Data Fabric & Data Governance

Data Fabric & Data Governance: The foundation for a robust data strategy

Companies are faced with the task of efficiently providing huge amounts of data from a wide variety of sources. Many companies struggle with long throughput times, especially in the analytics process: it often takes a long time from the initial data request to the finished dashboard. Different systems, fragmented ETL processes, unclear responsibilities, and a lack of standards often lead to delays—and thus become a bottleneck for data-driven decisions.

Data Fabric: The central platform for agility and self-service

The data fabric approach solves these problems by creating a unified platform for integrating, storing, and analyzing data. Solutions such as Microsoft Fabric or SAP Business Data Cloud enable end-to-end data processing, thereby promoting self-service for business departments. The result: less complexity, faster insights, and better collaboration between IT and business departments.

Data governance: The rules for trust and security

However, the centralization of data processing also increases responsibility. The most important question here is: Who is allowed to do what? Whereas separate systems used to automatically provide a certain level of access control, today, in theory, anyone could access everything. A clear set of rules is therefore needed. In this context, data governance defines clear rules for transparency, security, reliability, and compliance.

An effective governance framework includes:

• Data quality: Ensuring accuracy, completeness, and consistency

• Data security: Protection against unauthorized access and misuse

• Responsibilities: Clear roles and responsibilities

• Life cycle management: Controlling the creation, use, and deletion of data

• Data architecture: Uniform standards for structure and formats

Effective data governance requires not only clear rules and responsibilities, but also appropriate technological tools that support these principles in practice. Modern platforms such as Microsoft Fabric are therefore increasingly integrating governance functions directly into their architecture—such as centralized access protection, metadata management, and role-based approvals. In the SAP environment, tools such as Collibra, Databricks, and SAP Datasphere complement governance aspects such as data cataloging, quality management, and compliance.

Conclusion: Architecture meets responsibility

The introduction of a modern data fabric brings many advantages. However, it is only through clear governance that a flexible architecture can become a trustworthy data landscape. In this context, the data fabric approach provides the technological foundation, while data governance provides the regulatory framework. Through their interaction, both concepts unfold their full potential.

Our tip: Check how well your organization is positioned—and where there is still untapped potential. Are you already using a central data platform? Are there documented governance rules? Are roles and responsibilities clearly defined in all phases? If not, now is the right time to develop a structured roadmap. We would be happy to work with you to develop one— just get in touch!

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