Data Mart: Compactly explained

from | 27 June 2024 | Basics

Today, organisations are collecting information at an unprecedented rate. According to statistics, the average data volume of a typical organisation is 240 back-end terabytes (BETB). So how can you turn this data into actionable insights to make strategic decisions? 

This is where the data mart comes in: a targeted data repository that provides a focussed overview of key business information for specific departments or functions. Whether you're a business analyst or simply interested in data management, a clear understanding of data marts can enable smarter decision making.

What is a data mart? 

In large organisations, scattered data in different departments and systems hinders efforts to Business Intelligence. A data mart offers a solution by acting as a Central, topic-specific repository functions. It extracts and integrates relevant Data from various sources and converts them into a format that is optimised for analysis by a specific department, business unit or functional area.

Think of a data mart as a curated subset of a larger data warehouse. While a Data Warehouse aims to store comprehensive company-wide data, A data mart concentrates on a specific business area. This targeted approach offers several advantages:

  • Improved data accessibilityDepartmental users can access the data they need quickly and easily without having to find their way around a complex data warehouse.
  • Improved data quality: The data mart consolidation process often includes a Data cleansing and conversionThis ensures the accuracy and consistency of the information for the analysis.
  • Rationalised analysisThe optimised structure and focused scope of the data mart enables faster query processing and reporting. This enables departmental analysts to gain valuable insights with greater efficiency.
Business Intelligence illustration with laptop in a café - in the foreground a coffee cup and in the centre a data visualisation application on the laptop monitor - the logo of Alexander Thamm GmbH in the upper right corner.

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Types of data marts

There are three main types of data marts, each serving different purposes and meeting specific organisational requirements. These include

1. independent data marts

This is the most focussed type, designed for the analytical requirements of a single department, e.g. sales, marketing or finance. It stores data specific to the operations and KPIs (Key Performance Indicators) of the respective department. For example, a data mart for sales can contain customer information, sales data and product details. This data enables sales teams to search for valuable customers and optimise their sales strategies.

2. dependent data marts 

This type focuses on a specific business process or subject area rather than a specific department. For example, a customer relationship management (CRM) data mart would integrate data from different sources, such as customer service records and marketing campaigns. This consolidated view enables companies to better understand customer behaviour and preferences, which contributes to customer loyalty and satisfaction.

3. hybrid data marts 

This versatile approach combines elements of independent and dependent data marts. It provides a broader view of cross-functional analyses that meet the needs of multiple departments that share a common interest in a particular business area. For example, a product profitability data mart could combine sales data and marketing spend to help organisations evaluate product performance and make decisions.

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Data Mart vs. Data Warehouse

Although data marts and data warehouses play an important role in business intelligence, they differ in their scope, focus and range of functions. These differences include

Feature Data Mart Data Warehouse 
Focusspecific department or function (e.g. sales, marketing, finance)Company-wide, covering all business units and departments
Data scopeSmaller, targeted subset of relevant dataa larger, comprehensive collection of data from various sources
Data structuresimpler, optimised for specific analysis requirements (often star or snowflake scheme)more complex, designed for different purposes
Amount of dataSmaller size compared to a data warehouselarger volume containing comprehensive data
Data processingLimited data processing options, focus on predefined queries and reportsSupports complex data transformations and integrations
Implementation timeFaster and easier to set up due to the focussed scopeLonger and more complex implementation due to company-wide data integration
User basespecifically for a particular business unit or departmentAccessible to users throughout the organisation
CostsGenerally less costly to implement and maintainHigher implementation and maintenance costs due to the larger data volume and complexity
MaintenanceEasy to maintainMore complex maintenance
Data governancemay have less stringent data governance requirements compared to a data warehouseusually requires robust data governance policies due to the sensitivity of the company-wide data
Differences between data mart and data warehouse
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Data mart vs. data lake

Data lakes and data marts are both valuable tools for data management, but they serve different purposes. Their main differences include:

FeatureData MartData Lake
Data structureStructured, pre-processed data (often star or snowflake schema)Unstructured, raw data in various formats (text, images, sensor data)
Benefitfocuses on specific business requirements or user groups, contains curated data for targeted analysesDesigned for storing large volumes of unstructured or semi-structured raw data from various sources
Data processingData is pre-processed and converted before storageData is stored in its raw form and processed as required
Data accessControlled access for authorised department usersOpen access for different users in different departments; may require additional security measures
Data qualityHigh data quality standards, data is cleansed and transformed before loadingData quality may vary; may require additional processing prior to analysis
Data governanceWell-defined data governance guidelines to ensure data accuracy and consistencyData governance is still under development for data lakes due to the variety of data formats
Scalabilitymoderately scalable; limited by the original design and the amount of dataHighly scalable; can accommodate large volumes of different data
Query performancedeveloped for fast query performance with structured dataQuery performance can vary depending on the complexity and structure of the data
Differences between data mart and data lake
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Data Mart vs OLAP-/Data-Cube

