Data Mart vs Data Warehouse: How to Choose the Right Approach for Your Business

Modern organizations depend on their data to guide operations and inform decision-making. Of course, data analytics and reporting applications are critical to the process, but where you store the data is important, too. The kind of data your organization has and how it’s used plays a large part in where it’s stored. Two such storage locations are data marts and data warehouses.

In this article, we’ll explore the differences between data marts and data warehouses, how best to leverage them, and offer insights to help your business choose the most appropriate approach for your objectives.

What is a data warehouse?

A data warehouse is a centralized data repository, a vast library where information from multiple internal and external sources is systematically organized, uniformly formatted, and stored. It provides a centralized access point for structured data so it can be queried quickly. Data warehouses provide a single source of truth, ensuring that data from disparate sources is available and accurate, enabling streamlined analysis and reporting across organizational departments.

Organizations of all sizes employ data warehouses to provide a comprehensive and integrated view of all data assets. By bringing together data from different sources into a standardized format, data warehouses ensure data consistency, accuracy, and reliability. They simplify data access so users can perform in-depth analysis and make sound decisions.

What is a data mart?

A data mart is a distinct subset of a data warehouse designed to cater to the specific analytical needs of individual departments or user groups within an organization. Unlike a data warehouse, which stores all types of organizational information in a centralized location, the data within a data mart is decentralized, dedicated to a distinct focus, subject area, or business function, providing targeted access to specific information.

Data marts gather data from just a handful of sources—or even a single source. The data is specific to a purpose, subject, or department like sales, marketing, finance, etc.—and nothing else. The specialized nature of a data mart means that it handles a relatively small amount of data compared to a data warehouse. The small relative size and the context-based function of data marts allow fast access to the data, enabling bespoke reporting and analytics functions for targeted initiatives.

Key differences between data marts and data warehouses

Data warehouses and data marts use the same technology to perform their relative functions but offer different ways to manage data. Understanding the differences will help organizations decide which approach is most appropriate. The choice depends on factors such as data complexity, organizational structure, budget, analysis, governance, and security requirements. Here are some key distinctions:

• Data structure: Data warehouses typically store detailed, granular data from multiple sources in a standardized format. Data marts focus on specific subject areas or business functions and may contain aggregated or summarized data tailored to the needs of individual departments or user groups.
• Performance: Data warehouses are designed to efficiently handle large volumes of data and complex queries, making them suitable for enterprise-wide analytics. Being smaller in scope, data marts often provide faster query performance and easier access to relevant data for targeted analysis within specific departments or functions.
• Focus and purpose: Data warehouses centralize comprehensive data for analysis across the organization, serving multiple departments or functions. Conversely, data marts offer a focused and tailored approach to data management and analysis, addressing the specific requirements of individual departments or user groups.
• Cost and setup time: Data warehousing projects can be resource-intensive and require a significant up-front investment in the infrastructure and the staff to build and maintain it. Data marts, on the other hand, are more cost-effective for small-scale projects or specific business units. They’re also faster to implement and require fewer resources to keep them running.
• Flexibility: Data warehouses are flexible regarding scalability and expansion, which is great for organizations anticipating increased data storage needs. Data marts’ flexibility comes from their customization and agility, which can provide distinct advantages for departments that need to quickly adapt to changing business needs without relying on centralized IT support.
• Data granularity: Data warehouses store detailed transaction-level data, which provides a comprehensive picture of historical and current information across all departments and functions. By contrast, data marts can focus on specific levels of data granularity, including aggregated or summarized data tailored to a specific goal, user group, or department.
• Scope of analysis: Data warehouses support complex, enterprise-wide analysis, allowing users to derive insights from data across multiple business domains and functions. Data marts are more narrowly focused, facilitating targeted analysis within specific areas of an organization.
• Data governance: Data warehouses are often implemented to adhere to centralized data governance policies and standards, ensuring data consistency, integrity, and security across the organization. Depending on departmental requirements and data ownership structures, data marts may have varying levels of governance and control.
• Integration with external sources: Data warehouses are designed to integrate data from a variety of internal and external sources, providing a holistic view of organizational data. Data marts may have limited integration capabilities with external data sources, and generally focus on internal data relevant to their department or user group.
• Security controls: Data warehouses are often implemented with robust security measures to protect sensitive organizational data, adding access controls, encryption, and auditing capabilities. They adhere to centralized security policies and standards, ensuring data privacy and compliance with regulatory requirements. Data marts can have varying levels of security controls, depending on the departmental requirements and data sensitivity. While some data marts may inherit security measures from the ‘parent’ data warehouse, others may implement additional security measures tailored to the specific needs of their department or user group.

