Data Mart: It's Definition and Benefits

Data Mart in Data Warehouse

A data mart is a smaller data warehouse that holds only the data relevant to a specific department or segment of users. It helps reduce the overall maintenance cost and optimizes the insights from the centralized data warehouse. It is an important component of any BI strategy. Data marts are smaller, more agile databases that hold just the data needed by analysts or departments. They are built on top of your central data warehouse, using sampling and filtering techniques to extract only the relevant data for a particular department or business unit. Here is everything you need to know about data marts:

Types of Data Marts

There are two main types of Data marts — Operational and Analytical. Let's dive into details on each one in this blog:

What is an Operational Data Mart?

An operational data mart is a smaller data warehouse that serves a specific operational process, such as Human Resources or supply chain management. Operational data marts are extracted from the data warehouse and are integrated with the operational systems like ERP, WMS etc. Operational data marts store data that can be used in real-time or near real-time. Typically, operational data marts are used to monitor or analyze data that is critical to an organization's day-to-day operations. Operational data marts are also referred to as "Decision Support Systems" (DSS). For instance, an operational data mart could contain information relevant to an HR department, such as the number of employees and their salaries. This type would be used to assist the HR department with their day-to-day activities like managing benefits, payroll, etc.

What is an Analytical Data Mart?

An analytical data mart is a smaller data warehouse that holds only the data needed for analysis and reporting. They are similar to operational data marts in that they are extracted from the data warehouse and integrated with the operational systems. Analytical data marts are created when you want to go beyond the standard data warehouse features. They are used to present and analyze the data impossibly with the standard data warehouse features. Analytical data marts are not used to support real-time operations. For instance, a data mart created only for analysis could be used to track sales across different regions or analyze the sales of products in different colours.

Benefits of Data Mart

Using data marts allows companies to leverage the benefits of both central and decentralized data storage. A centralized data warehouse can store large quantities of data (from dozens to hundreds of terabytes), but only a fraction can be used in any given division or department. On the other hand, a decentralized data storage system allows each division to store the data it needs. But because decentralized data storage is a very fragmented system, it's tough to get an overview of the entire company's data. Data marts are a perfect solution for this problem. How Data Marts Fit into Your Data Strategy: Data marts are smaller and more focused than a data warehouse. They don't store as much raw data but keep the divisional data managers need. They are a lot easier to maintain, and they are updated on a more frequent basis. Data marts are a great way to boost efficiency and optimize the use of your data.

Types of Data Marts: Which One should you choose?

● Operational Data Mart - Operational data marts are used by the operations team and are critical to the organization's day-to-day activities. An example of an operational data mart is the HR data mart which contains information about employees.
● Analytical Data Mart - Analytical data marts are used to analyze data in a way that is impossible in the standard data warehouse. An example of an analytical data mart is a sales data mart containing information about each region's sales.

These two types are the most common, but many more specialized data mart types exist. These include:

● Core data mart - A core data mart contains the data that is common across all data warehouses. Data discovery data mart - A data discovery data mart is created for exploratory data analysis.
● Data enrichment data mart - A data enrichment data mart is integrated with the data in the core data warehouse and data marts that hold transactional data. It pulls in data from external sources, such as government agencies, surveys, or internet search engines. It also pulls in unstructured data (like emails or social media posts). This data is used to enrich the data in the core data warehouse.
● Reporting data mart - A reporting data mart hosts data for reports that are published to business users.

Conclusion

A data mart is a smaller, more focused database created from a centralized data warehouse. Data marts are used to serve operational needs and help analysts who need to perform exploratory data analysis. There are two main types of data marts: operational and analytical. The operations team uses operational data marts and is critical to the organization's day-to-day activities. On the other hand, analytical data marts are used to analyze data in a way that is impossible in the standard data warehouse.