Target Data Quality: Why Product Data Eventually Becomes a Question of Cost

When Data Quality Becomes Economic: Product Data between Efficiency and Cost

Product Information betwee Growth and Cost Pressure

Nowadays, prodcut data is way more than units of information in a system. The decide over productivity, time-to-market, and competitiveness. At the same time, they are more and more becoming a cost factor: this is because each improvement in data quality comes at the expense of operative effort – and, therefore, with costs across the entire product data proces.

Many companies invest into data quality – usually, however, the effect remains limited because they are viewed through a lense mucht too technical. It’s not about making product data merely “complete” or “appealing,” but about understanding how data processes do, in fact, influence a product’s actual costs.

It may very well be true that classic metrics such as attribute coverage and error rate are indicative of the state of your data. This, however, does not answer the decisive question: what operative costs are created by data quality – and how can they be reduced systematically?

And this is where target data quality comes into play as a new method of going about it. Instead of viewing data quality as an isolated score it is embedded in its measureable, controlable, and economically meaningful context.

 

The Shift in Perspective: Data Qualität as an Efficiency Driver

When data quality is reduced to an exclusively technical indicator, it usually remains something “nice-to-have.” Only when it is put in relation with the costs per product and the underlying structures does it develop into a real corporate control variable.

Accordingly, the central question is no longer: “How good is our data?,” but: “What does this quality of data cost – today and in the future?”

This is because lackluster data quality leads not only to errors. It causes, before anything else, friction in day-to-day business: corrections, feedback loops, special processes, and delays when it comes to the market launch of new products. Such efforts are seldom documented as an explicit cost factor. Instead, it is distributed among various departments such as IT, external service providers, and process costs – and this grows as the variety of products and channels increases.

Especially in ecommerce, thise effects become particularly visible: delays in content distribution, inconsistent product information across various channels, or missing attributes directly influence the market launch time, conversion rate, and customer experience.

At the same time, the focus in PIM shifts too: away from the pure effect of product content on the outside – e.g., reach, content quality, or channel coverage – towards efficiency of the underlying data organization. It’s not only decisive what product data can accomplish but also effort required to consistently and sustainably enable and maintain such quality.

 

Why Target Data Quality does not Automatically Lower Costs

What matters most in this process: target data quality does not automatically lead to a decrease in costs. Many organizations may very well reach a much higher data quality but, in doing so, also create consistently high operativ efforts because inefficient structures are still looming large.

In many PIM systems, this generates a paradoxical result: the data qualiy increases while the costs per product stay high or grow even futher. The reasonfor this is that better data is often times grounded upon existing structures – such as unsuitable data models, manual maintenance, or numerous edge cases. In other words: better data on the basis of worse structures stay inefficient.

Targat data quality may be reached in this scenario, but merely indicates a stable cost plateau. However, the actual economic objective does lie in reaching target data quality but in the minimum cost across the entire product data processes – i.e., where target data quality is realized with as little operative expenses as possible.

Only when the data model, workflows, and automation are continuously developed with a strategy in mind, does the cost curve change sustainably. Post-editing decreases, special exceptions vanish, and product data can be organizes in a significantly more scalable manner. This makes target data quality not the end point but the mediator for a more efficient operation model.

 

A Modern PIM as the Foundation for Efficient Product Data Processes

A Product Information Management (PIM) system such as ATAMYA Product Cloud is way more than a mere data container. It is a central platofrm for not only distributing product data but also controlling and orchestrating it in a targeted manner.

Decisive is the interplay of systems, data model, and processes. Modern PIM solutions create the basis for scaling data processes automatically and scaleably. With a PIM solution, data models can be structured, responsibilities can be clearly defined, and returning tasks can be automated. This way, more transparency is created and consistent data is made available across all channels, whereas the communication and coordination expenses are minimized.

When PIM is understood as a control instrument, it becomes clear why target data quality is not technocratic but business-relevant: it defines the rules for data models, workflows, and ownership so that product information is transformed into operative strength.

 

From Concept to Reality: This is How Target Data Quality is Rendered Operative

Target Data Quality gewinnt erst dann an Wirkung, wenn sie fest in die täglichen Routinen integriert wird. Es geht nicht um ein einmaliges Projektziel, sondern um eine Steuerungsgröße, die kontinuierlich gemessen, reflektiert und verbessert wird.

Ein pragmatischer Einstieg beginnt damit, die wirtschaftliche Realität sichtbar zu machen: Wo entstehen tatsächlich Aufwände? Welche Schleifen wiederholen sich? Welche Verzögerungen wirken sich auf Time-to-Market und Ressourcenplanung aus?

In der Praxis bedeutet das konkret:

• Kosten pro Produkt transparent machen – inklusive Nacharbeit und Koordinationsaufwand
• Engpässe identifizieren im Datenmodell, in Workflows oder Verantwortlichkeiten
• Automatisierung gezielt nutzen, um manuelle Tätigkeiten zu reduzieren
• Datenhoheiten klar definieren, um Eskalationsschleifen zu vermeiden
• KPIs etablieren, die Prozessleistung und nicht nur Datenstatus abbilden

Auf dieser Basis entsteht Schritt für Schritt ein belastbarer organisatorischer Rahmen zur Steuerung von Produktdatenprozessen. Modellierung, Governance, Automatisierung und Organisation greifen ineinander – mit dem Ziel, vermeidbare Kosten systematisch zu senken.

Target Data Quality wird so vom theoretischen Konzept zur operativen Realität: Sie priorisiert die richtigen Hebel und ermöglicht fundierte Investitionsentscheidungen.

 

What Companies Gain

Companies that implement target data quality consistently report about empirically measurable improvements: lower costs for rework, faster product releases, less exceptions, and significantly more collaboration between departments and IT.

Product data develops

Produktdaten entwickeln sich dadurch vom operativen Pflegeaufwand zu einem echten Business Asset – und werden zu einem strategischen Enabler für skalierbare und stabile Datenprozesse.

 

Conclusion: Target Data Quality is a Cost and Process Model – Not a Data Project

Wer Datenqualität ausschließlich über technische Scores bewertet, optimiert häufig nur Symptome. Wird sie hingegen mit den realen Kosten pro Produkt verknüpft, entsteht ein Ansatz, der Datenqualität, Organisation und Wirtschaftlichkeit zusammenführt. Erst wenn Datenprozesse effizient funktionieren, entsteht echte Datenqualität, nicht umgekehrt.

Gerade in einem Umfeld wachsender Komplexität – mehr Produkte, mehr Kanäle und steigende Anforderungen – wird die strukturierte Organisation von Produktdaten damit zu einem zentralen Faktor für Skalierbarkeit, stabile Abläufe und nachhaltige Produktivität.

Target Data Quality bedeutet nicht, Produktinformationen möglichst perfekt zu machen. Entscheidend ist vielmehr, den Punkt zu erreichen, an dem Datenqualität keine vermeidbaren Kosten mehr verursacht und die zugrunde liegenden Strukturen wirtschaftlich tragfähig sind.