How Stibo Systems’ MDM powers trusted data for analytics and AI in Microsoft Fabric (Preview)
Coauthor: Simon Tuson, Principal Product Manager, Stibo Systems
The preview of the Stibo Systems Master Data Management (MDM) workload on Microsoft Fabric which integrates enterprise customers’ master data and ingests it directly into Fabric OneLake through their DaaS (Data as a service) feature to unlock analytics and AI use-cases for them is now available.
For organizations that rely on trusted master data across products, customers, suppliers, and other core domains, this integration unlocks a new way to operationalize MDM: without the need to build complex pipelines for data transformation, schema rework, or data duplication. Data is made available to different business units and user personas, from data scientists and analysts to business users, in a familiar and easy-to-consume format with minimal engineering effort.
Figure: Detailed description of the Stibo workload on the workload hub.
Why this matters
Master Data Management is essential—but data being optimized for highly scalable Master Data Management activities is not always the best approach for running analytics and AI enabled workflows at scale.
MDM systems are built for governance, stewardship, and data quality. Analytics platforms are built for speed, flexibility, and delivering actionable insights. Bridging the two has traditionally required rigid integrations, brittle exports, and ongoing maintenance.
Unlike traditional MDM integrations that might rely on exports or custom ETL, Stibo’s Data as a Service (DaaS) solution offers a simple approach to converting the master data into a consumable analytics‑ready data in Microsoft Fabric.
The Stibo Systems MDM workload for Fabric changes that by exposing Master Data as a cloud‑native data service inside Microsoft Fabric, allowing organizations to activate trusted data across BI, analytics, and AI workflows—while preserving the rigor of enterprise MDM.
The result is a cleaner separation of responsibilities and faster time to value existing MDM investments. This enables organizations to reduce operational overhead, accelerate time to insight, and power multi-agent workflows to automate business processes.
What’s available in preview
As organizations adopt Microsoft Fabric as their unified data and AI platform, the ability to activate access to governed master data at scale becomes critical.
The DaaS offering in Stibo’s workload bridges that gap by introducing a simple integrated experience on the Fabric platform where:
- Customers can bring relevant and trusted data from their upstream MDM system into OneLake.
- Harness the power of Power BI Direct Lake mode to enable reporting and visualization using Power BI.
- Take full advantage of the semantic models in Lakehouse to build Data Agents and run Copilot queries on top of their data.
- Enable business to be agile and pivot when needed to deliver timely insights into an ever-changing business landscape.
Workload capabilities:
Access curated data assets from your MDM directly in Fabric. Golden records, product data and other data can be grouped into data assets and transformed into analytics‑ready datasets, not raw exports, but stored in delta-parquet open-format in the customer’s Lakehouse. Without programming and detailed MDM knowledge, data is delivered into the Fabric ecosystem into the workloads that are familiar to your data experts.
- Leverage OneLake‑native integration: Master data can be materialized into Lakehouse tables or accessed via shortcuts, making it reusable across the Fabric ecosystem. Enable self‑service analytics with centralized governance
Data teams can configure which assets are exposed and how they refresh, while MDM teams retain control over definitions and quality rules.
- Power real‑time analytics and AI: With the data in Lakehouse trusted analytics ready data can be used to power BI reporting using Direct Lake mode. With new data being ingested into the Lakehouse, the underlying semantic model that powers the Power BI visualization layer is always up to date in near real time.
The Stibo differentiator—Delivering customer value; solving a problem
- Use-Case One: Context-aware dynamic assortments. Assortment decisions remain largely static and slow to adapt to real-world context, leading to missed sales opportunities and excess inventory at store level. Golden product master (attributes, hierarchies, lifecycle), store and location master data shared to Fabric and combined with contextual signals (weather, events, local demand) enable retailers to optimize assortments dynamically, leading to increased sales.
- Use-Case Two: As new environmental and product transparency rules emerge, retailers struggle to quickly identify which suppliers, products, and regions are impacted, increasing compliance risk and manual effort. Product, supplier, material, and country-of-origin master data shared into MS Fabric, enabling rapid impact analysis, scenario modeling, and reporting as regulations evolve.
In the traditional model, this would take months to deliver. Now, the workload can offer the same capability in a few hours.
Getting started
Begin exploring the workload today and see how governed master data can power analytics and AI across their organization.
To get started, check out the Product Overview and (Preview) offer in Azure Marketplace and watch the product demo on YouTube.
You can also contact Stibo directly to get support on adding their workload from the Workload Hub.
