Data Product

Knowledge Center

Data & Cloud Services

Data Product Explained

In a nutshell

A data product makes data available to business units in a structured, understandable, and reusable format, thereby laying the foundation for analyses, planning processes, and AI applications. It encompasses not only the data itself but also definitions, responsibilities, quality rules, and a clear business context. In the SAP Business Data Cloud, data products play a central role in the provision and use of data from SAP and non-SAP systems.

Why are data products important?

Many companies today have large amounts of data, but can only utilize it at considerable cost. Often, there are differing definitions of the same metric, redundant data sets, or complex ETL processes. The result is long project timelines, a high level of coordination effort, and a lack of trust in the data.

Data products take a different approach: data is treated as a product. This means that it is described in technical terms, documented, quality-assured, and made available to different user groups. Users do not have to reprocess or reinterpret the data each time; instead, they can access standardized and reusable data products.

This approach becomes particularly important in the context of modern data platforms. SAP Business Data Cloud, Databricks, Microsoft Fabric, and data mesh architectures are all increasingly relying on data products as building blocks of a scalable data landscape. Instead of individual data silos, reusable data products are created that can be shared by analytics, planning, AI applications, and operational processes.  

Furthermore, data products form an important foundation for the use of artificial intelligence. AI applications require consistent, domain-expert, and trustworthy data. Without clearly defined data products, the risk of flawed analyses and unreliable AI results increases significantly.  

How does celver AG provide support?

Many companies are currently grappling with the question of what a modern target architecture for data, analytics, planning, and AI should look like. Data products often play a central role in this context, but are frequently interpreted in different ways.

celver AG helps companies integrate data products into their existing architecture from both a business and technical perspective. We consider both SAP and non-SAP systems, as well as platforms such as SAP Business Data Cloud, SAP Datasphere, Databricks, and Microsoft Fabric. The goal is to design data products in such a way that they can be shared across different business units and technologies.  

Typical questions from our customers include:

  • What types of data are suitable for a data product?
  • Who is responsible for the technical aspects?
  • What is the difference between data models and data products?
  • How can data products be used for analytics, planning, and AI?
  • What role do data products play in SAP Business Data Cloud or Databricks?
  • How can SAP and non-SAP data be combined into shared data products?  

Our focus is not on individual technologies, but rather on a data architecture that is sustainable in the long term and supports business units, IT, and future AI applications alike.

Frequently Asked Questions

Are data products just an SAP issue?

No. Data products are a general architectural concept and are supported by platforms such as SAP Business Data Cloud, Databricks, and Microsoft Fabric alike.

What is the difference between a data product and a traditional report?

A report presents information. A data product provides the underlying data, including its business context, quality rules, and responsibilities, for a wide variety of use cases.

Why Are Data Products Becoming More Important for AI?

AI systems require consistent and domain-specific data. Data products provide the necessary foundation for making data trustworthy and reusable for analytics, planning, and AI.

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