The Key Differences Between Data Marts and Data Warehouses

As data volume continues to grow exponentially, more and more companies are investing in data analytics to guide business strategy. However, with so much data available from different sources and departments, organizations need robust systems to collect, store and manage their data. This is where data marts and data warehouses come in. But what exactly is the difference between these two important data management tools?

In this comprehensive guide we’ll explain everything you need to know about data marts versus data warehouses. including

  • Definitions and key characteristics
  • Use cases
  • Main differences
  • Pros and cons of each approach
  • Relevant careers

Let’s dive in!

What is a Data Mart?

A data mart is a subset of a data warehouse that contains data specific to a particular business function or department. For example the marketing department may have their own dedicated data mart that only contains marketing campaign data.

Some key characteristics of data marts include:

  • Focused on a single subject (e.g. marketing, sales, HR etc)
  • Typically less than 100GB in size
  • Quick to build – can be up and running in just a few weeks/months
  • Contains summarized, tactical data for analysis
  • Used to guide department-specific decisions

Data marts are easy to implement and manage, and provide users with quick access to their own dedicated data repository. They act as a sandbox where teams can analyze, manipulate and report on data relevant to their function, without trawling through the wider company data warehouse.

What is a Data Warehouse?

A data warehouse is a much larger, centralized data repository that brings together data from multiple sources across the entire organization. This includes both operational systems and external data feeds.

Typical characteristics of data warehouses include

  • Enterprise-wide, cross-functional data from all departments/systems
  • Very large – usually in excess of 100GB and often in the terabytes
  • Collated from numerous internal and external sources
  • Raw data combined with summary data
  • Used to guide high-level strategic decisions for the entire company
  • Long development cycles – often 6 months+

As you can see, data warehouses are far more extensive than data marts. They provide a ‘single source of truth’ for company-wide reporting and analysis. The trade-off however is that they take much longer to build and are more complex to maintain.

Data Mart Use Cases

Data marts are ideal for tactical analysis within a particular business function. Some examples include:

  • Marketing – Analyzing campaign performance, customer segmentation, brand positioning etc.
  • Sales – Tracking sales reps, regional targets, sales pipelines/forecasting.
  • Finance – Financial reporting, budgeting, identifying spending patterns.
  • HR – Recruitment metrics, compensation analysis, turnover rates.
  • Support – Customer ticket analysis, common issues, satisfaction scores.

In each case, the data mart provides quick access to summarized, department-specific data to guide day-to-day decision making.

Data Warehouse Use Cases

Data warehouses enable cross-functional, strategic analysis across the organization. Typical use cases include:

  • Company-wide reporting – Holistic view of performance across departments.
  • Systems integration – Combining data from multiple systems to identify patterns.
  • Data mining – Discovering trends and insights hidden within large datasets.
  • Machine learning – Training ML models on broad datasets.
  • Executive dashboards – Tracking KPIs and metrics for strategic decisions.
  • Predictive analytics – Forecasting future performance.

Without a centralized data warehouse, this kind of cross-functional analysis would be extremely difficult if not impossible.

Main Differences Between Data Marts and Data Warehouses

Characteristic Data Mart Data Warehouse
Scope Single department or function Enterprise-wide
Data sources Specific systems for one department Multiple sources across entire company
Volume <100GB >100GB (often TBs)
Speed to deploy Weeks/months 6+ months
Data structure Summarized for analysis Raw data + summaries
Purpose Tactical decisions Strategic decisions

Top Down vs Bottom Up Approaches

There are two main approaches when it comes to designing data warehouse architectures:

Top Down

  1. Build the enterprise data warehouse first as the centralized repository.
  2. Then create data marts later as required for specific departments.

Bottom Up

  1. Start by building decentralized data marts for each department.
  2. Gradually combine data marts into an enterprise data warehouse.

The top down approach maintains data consistency and a ‘single source of truth’ from day one. However, bottom up may be faster to get up and running if the initial focus is department-specific analysis.

In practice most organizations employ a hybrid model, balancing the pros and cons of both approaches.

Relevant Careers

Data marts and warehouses are critical tools for data-driven organizations. There are a number of specialist roles involved in designing, building, managing and analyzing these systems, including:

  • Data Architect
  • ETL Developer
  • Data Warehouse Engineer
  • Database Administrator
  • Business Intelligence Analyst
  • Data Analyst
  • Data Scientist

These professionals help organizations maximize the value of their data assets by building fit-for-purpose data management architectures. Their skills are in high demand as data volumes continue exponential growth.

Key Benefits of Data Marts and Warehouses

Data Mart Benefits

  • Simple to implement and manage
  • Quick access to department-specific data
  • Easy to customize for different teams
  • Enable tactical analysis and decisions
  • Less costly than an enterprise warehouse

Data Warehouse Benefits

  • Centralized data from all sources
  • Facilitates cross-functional analysis
  • Supports strategic business decisions
  • Data mining and machine learning capabilities
  • Single source of truth for the organization

Should You Have Both?

For larger enterprises, having both data marts for departmental analysis along with an enterprise data warehouse makes sense. However, smaller companies may choose to start with data marts only until there is a need for company-wide analytics capabilities.

Cloud-based data warehouse solutions have made enterprise repositories much more accessible and affordable for organizations of all sizes. So even smaller companies can now reap the benefits of consolidated data for strategic analysis and decision making.

The right choice depends on your organization’s size, budget and analytics needs. For most enterprises today, a combination of data marts and an enterprise data warehouse is the best approach to balance flexibility, speed and business insights.

