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What is a Data Mart in a Data Warehouse? Types and Examples

Segmentation is essential for specialization. Businesses can only flourish when they pay individualized attention to every aspect of the business. A data mart is a focused and segmented part of a data warehouse that focuses on a specific business area. What is a data mart all about? It is time we deep dive into the details.

Let us get familiar with Data Mart

Every business has various departments to manage and deal with different operations. Just as one department cannot handle every aspect of a business, a single database cannot tackle every functional area; this is where data marts come into play. A data mart is a subsection of a data warehouse that collects data from a single source. Businesses consist of various departments, including HR, Sales, Marketing, R&D, and Finance. One can simply not store and utilize all the information on a single platform as it hinders the platform’s functionality. Moreover, not every database is designed to perform operations related to every department. Thus, dividing data warehouses into specialized subsections has categorized data for a streamlined and collaborative organizational performance.

Data marts are customized databases with the required tools for analyzing data belonging to various sources. If you choose a data mart for a specific business line, it will be customized according to it. The whole system of data marts is aligned to the business area it works for. Although it is smaller and less complex, one cannot take away its ability to analyze data intricately. When a database is designed for a specific department, it only consists of essential analysis tools for that department. So, organizations should not blur the lines between departments when entering data into a data mart. A data mart accesses all the customer-facing information to use for further analysis. If you plan to buy a data mart for your organization, plan three to six months as it takes a few months to set up. 

Let’s do a deep dive down now on what is a data mart and to setup one for your enterprise?

Data Marts- A subsection of data warehouses

Unlike data warehouses that gather and consolidate information from a wide range of sources, data marts target a few sources to store information. On a corporate level, organizations prefer data warehouses, whereas data marts are designed for particular business lines. Data marts are much smaller in size, i.e., less than 100 GB. On the other hand, data warehouses are greater than 100 GB. Data warehouses are a repository of information from different parts; therefore, the storage size has to be much greater than data marts. As data marts are a part of data warehouses, they increase the efficiency of data warehouses to utilize business information for insightful analysis. Building data warehouses is not a simple task. A lot of complexity goes into creating data warehouses, but data marts are simpler to build and handle. Organizations should bring data marts into usage rather than gathering data into a single source that lacks specialization. 

What is a Data Mart – Types of Data Mart

Data marts are subsequently categorized into three main types.

  1. Dependent Data Mart

Benefitting the users with a centralized data warehouse, a dependent data mart combines all your business data. The dependent data mart comes into existence when organizations want to gain the benefits of centralization. Organizations must build departmental data stores with shareable day-to-day business information in a dependent data mart. Departmental data storage comprises financial documents, administrative information, and specific files. 

A dependent data mart is built in two ways.

  • Data marts and enterprise data warehouses are built to offer the operator simultaneous access to both. Operators can choose which database to choose upon the data requirements. 
  • A federated approach is initiated that stores information in temporary storage spaces rather than physical ones. In this case, operators have access to departmental data only.

The first approach to building dependent data marts is more optimal than the second one. Organizations do not prefer the second approach because it is usually junked even though the data begins with a single source. You will find a dependent data mart on the top of the central data warehouse. Information from the centralized data warehouse flows towards the dependent data mart according to the specific product line. The specialization is somewhat simpler at the data mart level than the data warehouse as all the information first enters the data warehouse. The dependent data marts rely on data warehouses to function. If any unforeseen situation occurs in a data warehouse, the data mart goes down. Dependence leads to such issues in the long run.

  1. Independent Data Mart

It is the complete opposite of dependent data marts. This model eliminates the need for a centralized data warehouse. An independent data mart functions totally on its own without any external support or aid. They are built to meet the needs of specific departments and are small entities that operate independently. Smaller organizational units prefer independent data marts as they require a lower level of centralization. As each independent data mart functions on its own, it loses the concept and essence of centralization. Organizations that use independent data marts need to have technical experts as independent entities require more technical supervision than non-technical ones. Each independent data mart is under the supervision of a technical expert who builds queries if the need for data aggregation arises in the organization. Otherwise, independent data marts analyze their information on their own. Each separate independent data mart conducts its own analysis and generates specific reports; this fails at the executive level, where businesses need a collaborated report of every operation in an organization. DotNet Report Builder’s chart and dashboard feature provide users with an embedded report generating tool that filters out excess data and creates customized charts to support systematized data analysis. Organizations that adopt independent data marts should issue an organizational taxonomy with standard terms used all across the data marts. It is easier to generate executive-level reports by collaborating with multiple data marts in an organization. 

