Product Data, Relationships – or: Quantity and Quality
Product Data and Relationships
Don't worry, we haven’t become therapists. But today’s article is about relationships — more precisely, about what product data has to do with relationship knowledge, and how both topics play a role in marketing.
First of all, everyone in marketing can imagine what a “product” is — a product detail page on a website, maybe a page in a print catalog… and that product has a name, an order number, and many properties used to describe it. How well this information is standardized and structured is a critical factor in how effectively the data can be used — and how well the products sell.
Are the right keywords included in the product text? Does the name of my technical attribute match the common language of the industry? Have I described my entire portfolio in a way that makes comparison easy for users? Is my data model expandable enough to quickly respond to changes in the market or new requirements from my systems? And at the same time, have I standardized everything well enough that I can feed multiple output systems with minimal friction? What about image variants?
So far, so good — these are all topics we've been working on for years. Today, here are some thoughts on two trends we’re currently observing.
Trend 1: Increasing Amount of Information per Product Record
On the one hand, we’re seeing a strong increase in the volume of content within our systems over the past few years: more media assets are being produced, more text is being written — the goal is to store and link as much additional information as possible per product record. Archived products continue to be published, images are stored in higher resolutions, and under the pressure of digitalization, the desire to consolidate as much as possible into a single system is growing — while streamlining output processes.
The new content, in turn, has its own creation paths and requires logic for correct assignment.
Want an example? The new product image for item A is also used as an “similar image” for item B. Accessory X and product Y belong together — but for users, this information is only truly useful if they know even more: for example, after how many hours of operation a wear part needs to be replaced. Or in which assembly group the part appears and how often. And so on!
Trend 2: Data Needs to Get Smarter
In recent years, we’ve also observed a growing importance of service-driven business models — or rather, that they increasingly need to be digitized. For example, quick access to the correct spare parts for a service technician on site is extremely important — it saves both time and money. Information about “compatible accessories,” including replacement intervals and operating times, not only leads to higher shop sales in some cases, but also helps the user plan quantities accurately within their own company — and that, in turn, becomes a strong argument in sales.
Data Foundation, Object Relationships, and Agility
The topics just described are prime examples of “relational knowledge” — the ability to model relationships between objects. Is it an accessory or a spare part? What’s the context? Do I mean the same thing across all countries? And what information “belongs to the relationship” rather than to either of the two linked objects? Could some of these relational insights even be used to automate tasks I’m currently doing manually?
As always, the most important thing is to understand the reality you’re trying to map.
While this may sound a bit abstract, it’s something we’ve known for decades in the automotive industry, where no manufacturer or workshop has managed without this type of information for over 20 years. It’s only logical that the global demand for data quality and structure is rising — and that’s closely tied to the possibilities it unlocks.
- Data quality and completeness are competitive factors — especially when industry standards like Tecdoc, ETIM, ARGE, and Datanorm provide measurable benchmarks.
- Generally — and outside standardized frameworks — “complete” can never be a permanent state in an era of rapidly changing requirements. That’s why “flexible” is an equally essential attribute.
Most new business models and projects require exactly that: a solid data foundation on the one hand and agility on the other. Which brings us back to that classic quote from Lothar Schmidt: “Relationships only hurt those who don’t have them.” Though he probably wasn’t thinking about product data at the time...