Data Mesh
Data & Cloud Services
In a nutshell
Data Mesh is an architectural and organizational approach for modern data platforms. Instead of having all data centrally managed by a single data or BI department, business units take responsibility for their own data. They make their data available in the form of standardized and reusable data products.
The goal of Data Mesh is to bring data closer to business units, improve data quality, and increase the scalability of modern data platforms.
Why is Data Mesh important?
In recent years, many companies have built extensive data warehouse or data lake solutions. However, as data volumes increase, these approaches often reach their organizational limits. Business units need new data or adjustments and must request them from a central data team. This results in long wait times and a growing backlog.
Data Mesh takes a different approach. Responsibility for data is assigned to the departments where the expertise lies. For example, Sales is responsible for sales data, Supply Chain is responsible for logistics data, and Finance is responsible for financial data. Each department makes its data available to the entire company in the form of high-quality data products.
This approach is supported in particular by modern platforms such as SAP Business Data Cloud, Databricks, and Microsoft Fabric . These platforms enable the technical deployment and sharing of data products across departmental and system boundaries. This topic is also gaining importance in the context of AI, as modern AI applications rely on consistent, documented, and trustworthy data.
For many companies, Data Mesh is therefore less of a technology project and more of a new operational model for data.
How does celver AG provide support?
Data Mesh is often viewed as a purely technical architectural concept. In practice, however, many initiatives fail not because of the technology, but because of a lack of roles, responsibilities, and governance structures.
celver AG helps companies build modern data organizations and define the appropriate architectural principles for analytics, planning, and AI. In doing so, we address both organizational and technical issues and integrate modern platforms such as SAP Business Data Cloud, Databricks, and Microsoft Fabric with clear governance and operational models.
Typical questions from our customers include:
- Does Data Mesh make sense for our company?
- What data should be organized as data products?
- Who is responsible for data quality?
- How do data meshes differ from traditional data warehouses?
- What role do SAP Business Data Cloud, Databricks, and Microsoft Fabric play?
- How can academic departments be more closely involved in data stewardship?
Our focus is on pragmatic solutions. Not every company needs a full-fledged data mesh model. Often, clearly defined data products, central governance rules, and well-defined responsibilities are enough to significantly improve data organization.
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Frequently Asked Questions
Data Mesh refers to organizational responsibility for data. Data Fabric, on the other hand, refers to the technical architecture that integrates and delivers data from various sources. Both approaches can complement each other effectively.
No. Data Mesh is primarily an organizational and governance concept. Technological platforms such as SAP Business Data Cloud, Databricks, or Microsoft Fabric can support Data Mesh approaches, but they are not the actual core of the concept.
A data product is a single data product with business significance and clearly defined responsibilities. Data Mesh describes the overarching organizational model in which many data products are provided by different business units.
No. Many companies are already benefiting from building individual data products and clearly defining responsibilities. A full-fledged data mesh usually makes sense only for larger and more complex organizations.
