At a glance
- Brings together multiple data and analytics capabilities on a single platform.
- OneLake serves as a shared logical data store for Fabric workloads.
- Data Factory supports data integration and data preparation.
- Power BI is integrated for analytics, visualization, and reporting.
- For controlling and management reporting, a consistent metrics framework and business logic are just as important as the underlying technical platform.
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Microsoft Fabric definition
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.
„Bissantz helped us consolidate our distributed sales, finance, and production data in Microsoft Fabric. What was decisive was not only that the technology was right, but that our KPIs are finally defined consistently.“Head of Controlling
Food producer
What is OneLake in the context of Microsoft Fabric?
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.
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What are the key components of Microsoft Fabric?
Microsoft Fabric consists of several workloads, each designed for a specific role or task while operating on the same underlying data foundation.
| Workload | Function |
|---|---|
| Power BI | Connects to data sources and provides interactive reports and dashboards for business users |
| Data Factory | Data integration with more than 200 connectors for on-premises and cloud sources |
| Data Engineering | Processes large data volumes using Apache Spark, notebooks, and pipelines |
| Data Warehouse | SQL-based data warehousing with separate scaling of storage and compute, natively using the Delta Lake format |
| Data Science | Builds, trains, and operates machine learning models, integrated with Azure Machine Learning |
| Real-Time Intelligence | Processes and analyzes streaming data, such as data from IoT sensors or application logs |
| Databases | Transactional databases in Fabric and mirroring of existing data sets to OneLake |
| Fabric IQ | Unifies business semantics across ontologies, data agents, and semantic models |
Why do organizations need Microsoft Fabric?
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.
What does Microsoft Fabric mean for business intelligence?
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.
Microsoft Fabric compared to a traditional BI architecture
| Aspect | Traditional BI architecture | Microsoft Fabric |
|---|---|---|
| Data storage | Multiple storage locations per tool or department | One central OneLake per tenant |
| Integration | Individual tools with separate interfaces | Shared compute and storage model for all workloads |
| Licensing | Separate licenses per product | A single capacity-based licensing model (F-SKUs) for all workloads |
| Governance | Inconsistent permission concepts per system | Central governance via Microsoft Purview, tenant-wide |
| Scaling | Scaling per individual system | Shared, cross-workload scaling via capacity units |
What are typical mistakes when introducing Microsoft Fabric?
A modern platform does not automatically solve the functional and organizational problems of a BI landscape. Common mistakes therefore lie less in the technology itself than in its integration. Some examples are the following:
1. Technology before function
If storage, workspaces, and data pipelines are built first, without defining the KPIs and management questions needed later, it is easy to create a technically powerful but functionally unhelpful data landscape.
Better: First define the relevant business questions, KPIs, and analytical requirements, and derive the technical architecture from them.
2. Confusing data integration with KPI logic
Merging data sources does not yet establish a unified view of the company. Different definitions of revenue, margin, or incoming orders can persist despite central storage.
Better: Treat data integration and functional KPI logic as two connected but distinct tasks.
3. Too many tools and reports
A platform can create new opportunities, but it can also lead to a larger number of data products, dashboards, and reports.
Better: Clear responsibilities, standardized KPIs, and a consistent focus on the decisions of the users.
4. Considering governance only later
Access rights, data lineage, and responsibilities should not be clarified only after the technical setup. Fabric provides central governance and catalog functions for this purpose.
Better: Consider governance, roles, and data responsibility already when building the architecture.
Practical example: Microsoft Fabric and Bissantz
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.
| Function | Benefit |
|---|---|
| Target vision for the data platform | clear alignment of the architecture with controlling and business department requirements |
| Migration to Microsoft Fabric | modernization of grown BI landscapes onto a unified platform |
| OneLake and medallion approach | structured foundation for processing and using the data |
| Central data model | consistent data for reporting, analysis and planning |
| DeltaMaster for Decision Intelligence | translation of technical data into information relevant for management |
„We wanted to move our BI landscape to the cloud, and with Bissantz we first clarified the business-related management questions before building the architecture on Microsoft Fabric. Reporting and planning have run noticeably more efficiently ever since.“Head of IT
Industrial company
Frequently asked questions
What is Microsoft Fabric in simple terms?
Microsoft Fabric is a cloud platform that brings the entire data lifecycle together in one place: collecting, preparing, storing, analyzing, and presenting data in reports. Instead of working with multiple standalone applications, users work in a shared environment with a common data foundation.
What is the difference between Microsoft Fabric and Power BI?
Power BI is a component of Microsoft Fabric and is used for reports and dashboards. Microsoft Fabric goes beyond Power BI by also providing data integration, data warehousing, data engineering, real-time analytics, and data science on the same underlying data foundation.
What mistakes are common when implementing Microsoft Fabric?
Fragmented environments often emerge when individual workspaces and lakehouses are built independently without a clear target architecture. A structured architecture and governance strategy from the beginning helps prevent costly consolidation efforts later on.
How does Bissantz support organizations implementing Microsoft Fabric?
Bissantz supports organizations throughout the process, from analyzing the existing data landscape and designing an architecture based on OneLake and a medallion architecture to using the platform for reporting, analytics, and planning — including migration, governance, and operations.
Summary
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.
Using Microsoft Fabric effectively in practice
Bissantz supports organizations in building modern data platforms with Microsoft Fabric — creating solutions that deliver both technical excellence and business value.
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