What is a Data Mart?

What is a Data Mart?

A data mart is a type of data repository that stores and organizes a subset of data from a larger data warehouse or other data sources. A data mart is designed to serve the specific needs of a particular business unit, department, or subject area within an organization. A data mart provides a simplified and focused view of the data, enabling faster and easier access, analysis, and reporting for the intended users.

Why Use a Data Mart?

A data mart can offer several benefits for an organization, such as:

• Improved performance: A data mart contains less data than a data warehouse, which means it can process queries and generate reports faster. A data mart also reduces the workload and traffic on the central data warehouse, improving its overall performance and availability.
• Enhanced usability: A data mart is tailored to the needs and preferences of a specific group of users, which means it can provide more relevant and understandable data for them. A data mart can also use a different data model or schema than the data warehouse, allowing for more flexibility and customization.
• Increased efficiency: A data mart can help users find and analyze the data they need without having to sift through irrelevant or redundant data from other sources. A data mart can also improve the quality and consistency of the data by applying specific rules, filters, and transformations.
• Lower costs: A data mart is cheaper to build and maintain than a data warehouse, as it requires less hardware, software, and resources. A data mart can also reduce the dependency on IT staff, as users can manage their own data marts with minimal support.

How to Create a Data Mart?

There are three common types of data marts, depending on how they are created and connected to the data warehouse or other data sources:

• Independent Data Mart: An independent data mart is created and maintained separately from the data warehouse. It is sourced directly from the operational systems or external sources that are relevant to the specific business function or domain. An independent data mart offers more flexibility and agility, as it can be set up quickly and independently from the rest of the organization. However, it may also result in data redundancy and inconsistency, as the same data may be replicated across different data marts.
• Dependent Data Mart: A dependent data mart is derived from the data warehouse. It extracts and copies some of the data from the central repository and organizes it according to the specific needs of the users. A dependent data mart ensures data consistency and quality, as it relies on the integrated and standardized data from the data warehouse. However, it may also face some challenges, such as scalability, security, and synchronization issues.
• Hybrid Data Mart: A hybrid data mart combines both independent and dependent components. It uses the data warehouse as the main source of truth for the core data, but also integrates additional data sources that are specific to the business unit or department. A hybrid data mart offers the best of both worlds, as it provides both centralized control and localized flexibility.

Examples of Data Marts

Data marts can be used for various purposes and applications across different industries and domains. Some examples of data marts are:

• Sales Data Mart: A sales data mart contains information about sales transactions, customers, products, prices, promotions, etc. It can help sales managers and analysts to monitor sales performance, identify sales trends and patterns, forecast sales revenue, optimize pricing strategies, etc.
• Marketing Data Mart: A marketing data mart contains information about marketing campaigns, channels, activities, costs, outcomes, etc. It can help marketing managers and analysts to measure marketing effectiveness, evaluate marketing ROI, segment customers, personalize marketing messages, etc.
• Finance Data Mart: A finance data mart contains information about financial transactions, accounts, budgets, expenses, revenues, profits, etc. It can help finance managers and analysts to manage financial operations, prepare financial statements and reports, conduct financial analysis and planning, etc.
• HR Data Mart: An HR (human resources) data mart contains information about employees, recruitment, training, performance appraisal, compensation, benefits, etc. It can help HR managers and analysts to manage HR processes, improve employee engagement and retention, develop talent and skills, etc.

Conclusion

A data mart is a simple form of a data warehouse that is focused on a single subject or line of business, such as sales, finance, or marketing. Given their focus, data marts draw data from fewer sources than data warehouses. Data mart sources can include internal operational systems, a central data warehouse, and external data¹.

A data mart can offer several benefits for an organization, such as improved performance, enhanced usability, increased efficiency, and lower costs. However, a data mart also has some challenges, such as ensuring data consistency, quality, and security.

There are three common types of data marts: independent, dependent, and hybrid. Each type has its own advantages and disadvantages, depending on the needs and preferences of the users and the organization.

Data marts can be used for various purposes and applications across different industries and domains. Some examples of data marts are sales data mart, marketing data mart, finance data mart, and HR data mart.