Why build a data mart? Streamline financial analysis with self-service and enhanced security

Frustrated by slow financial analysis? Data warehouses, while essential for storing and analyzing vast amounts of data, can be overwhelming for users seeking specific insights. For business users there are data marts, offering a tailored approach to data organization and accessibility. This article will introduce you to the key differences and familiarize you with how they can support self-service analytics.

What are data marts and how do they differ from data warehouses?

A data mart is like a miniaturized, department-specific store within a giant data warehouse supermarket.

Let's compare this type of store to a large data store - a data warehouse. Data warehouse is like a massive supermarket that holds all the products (data) a company might ever need. It's organized by type (databases, sales figures, customer information), but can be overwhelming to navigate for specific needs.

Data warehouses provide a centralized repository for storing and analyzing vast amounts of data. In order to optimize data processing, retrieval, and analysis, data warehouses store information in a normalized form. Normalization minimizes data redundancy and improves data integrity. While this structured format is ideal for machines, it can be difficult for humans to read and understand the relationships between different data points. This is where data marts come into play. Data marts are designed to be more user-friendly, often focusing on a specific business area and presenting data in a way that's easier for analysts to understand.

The data warehouse contains the entire assortment, the data mart, on the other hand, serves as a specialized one for specific department needs.

Data marts act as a specialized layer on top of the data warehouse framework, tailored to meet the specific needs of a particular department or business unit. They are designed to hold a subset of the data warehouse's information, carefully curated and organized for a specific use case. Each department requires specific segments of the Data Warehouse schema.

Back to our metaphor, data marts are smaller stores within the supermarket, dedicated to a specific department, like the bakery. It only carries items relevant to baking (flour, sugar, recipes), making it much easier for bakers (department users) to find what they need quickly.

What does it look like in practice?  

Data warehouses store information in a very organized way. Each piece of data is kept separate, to avoid duplicates. Imagine a transaction table storing amounts, but not customer names. Instead, it would have a customer ID (like "101") linked to another table containing names (e.g., "Peter Walker"). This organized structure is great for data experts, but it can be tricky to find things if you're not familiar with it.

Go from a complex data warehouse schema.

Data marts are pre-made group of tables. Data marts take data from the warehouse and reorganize it for a specific purpose. For example, a marketing data mart might only contain marketing data, already reorganized for easier analysis. Instead of separate tables for customer ID, item ID, and price, the data mart might directly show "Peter Walker purchased a T-shirt for 30 euros." This makes it much faster and easier for marketers to analyze their data.

To a schema where every department has exactly what it needs.

In short, data warehouses store everything in a super organized way, but it can be complex to find what you need. Data marts take data from the warehouse and reorganize it for specific uses, making it much easier to analyze.

Data marts offer a compelling solution for focused data analysis, particularly for projects with time constraints. Data engineers and developers work with smaller, more manageable datasets, reducing complexity and streamlining the process. Additionaly, they are fast to implement. This makes them ideal for organizations with specific analytical needs and limited resources.

Data marts benefits

Empowering Self-Service Analytics

Data marts democratize data access, enabling business users without extensive technical expertise to explore and analyze data independently. The denormalized structure and user-friendly presentation make it easy to connect data marts to visualization tools like Tableau or Power BI, allowing users to create reports, dashboards, and visualizations without relying on IT specialists.

Example: A marketing team can utilize a marketing data mart to analyze customer purchase patterns without navigating the complexities of the entire data warehouse. The mart provides a readily accessible dataset, enabling them to create self-service reports on customer behavior, identify trends, and optimize marketing campaigns.

Enhanced Security

Data marts play a crucial role in data governance and security. By isolating sensitive data within specific marts, organizations can restrict access to authorized users only. This control safeguards confidential information, complies with privacy regulations, and prevents unauthorized access.

Example: A company can grant marketing teams access to the marketing data mart while restricting access to financial data. This approach ensures that sensitive financial information remains protected while empowering marketing teams to make data-driven decisions.

How Dawiso can help you?

Data governance governs access to the data mart. It determines the information that can be viewed, minimizing the risk of unauthorized data breaches and misuse, ensuring secure data management, and preventing accidental or malicious manipulation.

Beyond security, Dawiso offers another key benefit for finance teams: improved data lineage visibility. Understanding the journey of data is critical for finance teams. Dawiso simplifies this process by showing exactly where each piece of data in the data mart came from. This means finance professionals can easily trace each metric back to its source spreadsheet or system. This transparency builds confidence in the accuracy of the data and allows finance teams to make informed decisions based on a clear understanding of the data's background.

Petr Mikeška
Dawiso CEO
Petr Mikeška
Dawiso CEO

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