Data mart vs data warehouse: Examples

Let’s consider a hypothetical retail business scenario to compare the two approaches. The Generic Retail Company (GenRetail) operates multiple stores across several regions and wants to improve its sales analytics capabilities.

Data warehouse implementation

GenRetail implements a data warehouse to centralize its data and perform comprehensive sales analysis across all its stores. The data warehouse integrates data from multiple sources, including point-of-sale systems, customer relationship management (CRM) software, and various databases. It stores detailed transactional data, customer information, and inventory records in a standardized format.

With the data warehouse in place, the GenRetail management team gains insights into overall sales performance, customer behavior, buying trends, and inventory management across the entire organization. The team can identify top-performing stores, analyze sales trends by region, and forecast demand for specific products.

Data mart implementation

In addition to the data warehouse, GenRetail’s marketing department decides to implement a data mart to focus specifically on analytics from its marketing campaigns. The marketing-focused data mart aggregates data related only to marketing efforts, customer demographics, and promotional activities pulled from the central data warehouse.

Using the customized data mart, the marketing team can analyze the performance of different marketing campaigns, define customer segments based on demographics and purchasing behavior, and tailor promotional strategies that target those specific segments. They can track key performance metrics, including return on investment (ROI) for each marketing campaign, and make data-driven decisions to optimize and focus their efforts.

Comparison

While the data warehouse provides a full view of organizational data for enterprise-wide analysis, the data mart enables a focused and tailored approach to the marketing department's specific goals. The data warehouse is the foundation for GenRetail’s centralized data management and analysis, while the marketing-specific data mart enables the department to extract insights relevant to its specific needs and objectives.

How to choose the right approach for your business

Organizations should assess their specific needs, objectives, and resources when evaluating whether to implement a data mart or a data warehouse. Here are some elements to consider as you evaluate the right approach for your business:

Use data marts for:

• Rapid analysis and departmental focus: Data marts are ideal if your organization requires quick access to specific data for departmental analysis or decision-making. They offer targeted access to relevant data, enabling departments to perform in-depth analysis without relying on centralized IT support.
• Decentralized data ownership and management: Data marts permit departments or business units to own and control their data, enabling autonomy and agility with a focused intent. This decentralized approach enables the building of customized data structures and analytical models to suit specific needs and preferences.
• Cost-effectiveness for smaller projects: Data marts offer a cost-effective solution for smaller-scale projects or specific business units with limited resources. Compared to data warehouses, they can be implemented faster and at a lower cost, making them suitable for organizations looking to start small and scale gradually.

Use data warehouses for:

• Complex data integration and cleansing: A data warehouse is the right choice if your organization manages large volumes of data from diverse sources that require complex integration and cleansing processes. Data warehouses provide a centralized platform for integrating, standardizing, and cleansing data to ensure accuracy and consistency.
• Future flexibility and adaptability: Data warehouses are supremely scalable and flexible, able to accommodate an organization's evolving data needs and analytical requirements. They serve as a foundation for enterprise-wide analytics initiatives, providing a solid infrastructure that can adapt as the organization grows.
• Enterprise-wide analysis and reporting: Data warehouses are indispensable for organizations that require comprehensive data analysis and reporting capabilities across multiple departments or business functions. They provide a unified view of organizational data, enabling stakeholders to make informed decisions at the enterprise level.

Aligning these factors with your organization's goals and priorities will inform the approach you take—whether a data mart, a data warehouse, or a combination of both—to optimize your data management and analytics capabilities.

The CData difference

Optimizing data storage and analytics within departments—and the organization itself—is critical for staying competitive and relevant. Whether you use a data mart, data warehouse, or something else to manage your data, CData Connect Cloud provides streamlined data integration from your data sources—no matter where they are.