How medallion architecture in Microsoft Fabric changes the way organizations work with data
For financial institutions, data architecture is the foundation of process control, reporting, and business decision-making. As a result, a central platform that integrates data from core systems is a natural starting point. Unfortunately, on its own it does not solve issues related to data quality and consistency. Without a clearly defined data structure, even advanced analytics and AI-based solutions fail to deliver results that are sufficiently reliable from both a management and regulatory perspective.
Medallion architecture in the financial sector introduces a clear separation of data into layers with increasing levels of quality and accountability. This makes it possible to use data in a controlled, auditable, and predictable way, directly supporting business decisions and a consistent customer experience.
In previous articles in this series, we explored how Microsoft Fabric is reshaping analytics and how Copilot brings natural language into everyday work with data. We now know where to work and with whose support. The next and most important question is how to organize data so that analytics and AI-driven solutions deliver reliable and correct answers.
When classic ETL no longer keeps up: where data analytics bottlenecks come from
In the traditional ETL approach (Extract, Transform, Load), still widely used by financial organizations, data is cleansed and transformed before it is loaded into the data warehouse. This means that how the data will be used must be defined at an early stage, and business logic is permanently embedded in integration processes. As a result, any change in business requirements, even something as simple as adding a new field to a customer preferences report, requires modifying and retesting the processes.
From a business perspective, this translates into longer change cycles and a strong dependency on teams responsible for data integration. In environments where speed of response directly affects customer experience, this becomes a noticeable limitation to operational efficiency.
ELT as an answer to the limitations of classic ETL
In the ELT approach (Extract, Load, Transform), used in Microsoft Fabric, data is first loaded in its raw form into a shared data repository within a Lakehouse architecture built on OneLake. The Lakehouse combines the flexible storage capabilities of a data lake with the governance and control principles typical of a data warehouse. Only at a later stage is the data processed and prepared for analytics and reporting.
This approach makes it possible to introduce changes to reports, analytics, or data models without impacting the entire integration chain, significantly reducing response time to changing business and regulatory requirements.
Medallion architecture: three levels of trust and better CX
With the ELT approach, data must be organized into a coherent structure that clearly defines its quality and readiness for business use. Instead of a single, large, and often opaque database, microsoft fabric applies a division into three logical layers. Each of them supports a better understanding of the customer.
1. Bronze (raw data): the organization’s digital archive
The Bronze layer serves as the entry point for data ingested directly from source systems such as transactional systems, policy administration platforms, or CRM solutions. Data is stored in its original, unmodified form.
- Security foundation: with a complete data history stored in OneLake, it is possible to perform retrospective analyses. If, after several months, there is a need to determine at which point customers abandoned a loan application or insurance form, teams can return to the source data and analyze the situation historically.
- Time travel: thanks to the Delta Lake format, it is possible to recreate the state of the data for a specific date. This is critical during audits or complaints handling, when it is necessary to clearly demonstrate which data was available at the time a decision was made.
2. Silver (cleansed data): an integrated source of information
The Silver layer is responsible for standardizing, cleansing, and integrating data from multiple systems. At this stage, duplicates are removed and customer data is consolidated into a single, consistent profile, forming the foundation for a single customer view.
- Consistency in customer service: with the Silver layer in place, a branch advisor, a call center agent, and a digital channel bot all rely on the same verified data. Customers do not receive conflicting information about their status, offers, or relationship history, which directly improves customer experience and trust in the institution.
3. Gold (business-ready data): decision support
The Gold layer contains data that has been processed and optimized for specific business use cases such as credit scoring, churn analysis, or customer segmentation. These are ready-to-use data products consumed directly in reporting, analytics, and decision-making processes.
- Hyper-personalization: with direct lake technology, Power Bi reports access Gold layer data directly. In practice, this means that when a customer contacts the call center or a relationship manager, up-to-date recommendations and analyses based on verified data are immediately available. Decisions are not based on assumptions but on a current and accurate view of the customer’s situation.
Why does modern data architecture better address the needs of financial institutions?
In discussions with executive leadership, the argument of security often arises in favor of proven, classic ETL-based approaches. In practice, however, growing business and regulatory requirements mean that the rigidity of this model increasingly limits an organization’s ability to respond quickly and consistently build customer experience.
Key advantages of Microsoft Fabric:
- Lower total cost of ownership (TCO): reducing unnecessary data copies and simplifying the architecture lowers the cost of maintaining the data environment, freeing up resources for initiatives that directly support the customer.
- Shorter time to market: a flexible data processing model enables faster changes in reporting, analytics, and decision models, allowing organizations to test and develop new initiatives in shorter cycles.
- Openness: the use of open formats such as delta lake reduces the risk of vendor lock-in and supports the long-term evolution of the data architecture.
Natural language as the new interface to data: Copilot in practice
In a previous article, we showed how Microsoft Copilot enables working with data using natural language.
The ability to ask questions and receive answers directly based on data significantly simplifies analysis. However, the effectiveness of this approach depends directly on the quality and structure of the data that copilot operates on.
If Copilot is expected to help an advisor prepare a response for a high-value customer, it must rely on Gold layer data. When a manager asks, “Why are auto insurance customers leaving after the first year?”, copilot supports the analysis of curated datasets, helping identify patterns that are difficult to detect in traditional Excel-based reports.
Thanks to medallion architecture in the financial sector, analysis is not performed on raw Bronze data but on business-ready Gold data. This enables faster insights, more efficient processes, and decisions that translate into a stronger offer and a more consistent customer experience.
Summary: modernization as an investment in the relationship
Migrating from a traditional data warehouse to Microsoft Fabric and medallion architecture is a strategic decision that structures how data supports business processes, management decisions, and customer experience across the organization.
In an environment of increasing regulatory requirements, such as FIDA, and growing data availability, competitive advantage goes to institutions that can quickly and in a controlled manner transform data into reliable decision-ready information. Medallion architecture enables this approach by ensuring consistency, auditability, and a clear distinction between data based on quality and intended use.
As a result, data moves beyond a purely archival role and begins to actively support customer relationship management. Organizations that can leverage data in this way are better prepared to make informed decisions, deliver a consistent customer experience, and achieve long-term growth in a regulated environment.
