04.09.2024 | BJÖRN BAYARD
Data quality is a much-discussed topic in many companies. When problems such as inefficient information flows, high return rates, or dissatisfied customers or partners arise, poor data quality is often quickly identified as the likely root of all evil.
However, upon closer inspection of the actual causes, the real problems emerge: very few companies have a clear idea of precisely what data quality means, how it is anchored in their own organisation in terms of processes, and what responsibilities arise in this context. In this blog article, we get to the bottom of this important topic, look at the complexity of the term ‘data quality’, and explain the consequences for technology, processes, and corporate culture.
Which content elements does data quality affect?
The term data quality on its own is a little misleading because for many it is limited to the granular structured data that is generated as part of product communication. In particular, this includes product master data that is stored in ERP and PIM systems, for example. But it also includes additional product information and descriptions that are stored in PIM systems.
However, product communication requires much more than this: digital content such as product images, videos, documents such as certificates, labels, or instructions for use, 3D visualisations, logos and other graphic elements, together with structured product information, forms the basis for transparent and comprehensive product communication. In principle, data quality therefore applies to all product content.
Which content elements does data quality affect?
The term data quality on its own is a little misleading because for many it is limited to the granular structured data that is generated as part of product communication. In particular, this includes product master data that is stored in ERP and PIM systems, for example. But it also includes additional product information and descriptions that are stored in PIM systems.
However, product communication requires much more than this: digital content such as product images, videos, documents such as certificates, labels, or instructions for use, 3D visualisations, logos and other graphic elements, together with structured product information, forms the basis for transparent and comprehensive product communication. In principle, data quality therefore applies to all product content.
General quality rules
There are a number of quality rules that are generally valid and globally applicable. Depending on the type or intended use of the data, however, different characteristics apply to the interpretation of the specific quality requirements. Management reporting, for example, places fewer demands on the completeness of product data than marketing does. It is therefore essential to always place the concept of data quality in the context of its use.
The general data quality rules include
• Actuality
The data is not out of date – it is updated immediately in the event of changes.
• Correctness
The information is correct.
• Consistency
The data is presented clearly and always in the same way.
• Completeness
All information and values belonging to the data are mapped.
• Credibility
The trustworthiness of the data, the processing system, and the data source are a further quality feature.
• Unambiguity
It is also important that the data is interpreted in the same technically correct way by every user in the organisation.
• Added value
The utilisation of the data creates quantifiable added value for the company.
General quality rules
There are a number of quality rules that are generally valid and globally applicable. Depending on the type or intended use of the data, however, different characteristics apply to the interpretation of the specific quality requirements. Management reporting, for example, places fewer demands on the completeness of product data than marketing does. It is therefore essential to always place the concept of data quality in the context of its use.
The general data quality rules include
• Actuality
The data is not out of date – it is updated immediately in the event of changes.
• Correctness
The information is correct.
• Consistency
The data is presented clearly and always in the same way.
• Completeness
All information and values belonging to the data are mapped.
• Credibility
The trustworthiness of the data, the processing system, and the data source are a further quality feature.
• Unambiguity
It is also important that the data is interpreted in the same technically correct way by every user in the organisation.
• Added value
The utilisation of the data creates quantifiable added value for the company.
GS1 GDSN and data quality
The purpose of the GS1 GDSN data exchange network is to standardise and optimise cross-company data exchange processes. To achieve this, the network uses generally recognised quality rules that cover the requirements of the various industries. For example, the quality catalogue that the HCDP (Healthcare Content Data Portal) uses for its medical sector participants is different from the data pools used for the food retail sector, where, for example, the correct presentation of food labels determines whether or not a product ends up on retailers’ shelves.
For manufacturers who share their product data with their retail partners via global data pools, it therefore makes sense to take the respective quality rules into account when creating the product data in the PIM system.
At this point, it should also be pointed out once again that the GS1 GDSN and country-specific quality systems based on it (such as DQX in Germany) do not increase the quality of the product data, but only determine or document the level of quality that the data provider has supplied! All data providers must be aware that they have to invest in processes and systems so that they can deliver the quality of product content that their data consumers expect.