A downside of independent marts is data redundancy. Data redundancy occurs in independent data marts as the same information is held in multiple places. As every data mart stores comprehensive information that flows in an organization, repetition is bound to happen. Consecutive data repetition leads to information duplication at various levels, making it complicated to analyze information efficiently and scale business growth. This shows that data centralization is crucial to an organization’s performance as centralized data generates insightful reports that can be used for forecasting future trends and current lags in business operations.

  1. Hybrid Data Mart

If you are a business that aims to introduce a new product line in the market soon, hybrid data marts are the most viable option for you. Hybrid data marts collect data from multiple sources rather than gathering data from a single data warehouse. Evident from its self-explanatory name, hybrid data marts combine dependent and independent data marts. Organizations that are not relying on a single type of data mart should consider switching to the hybrid data mart model as it combines both types of data marts existing in an organization. This combination of data marts is essential for the smooth running of the business. 

Organizations should essentially initiate their operations through independent data marts as they are more useful for new subject-specific product lines. However, relying solely on independent data marts is not a feasible option. So, integrating hybrid data marts is recommended as they can abide by the business policies in later stages. Fast-paced organizations need quick data analysis to run their day-to-day operations in a profitable manner which is only possible by integrating hybrid data marts as they provide a reliable combined output from all the data marts present in an organization. Before jumping on the bandwagon and choosing hybrid data marts, it is crucial to estimate the costs it can incur on a business. Only go for a hybrid data mart if you have the required technical expertise in your organization, as only buying a hybrid data mart will not align the information. It is a complex tool that requires experience and technical knowledge on the user’s side. Furthermore, huge storage space is required by a hybrid data mart as it is a combination of independent and dependent data marts. Employees managing data warehouses and data marts should understand its functionalities as without being an expert, one can simply not utilize the features to gain maximum benefit from the data.

Choose the Right Data Mart for your Business

Let us guide you through the process of choosing the right data mart. Consider whether your business departments need to work dependently or independently. Secondly, it is essential to understand the data privacy policies to see which data mart would be suitable for your business. Assessing your technical expertise is essential before you make the final decision, as handling a complex type of data mart without the subject experts would only lead to extra costs. Avail the services of DotNet Report and allow us to combine various data sources in affordable packages for you.

Pros and Cons of Data Mart


  1. Sub-division of data into product-specific segments leads to better data organization.
  2. Cost-effective data gathering option.
  3.  A simplified tool to access data.
  4. Higher security than data warehouses as one department will not have access to another department’s data if not required.


  1. As it organizes data on a micro-level, users are unable to analyze data on a larger scale.
  2. The emergence of redundant data.
  3. Too many unrelated data marts can begin to exist in an organization which is not the ideal case to boost productivity.
  4. Organizations have to bear additional costs along with paying for the data warehouse. 

Data Mart Implementation

  1. Identify the requirements and data sources.
  2. Choose the data subset
  3. Build the database schema
  4. Generate indexes, tables, and fields
  5. Transfer data
  6. You are all set to access the stored data in the data mart according to your organization’s requirements.

Post-Implementation Phase

As the implementation process is completed, businesses can promptly access information due to data marts segmentation according to specific business lines. As different departments monitor their own updated data from time to time, it guarantees to minimize the risk. Moreover, data marts ensure that future forecasts are accurate and relevant to the selected department or product line. You will never have to put in grueling efforts to filter out the relevant data from a data warehouse once data marts are brought into the picture. Each department present in the organization has to focus on its own data, thus reducing its workload. Business intelligence tools are crucial to provide insightful information and data analysis. Data marts can be easily integrated into those tools to smooth the whole process for the end-user. On a macro level, it stimulates informed decision-making, which focuses on different segments of the business rather than its whole.

We believe this article would have helped you understand in detail what is a data mart. 🙂

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