Data marts and OLAP/data cubes are used to analyse data, but differ in their technical implementations and functions. These differences include

FeatureData MartOLAP-/Data-Cube
Data storageSeparate physical storage; can be connected to a data warehousenormally stored within a data warehouse
Data analysisOptimised for specific analysis requirements that are defined when the data mart is createdSupports complex, multidimensional analysis and modelling
Data aggregationData is often aggregated and summarised based on specific business requirementsData is organised in a multidimensional structure that enables complex aggregations and calculations
Data modelcan use different models (star scheme, snowflake scheme)primarily uses multidimensional schemas that are optimised for fast retrieval and aggregation
UpdatesData is updated periodically through ETL processesData cubes are precalculated and may need to be recalculated after data updates in the underlying data source
User interfaceAccess via various business intelligence tools and reportsAccess is often via special OLAP clients that have been developed for multidimensional analyses
Drill-down and slice-and-diceSupports basic drill-down and slice-and-dice functionsoffers advanced drill-down and slice-and-dice functions for in-depth data exploration
ScalabilityScalability may be limited by the scope and size of the data martHighly scalable, can process large amounts of data and complex analytical requirements
Areas of applicationSuitable for specific business reports and decision-makingIdeal for advanced analyses, business modelling and strategic planning
Differences between data mart and OLAP/data cube
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Data mart architecture

The architecture of a data mart serves to efficiently store, retrieve and analyse data for the respective business area. Here is a breakdown of the most important aspects:

Bottom-up approach

In a bottom-up approach, data marts are built from the data sources of a department or business unit. This enables a more flexible warehouse as it is built up step by step. The data is loaded into the mart from various sources and then structured in dimension tables for easy access.

Top-down approach 

In a top-down approach, a central data warehouse is created first. The data marts are then developed for the requirements of specific business areas. This approach is used in larger companies with more complex data requirements.

Federated approach

In a federated approach, several data marts are created that remain independent of each other. Users can access data from these data marts via a virtual layer without having to move the data. This approach is flexible and enables simple data access.

Data marts can process structured data from various sources, including internal operational systems and external data sources. They are designed to support specific business functions and data trends. However, they cannot store all of a company's data and are not suitable for data mining across the entire organisation.

Advantages of a data mart

Data marts offer numerous advantages for companies that want to Data analysis want to improve. Here are some of the key benefits of a data mart:

  • Improved accessibilityData marts provide a user-friendly interface with relevant and pre-processed information. This information helps departmental analysts to create reports and gain insights without extensive technical knowledge of data warehousing.
  • Faster analysisThe concentrated data size and optimised structure of data marts enable much faster query processing and reporting than data warehouses. This enables departmental teams to respond quickly to business requirements and make data-driven decisions.
  • Improved data qualityThe data consolidation process that accompanies the creation of data marts often involves data cleansing and transformation. This ensures the accuracy and consistency of the information within the data marts, leading to more reliable insights for analysis.
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Challenges and considerations

While data marts offer many benefits, it is important to recognise some potential challenges:

  • Limited data scopeBy focussing on data marts, the requirements for company-wide data exploration may not be met. Insights from a data mart may not provide a holistic view that can be critical for strategic decision making across the organisation.
  • Challenges when updating dataMaintaining data accuracy in a data mart requires continuous data refresh processes to ensure that the information reflects current business realities. Failure to do so can lead to outdated insights and hinder effective decision making.
  • Data governance: Data governance guidelines and procedures are crucial for ensuring the Data quality and consistency within the data mart. These aspects are necessary to maintain the reliability and overall value of the data.

Why should companies consider data marts?

Despite these challenges, data marts offer a valuable solution for organisations looking to empower their departments with data-driven decision-making capabilities. Their targeted approach, greater accessibility and faster analysis cycles make them a cost-effective way to gain valuable business insights in specific areas. Organisations can use data marts to improve the performance of their departments and gain a competitive advantage by carefully considering the needs of their departments and potential constraints.



Pat has been responsible for Web Analysis & Web Publishing at Alexander Thamm GmbH since the end of 2021 and oversees a large part of our online presence. In doing so, he beats his way through every Google or Wordpress update and is happy to give the team tips on how to make your articles or own websites even more comprehensible for the reader as well as the search engines.

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