RADACAD Power BI Summit and Medallion Architecture in Data Warehousing
- Jim Walker
- Feb 28
- 5 min read
Updated: Mar 13
Attending the Power BI Summit hosted by RADACAD offers invaluable opportunities for IT consultants to deepen their understanding of advanced data architectures and their practical applications. Not just the structured presentations, but the opportunity to join the Q&As after each one and ask your questions to Microsoft MVPs across the globe.
One session I really enjoyed was delivered by Kelly Broekstra, who demystified the Medallion Architecture tracing its conceptual roots back to Ralph Kimball's pioneering work.
This blog post explores the insights from that session, delves into Kimball's foundational ideas, and examines how the Medallion Architecture can be effectively applied within social housing management systems to enhance self-service Business Intelligence (BI).
Unpacking the Medallion Architecture
The Medallion Architecture is a data design pattern that organizes data processing into three distinct layers: Bronze, Silver, and Gold. Each layer serves a specific purpose in refining raw data into actionable insights:
Bronze Layer (Raw Data): This initial stage involves ingesting data in its original form from various sources. The focus here is on capturing all data, regardless of quality or structure, to ensure nothing is overlooked.
Silver Layer (Cleansed and Conformed Data): In this layer, the raw data undergoes cleaning and transformation processes to address inconsistencies, duplicates, and errors. The goal is to create a unified and reliable dataset that accurately represents business entities and transactions.
Gold Layer (Curated Business-Level Data): The final layer involves structuring the cleansed data into formats optimized for analytics and reporting, such as star schemas. This curated data is tailored to meet specific business requirements and is ready for consumption by BI tools and end-users.
It was emphasized that this layered approach not only streamlines data processing but also enhances data quality and accessibility, enabling organizations to derive meaningful insights more efficiently.
Kimball's Influence
The principles underlying the Medallion Architecture can be traced back to Ralph Kimball's work on data warehousing. In his article "Data Warehouse Dining Experience," Kimball uses the metaphor of a restaurant to illustrate the importance of separating data processing environments:
The Kitchen (Back-End Data Processing): Analogous to the data staging area, where raw data is transformed and prepared. This environment is managed by skilled professionals who ensure data quality and consistency before it reaches the end-users.
The Dining Area (Front-End Data Consumption): Represents the presentation layer where end-users interact feast on the data. This area is designed for ease of access and usability, allowing users to derive insights without delving into the complexities of data preparation. Its structured in such a way as to avoid the chance of doing the wrong thing.
Kimball's emphasis on separating the back-end processing from front-end consumption aligns with the structured layering of the Medallion Architecture, ensuring that data is systematically refined and presented in a user-friendly manner.
Applying Medallion Architecture in Social Housing Management
Implementing the Medallion Architecture within social housing management systems can significantly enhance data handling and decision-making processes. Here's how each layer can be tailored to our sector:
Bronze Layer: Aggregating Diverse Data Sources
Social housing organizations collect data from various sources, including tenant applications, repairs and maintenance records, financial transactions, compliance and so much more. In the Bronze Layer, all this data is ingested in its raw form, creating a comprehensive repository that captures every detail, regardless of format or quality.
Silver Layer: Standardizing and Integrating Data
Once collected, the data moves to the Silver Layer, where it undergoes cleansing and standardization. For instance:
Data Cleansing: Correcting errors such as missing details, missing lookups or types or incorrect property addresses.
Deduplication: Removing duplicate records to ensure each tenant or property is uniquely represented.
Integration: Merging data from different departments (e.g., maintenance and finance) to provide a unified view of each property and tenant.
This process results in a reliable dataset that reflects the current state of the housing portfolio and tenant demographics.
Gold Layer: Delivering Actionable Insights
In the Gold Layer, the refined data is organized into analytical models tailored to specific business needs, such as:
Occupancy Rates: Analysing trends in tenant occupancy to identify patterns and forecast future housing demands.
Maintenance Efficiency: Evaluating the time and cost associated with property maintenance to optimize resource allocation.
Financial Performance: Monitoring rent collection and budgeting to ensure financial sustainability.
By structuring data into intuitive models, stakeholders can easily access and interpret information, facilitating informed decision-making.
The Importance of Getting It Right
One of the biggest risks in data-driven decision-making is basing conclusions on inaccurate, incomplete, or misleading data. Poorly joined, duplicated, or erroneous data can lead to serious operational consequences:
Eroded Trust in Data: If business users consistently find errors in reports or dashboards, they may lose confidence in the data, leading to a reluctance to use BI tools.
Incorrect Operational Decisions: Decision-makers relying on faulty data could misallocate resources, fail to identify at-risk tenants, or incorrectly prioritize maintenance efforts.
Compliance and Regulatory Risks: Social housing organizations operate in a highly regulated environment, and errors in reporting can result in compliance breaches, penalties, or even legal consequences.
Inefficiencies and Increased Costs: Duplicate or inaccurate data may lead to redundant work, wasted resources, and inefficiencies that could otherwise be avoided with a robust data governance strategy.
A well-implemented Medallion Architecture helps reduce these risks by ensuring that only high-quality, trustworthy data reaches decision-makers. The structured approach ensures that erroneous or duplicate records are identified and rectified at the Silver Layer, while the Gold Layer ensures that only curated and business-relevant data is used for analytics and reporting.
Empowering Self-Service BI in Social Housing
A significant advantage of adopting the Medallion Architecture is its support for self-service BI, enabling non-technical users to generate reports and insights without relying on IT specialists. In the context of social housing:
User-Friendly Dashboards: Front-line staff can utilize dashboards that display real-time data on tenant inquiries, maintenance requests, and compliance statuses, allowing for prompt responses and efficient service delivery.
Ad-Hoc Reporting: Managers can create custom reports to explore specific issues, such as analysing the impact of recent policy changes on tenant satisfaction or identifying properties with recurring maintenance problems.
Data-Driven Decision Making: With direct access to curated data, decision-makers can base their strategies on empirical evidence, leading to more effective and transparent governance.
By democratizing (dishing out :-) data access (in a controlled way obviously!), social housing organizations can foster a culture of data-driven decision-making, improving services and outcomes for tenants.
Conclusion
The insights from Kelly Broekstra's session at the Power BI Summit underscore the practical value of the Medallion Architecture in transforming raw data into actionable intelligence. Rooted in Kimball's foundational concepts, this structured approach is particularly beneficial for social housing management systems, where diverse data sources and the need for timely, informed decisions are prevalent. By implementing the Medallion Architecture, these organizations can enhance their data processing capabilities, empower staff through self-service BI, and ultimately deliver better services to their communities.
For a deeper understanding of the Medallion Architecture and its applications, consider exploring the following resources:
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