This process is highly comparable to the process of ensuring the quality of physical products. Here too, the manufacturer must consider how to organise its production processes so that its products always have the same quality that the retailer or consumer expects.
GS1 GDSN and data quality
The purpose of the GS1 GDSN data exchange network is to standardise and optimise cross-company data exchange processes. To achieve this, the network uses generally recognised quality rules that cover the requirements of the various industries. For example, the quality catalogue that the HCDP (Healthcare Content Data Portal) uses for its medical sector participants is different from the data pools used for the food retail sector, where, for example, the correct presentation of food labels determines whether or not a product ends up on retailers’ shelves.
For manufacturers who share their product data with their retail partners via global data pools, it therefore makes sense to take the respective quality rules into account when creating the product data in the PIM system.
At this point, it should also be pointed out once again that the GS1 GDSN and country-specific quality systems based on it (such as DQX in Germany) do not increase the quality of the product data, but only determine or document the level of quality that the data provider has supplied! All data providers must be aware that they have to invest in processes and systems so that they can deliver the quality of product content that their data consumers expect.
This process is highly comparable to the process of ensuring the quality of physical products. Here too, the manufacturer must consider how to organise its production processes so that its products always have the same quality that the retailer or consumer expects.
Requirements from D2C and retail channels
In addition to product safety, which is particularly essential in sectors such as healthcare and food retail, product communication also benefits from the quality of the product content provided. Each communication channel places specific requirements on the product information and media content to be used, their length, formats, cuts, and file sizes. The respective customer journey is also very specific and should be taken into account in the channel-specific preparation of the product content.
The most important D2C (direct-to-customer) channels include your own online shop, app, website, and social media channels. However, third-party channels such as online marketplaces or retailers’ online shops and social commerce channels must also be supplied with appropriately optimised product content.
Requirements from D2C and retail channels
In addition to product safety, which is particularly essential in sectors such as healthcare and food retail, product communication also benefits from the quality of the product content provided. Each communication channel places specific requirements on the product information and media content to be used, their length, formats, cuts, and file sizes. The respective customer journey is also very specific and should be taken into account in the channel-specific preparation of the product content.
The most important D2C (direct-to-customer) channels include your own online shop, app, website, and social media channels. However, third-party channels such as online marketplaces or retailers’ online shops and social commerce channels must also be supplied with appropriately optimised product content.
Data quality – an unmanageable regulatory construct?
All of this means that data quality is becoming an increasingly difficult construct to grasp if companies do not respond with a comprehensive strategy and concept. Especially for larger manufacturers with a broad product range that supply a large number of retail partners and also use their own D2C channels, the topic of data quality is constantly on the agenda.
With the help of comprehensive data governance, all rules – whether from global data pools, legislators, or trading partners – can be clearly documented and made available centrally. However, by no means is this the end of the story: it is necessary to implement data governance in every
data process and thus heighten employee awareness. Automated validation processes by the PIM system help to ensure compliance with quality rules.
All of this also has important implications for the corporate culture, as the topic of data can no longer be dealt with in isolation by a dedicated department but must be introduced and understood as a fundamental asset in all areas of the company. Establishing data literacy, i.e., a shared understanding of data and its significance for the company’s own work processes, is therefore one of the most important tasks for these companies.
Data quality – an unmanageable regulatory construct?
All of this means that data quality is becoming an increasingly difficult construct to grasp if companies do not respond with a comprehensive strategy and concept. Especially for larger manufacturers with a broad product range that supply a large number of retail partners and also use their own D2C channels, the topic of data quality is constantly on the agenda.
With the help of comprehensive data governance, all rules – whether from global data pools, legislators, or trading partners – can be clearly documented and made available centrally. However, by no means is this the end of the story: it is necessary to implement data governance in every data process and thus heighten employee awareness. Automated validation processes by the PIM system help to ensure compliance with quality rules.
All of this also has important implications for the corporate culture, as the topic of data can no longer be dealt with in isolation by a dedicated department but must be introduced and understood as a fundamental asset in all areas of the company. Establishing data literacy, i.e., a shared understanding of data and its significance for the company’s own work processes, is therefore one of the most important tasks for these companies.
Would you like to learn more about data quality?
Best regards – Björn Bayard