{"id":23505,"date":"2026-04-23T10:30:32","date_gmt":"2026-04-23T08:30:32","guid":{"rendered":"https:\/\/www.atamya.com\/blog\/target-data-quality\/"},"modified":"2026-04-24T15:46:22","modified_gmt":"2026-04-24T13:46:22","slug":"target-data-quality","status":"publish","type":"post","link":"https:\/\/www.atamya.com\/en\/blog\/target-data-quality\/","title":{"rendered":"Target Data Quality: Why Product Data Eventually Becomes a Question of Cost"},"content":{"rendered":"\n
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When Data Quality Becomes Economic: Product Data between Efficiency and Cost<\/h2>\n

Product Information betwee Growth and Cost Pressure<\/span><\/h3>\n

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 \u2013 and, therefore, with costs across the entire product data proces.<\/p>\n

Many companies invest into data quality \u2013 usually, however, the effect remains limited because they are viewed through a lense mucht too technical. It\u2019s not about making product data merely \u201ccomplete\u201d or \u201cappealing,\u201d but about understanding how data processes do, in fact, influence a product\u2019s actual costs.<\/p>\n

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 \u2013 and how can they be reduced systematically?<\/p>\n

And this is where target data quality<\/strong> 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.<\/p>\n

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The Shift in Perspective: Data Qualit\u00e4t as an Efficiency Driver<\/span><\/h3>\n

When data quality is reduced to an exclusively technical indicator, it usually remains something \u201cnice-to-have.\u201d 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.<\/p>\n

Accordingly, the central question is no longer: \u201cHow good is our data?,\u201d but: \u201cWhat does this quality of data cost \u2013 today and in the future?\u201d<\/p>\n

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 \u2013 and this grows as the variety of products and channels increases.<\/p>\n

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.<\/p>\n

At the same time, the focus in PIM shifts too: away from the pure effect of product content on the outside \u2013 e.g., reach, content quality, or channel coverage \u2013 towards efficiency of the underlying data organization. It\u2019s not only decisive what product data can accomplish but also effort required to consistently and sustainably enable and maintain such quality.<\/p>\n

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Why Target Data Quality does not Automatically Lower Costs<\/span><\/h3>\n

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.<\/p>\n

In many\u00a0PIM<\/a> 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 \u2013 such as unsuitable data models, manual maintenance, or numerous edge cases. In other words: better data on the basis of worse structures stay inefficient.<\/p>\n

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<\/strong> \u2013 i.e., where target data quality is realized with as little operative expenses as possible.<\/p>\n

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.<\/p>\n

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A Modern PIM as the Foundation for Efficient Product Data Processes<\/span><\/h3>\n

A Product Information Management (PIM) system such as ATAMYA Product Cloud<\/a> 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.<\/p>\n

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.<\/p>\n

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.<\/p>\n

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From Concept to Reality: This is How Target Data Quality is Rendered Operative<\/span><\/h3>\n

Target Data Quality gewinnt erst dann an Wirkung, wenn sie fest in die t\u00e4glichen Routinen integriert wird. Es geht nicht um ein einmaliges Projektziel, sondern um eine Steuerungsgr\u00f6\u00dfe, die kontinuierlich gemessen, reflektiert und verbessert wird.<\/p>\n

Ein pragmatischer Einstieg beginnt damit, die wirtschaftliche Realit\u00e4t sichtbar zu machen: Wo entstehen tats\u00e4chlich Aufw\u00e4nde? Welche Schleifen wiederholen sich? Welche Verz\u00f6gerungen wirken sich auf Time-to-Market und Ressourcenplanung aus?<\/p>\n

In der Praxis bedeutet das konkret:<\/strong><\/p>\n