What Are Data Marts? (Definition And How To Utilise)

Updated 24 September 2022

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Businesses can organise data for simple access by using a data mart. Data organisation is crucial for operating a business because having access to particular data sets can increase productivity, enhance customer interactions and increase revenue. You can decide whether this technology is required for a business by gaining an understanding of what a data mart is and where its utility lies. In this article, we define what a data mart is, discuss who uses them, describe the benefits, review common types and structures, and explore how to use a data mart.

What Are Data Marts?

Data marts are repositories of data that focus on a certain topic or area of business. A data mart may focus on areas like accessibility, marketing, specificity, sales, employee performance or finance to provide insightful collections of data. Businesses employ data to enhance the efficacy of their operations and gain additional insights into internal business procedures.

Who Uses Data Marts?

Many types of businesses can use a data mart for improving data accessibility. Here are some examples of industries that can benefit from this technology:

Finance: A data mart in the financial industry typically aids in categorising and storing distinct sets of financial data for businesses. This is particularly beneficial for financial planning.
Sales: While planning for promotional periods or gathering data for particular sales approaches, sales teams typically employ data marts. For example, a sales team might collect data for the holiday season.
Marketing: Marketing teams can use a data mart to extract requisite data from a data warehouse or a collection of general data sets for quick access. Marketing experts use customer engagement, product performance and market analytics data to create effective campaigns.

Benefits Of Using A Data Mart

Most sectors benefit from data mart usage. Here are a few prominent advantages:

Centralised data: A data mart can assist in centralising particular data sets so that everyone can access information from a specific source. This minimises errors and prevents data-related discrepancies.
Scalable data management: This technology offers data management with greater scalability, or the capacity to expand data infrastructure as a company's requirements alter.
Fast implementation: These tools may be quicker and simpler to create because they are more specialised than larger data warehouses. For a business, this can save money and time.
Quick data access: Teams can review data sets and easily access specific data points using this technology. This might speed up and save both money and time during the process of data collection.
Better decision-making: Teams may be able to make better judgments based on factual information if they have access to precise data sets. This can lower costs and increase general efficiency.
Low cost: A data mart setup for a company may be considerably less expensive than a full-scale data warehouse setup.
Easy maintenance: A data mart simply includes the data necessary for a specific business unit or department, as opposed to data warehouses, which are required to be integrated with a wide range of internal and external data sources. Because this technology aims to meet the needs of a particular business team instead of an entire organisation, deployment and maintenance is quicker and simpler.

3 Common Data Mart Types

Here are the three common types of a data mart:

1. Dependent data mart

A dependent data mart usually emerges from an organisation's pre-existing data warehouse. The data mart functions as an integral component of the entire warehouse and is dependent on data inputs from it. A dependent data mart only takes data sets out of the warehouse when a team requires certain information. By placing particular data sets into clusters and then removing a specific subset of the data for analysis as required, you can create a dependent data mart.

2. Hybrid data mart

A hybrid data mart is an intermediary between an independent and dependent data mart, which uses information from both an internal data warehouse and external sources. The hybrid model combines the flexibility of an independent data mart and the dependability of a company's exclusive data warehouse. You can create a hybrid data mart by creating a data set as a dependent of the data warehouse and connecting it to external sources. This allows teams to collect data from both internal and external sources.

3. Independent data mart

An independent data mart functions independently of a data warehouse. These are prevalent in smaller companies or companies that do not want to expend the resources necessary to build a complete data warehouse. By creating distinct systems, a firm may acquire the data it requires without having to expand and possibly go over budget. When an independent data mart is not reliant on a data warehouse, it may also become more adaptable and accessible.

Data Mart Structures

A data mart typically has specific structures depending on its use. Here are a few examples of data mart structures:

Star schema

The star structure is a multidimensional database in the form of a five-pointed star. The centre of the star represents the data mart's intended use, which is a specific business process, and its outlying regions hold data related to that use. The core database serves as the connection point and the basis for all of the outer arms' relationships, but they are not strictly necessary for one another to function.

Vault

The vault-style data mart, which helps set the groundwork for a complete data warehouse, is a more tiered approach to the data mart system. The vault style helps overlay data sets for greater flexibility and usability for user teams accessing the data and requires less upkeep than the star model. A vault system also improves the security of data sets.

Snowflake schema

The snowflake model is an extension of the star model. In addition to adding new data dimensions for a main data collection, it takes advantage of the star data mart's existing design. By adding more tables, the snowflake approach may produce large data sets and use less disc space to store and maintain them.

How To Use A Data Mart?

Follow these steps to set up and use a data mart effectively:

1. Design a data management strategy

While developing a firm's data mart plan, consider if you are creating an independent data mart, utilising an existing warehouse or planning to establish a future data warehouse. Choose a strategy regarding the type of data you want to gather, store and use for the firm. Determine the potential expenses to the organisation and whether any changes to the current data warehouse system are required to enable the inclusion of specific data sets. You can also consider scaling the data mart if necessary.

2. Construct the data mart architecture

Develop the data mart design keeping in mind the criteria that you establish in the first phase. Choose the database which you can use for your data mart and make any necessary adjustments to the structure of the pre-existing data warehouse. You can also ensure accessibility to make data more accessible and easier for team members to use. Ensure that users have access to the data they require if they possess the right permissions within the system and the right connectivity.

3. Populate the data mart

Populating data mart architecture means executing the data flow between the data mart and a data warehouse or external sources. By doing so, you can fill the data mart with the information that the business requires, and troubleshoot any issues. To make the information in the data mart more accessible, you can choose what conditions make data accessible, how to clean and normalise data and what indices to utilise.

4. Access the data mart

By creating queries for particular data sets and making sure the data mart receives them, you can access the populated data mart. This presents an opportunity to resolve certain problems and make sure that the data mart is working properly. You can evaluate how the data mart integrates with your current data warehouse and how users react to the user experience and functionality and identify potential updates. Documenting the deployment stage can help you track mistakes, achievements and user input for upcoming enhancements.