onboarding or data syndication, practice shows that in-house product data management often suffers from missing structures. One of the main reasons, besides the lack of specialized software, is insufficient awareness of data quality and its impact on revenue potential.

Essential data management principles are: data must always be

• up-to-date,
• complete, and
• meaningful/understandable.

This applies not only to later adjustments or maintenance in the PIM but already at initial data creation. An initial, already high-quality data entry by product developers, for example, makes it much easier for downstream departments such as sales and marketing to create clear, appealing product descriptions – and effectively present products to potential customers. This is crucial not only for the company’s own online shop but also, in terms of data syndication, for efficient listings on external channels.

Following these data quality guidelines is particularly important, because data standards often need to be generated multiple times, regularly, and automatically for new product data. Training employees is also key: how do I map data? How do I use transformation functions? Software vendors of data syndication tools often provide guidance and best practices to support users effectively.

 

Top 5 Learnings for Successful Data Syndication

This article demonstrates: efficient data syndication is not rocket science. In practice, these five learnings are essential:

1. Develop basic awareness of data quality: without high-quality (PIM) product data, good listings and revenue potential are lost.
2. Understand data syndication (software): actively use flexible generation of data standards and exchange formats—from initial source consolidation, through data transformation, to delivery of perfectly tailored product data.
3. Recognize manual limits—enable automated processes: manual adjustments and exports are time-consuming, error-prone, and impractical.
4. Develop a clear understanding of data requirements: which formats and standards do your recipients require? What must be considered (version, format, structure, …)?
5. Use specialized software solutions: data syndication middleware between the PIM, other systems, and sales channels helps consolidate datasets, generate required data formats, validate them, and deliver them to target channels.

In other words: In an increasingly complex e-commerce world, data syndication solutions provide the flexibility to turn individual product data into potential revenue drivers across every target channel.