What is Microsoft Fabric?

Microsoft Fabric is a SaaS analytics platform from Microsoft that brings together data integration, data engineering, data warehousing, real-time analytics, and data science in a single environment. For organizations, Fabric can provide the foundation for modern business intelligence architectures. Ultimately, however, its value depends on how data, metrics, and analytics are structured from a business perspective. Bissantz helps organizations make effective use of Microsoft Fabric.

Microsoft Fabric was introduced in 2023 to bring previously separate Microsoft analytics tools, such as Azure Data Factory, Azure Synapse Analytics, and Power BI, together on a single platform. Instead of relying on multiple standalone products, each with its own infrastructure, permission models, and storage locations, Microsoft Fabric uses a shared compute and storage model. All workloads—from data integration and processing to visualization—can access the same underlying data.

From a business perspective, Microsoft Fabric is aimed at organizations that want to consolidate their data landscape: moving away from scattered Excel spreadsheets, parallel databases, and separately maintained metrics toward a single data platform with centralized governance. Because Fabric is delivered as a fully managed SaaS service, organizations no longer need to manage infrastructure details such as resource groups, regions, or server capacity.

One of the core components of Microsoft Fabric is OneLake. OneLake is a tenant-wide logical data lake for an organization’s analytics and AI data. It is based on Azure Data Lake Storage and is designed to reduce data silos by allowing different Fabric workloads to access a shared data foundation.

Organizations can use a lakehouse architecture to process both structured and unstructured data. Fabric also supports connections to external storage, meaning that data does not necessarily have to be physically copied.

Microsoft Fabric consists of several workloads, each designed for a specific role or task while operating on the same underlying data foundation.

The key benefit of Microsoft Fabric is its ability to unify data landscapes that are often fragmented. In many organizations, data is spread across ERP systems, CRM applications, databases, files, and cloud services. To enable reliable analytics, this data first needs to be located, integrated, prepared, and brought together from a business perspective.

Microsoft Fabric provides various building blocks for this within a common environment. Data Factory supports data integration, OneLake provides centralized storage, and Power BI supports analytics and visualization.

Fabric can be particularly useful for organizations that:

• want to move more of their data platform to the cloud,
• need to bring together data from different sources,
• want to make data centrally available for BI and analytics,
• want to run data processing and analytics on a shared platform,
• need to analyze real-time data alongside historical data.

An important distinction, however, is that a modern data platform alone does not create effective controlling or management reporting. Only when technically available data is combined with well-defined metrics, a robust data model, meaningful analytics, and a clear connection to business decisions does it become useful management information.

Microsoft Fabric can serve as the technical foundation for business intelligence. The platform focuses on processing, storing, and analyzing data. Power BI is integrated into Fabric to provide interactive visualizations and reports.

From a controlling perspective, however, business intelligence goes further. Key questions include:

• Which metrics are relevant for managing the business?
• How are those metrics defined from a business perspective?
• Which variances are significant?
• What are the underlying causes?
• What actions should follow from an insight?

This is where a data platform differs from a business management and decision-support solution. Microsoft Fabric can provide the technical data foundation. A BI and decision intelligence solution such as DeltaMaster from Bissantz, by contrast, focuses on analytics, planning, reporting, and translating data into information that supports business decisions.

A modern platform does not automatically solve the business and organizational challenges of a BI environment. Common mistakes therefore have less to do with the technology itself than with how it is implemented and embedded within the organization. Some typical examples include:

1. Technology before business requirements

If storage, workspaces, and data pipelines are built first without defining the metrics and management questions that will ultimately be needed, the result can easily be a technically powerful data environment that provides little value to the business.

Better approach: Define the relevant business questions, metrics, and analytical requirements first, and derive the technical architecture from them.

2. Confusing data integration with metric logic

Bringing data sources together does not automatically create a unified view of the business. Different definitions of revenue, margin, or order intake can persist even when all data is stored centrally.

Better approach: Treat data integration and business metric logic as two distinct but closely connected disciplines.

3. Too many tools and reports

A platform can create new possibilities, but it can also lead to a growing number of data products, dashboards, and reports.

Better approach: Establish clear responsibilities, standardized metrics, and a consistent focus on the decisions users need to make.

4. Addressing governance too late

Access rights, data lineage, and responsibilities should not be addressed only after the technical environment has been built. Fabric provides centralized governance and catalog capabilities for this purpose.

Better approach: Incorporate governance, roles, and data ownership into the architecture from the outset.

A food company wants to modernize its established BI environment. Sales, finance, and production data are currently spread across different systems and data warehouse structures, making consistent reporting and cross-functional analytics time-consuming.

Bissantz first supports the company in developing a target vision for its data platform and migrating the existing architecture to Microsoft Fabric. This includes analyzing data sources, designing data flows and the central data model, and incorporating the requirements of controlling and the business functions. The OneLake and medallion approach provides a structured foundation for further data processing and use.

The result is an integrated data platform that provides consistent data for reporting, analytics and planning. The project therefore combines technical modernization with tangible business value: The company gains a future-ready data architecture and efficient BI processes, reducing the effort and time required for day-to-day controlling activities.

Microsoft Fabric brings together data integration, data warehousing, data engineering, real-time analytics, data science, and Power BI on a single SaaS platform, with OneLake serving as the shared data foundation. For organizations, this means fewer data silos, more consistent metrics, and faster implementation of new reporting and planning requirements. When introduced with a clear architecture and governance strategy, the platform provides a solid foundation for analytics, reporting, and planning from a single environment. This is where Bissantz’s consulting and services come in.

Single Source of Truth Controlling Business Intelligence