Summary and Key Takeaways

  • Data marts provide localized, department-specific data for tactical analysis.
  • Data warehouses enable cross-functional strategic analysis across the organization.
  • Main differences include scope, volume, speed, data structure and purpose.
  • Top down builds the data warehouse first then data marts, while bottom up starts with marts.
  • Specialist roles like data engineers and business analysts work on these systems.
  • Cloud data warehousing has made enterprise repositories more accessible.
  • A hybrid approach makes sense for most organizations.

In today’s highly competitive markets, leveraging data marts and warehouses for robust analytics is a must. Understanding the core differences between these two technologies will allow you to build an optimal data architecture to accelerate insights and guide better business decisions.

data mart vs data warehouse

Data mart vs. data warehouse

To help determine whether a data warehouse or a data mart — or some combination of the two approaches — best meets your organizations needs, heres a more detailed comparison of how they differ and what each one offers to users:

  • Size/data volume. A data warehouse typically contains at least 100 GB of data, and many have terabytes or more — often much more. Data marts can also hold terabytes of data but are usually smaller than data warehouses. One exception is that a data mart in a large organization might well be bigger than a data warehouse in a smaller one.
  • Focus and scale. An enterprise data warehouse provides an enterprise-wide view of an organizations business operations, while a data mart delivers a more granular view of a specific business unit, subject area or other aspect of operations. In many cases, a data mart is a subset of the data warehouse in an organization.
  • Data sources. Data warehouses commonly store data from various business applications and systems throughout an organization. A data mart has a more limited number of source systems related to its specific focus, or it might be fed directly by a data warehouse. Both types of repositories can also store external data sets needed for analytics uses.
  • Ownership and control. A centralized data warehouse is funded, deployed and managed at the enterprise level. Business units might still own their data, but IT or a central data management team oversees the data warehouse, and the CIO or chief data officer usually is responsible for it. Data marts generally are controlled by the department or business unit theyre built for, although central IT or data management staffers might help manage and support a data mart.
  • Ease of user access. Access to a data warehouse tends to be tightly controlled because of its enterprise nature, with users limited to data sets that are relevant to their roles. In addition, using a data warehouse can be more suited to skilled analytics professionals than business users. A data mart is generally designed for easier access and use by business analysts and other end users in a business unit, as well as BI and data analysts assigned to the unit.
  • Decision-making use cases. Both data warehouses and data marts enable BI and analytics applications that can help organizations make better tactical and strategic business decisions. But a data warehouse can be used to support decision-making for individual business units and an organization as a whole, while the use of a data mart is usually limited to a single unit. Data marts are also more suited to aiding in operational decision-making than data warehouses.
  • Speed of decision-making. The size of data warehouses and the breadth of the data sets they contain complicate the data analysis process. It can take longer to run queries, analyze data and create reports before the results are available for use by decision-makers. Because data marts are more narrowly focused, they tend to enable a shorter lead time on decision-making uses.
  • Startup and support costs. Not surprisingly, a data warehouse likely will have higher development, deployment and support costs than a data mart. That applies to both on-premises and cloud-based platforms. However, if an organization has various data marts for different business units, the combined cost of deploying and supporting them can add up.
  • Development and build time. Building a data warehouse is often a big-budget, multiyear project. A data mart is more likely to take months or maybe just weeks to build. Again, though, creating a series of data marts for different units in an organization can be a longer process.

This summarizes the biggest differences between data warehouses and data marts.

Data marts and data warehouses both play key roles in the BI and analytics process. Here’s how they differ and how they can be used to help drive business decisions.

data mart vs data warehouse

During the early stages of my IT career, there was no concept of organizational data sharing or an understanding of datas global value to the enterprise. IT departments primarily designed and administered applications that focused on day-to-day business operations. Although IT produced reports to facilitate business decision-making, data was only offloaded to separate environments when queries began to negatively affect the performance of operational systems.

As reporting systems matured and their popularity skyrocketed among business units, enterprises realized that the real value of all the data they generated was its ability to help drive business decision-making. Once disparate departmental data stores came to be seen as strategic assets that would provide intrinsic benefits at an organizational level, IT teams began to combine their contents in data warehouses — and eventually data marts.

Both data warehouses and data marts are special-purpose platforms used to ingest, store and process data for BI and analytics applications. The primary difference between them is data warehouses are centralized repositories that typically store data from multiple business units and subject areas, while data marts are built for individual units or groups of users. In their purest form, data warehouses support decision-making at an enterprise level and data marts do so at a departmental level.

The challenge with attempting to define and compare a data warehouse vs. data mart is the criteria used to categorize them can be somewhat fluid. There are departmental platforms that contain large amounts of data from different source systems. Although they might meet the data mart criteria of providing decision-making information to a specific department, their size and the high level of detailed data they hold could also categorize them as data warehouses.

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But to help make the differences between the two approaches to storing analytical data clearer, lets look more closely at the characteristics and attributes that generally set data marts and data warehouses apart and how they separately fit into an overall data management strategy.

As mentioned above, the main goal of a data warehouse is to provide a centralized data repository that enables more informed and insightful decisions at an enterprise level. From C-level executives to business managers, business analysts, operational workers and others, data warehouses serve a wide and varied user base.

Virtually any data the organization creates or collects could potentially be ingested into a data warehouse. Data managers, data warehouse analysts and other IT specialists often perform a high level of analysis to identify and evaluate potential data sources and then work to integrate, consolidate and cleanse the data sets being ingested.

A key benefit of data warehouses for BI and analytics uses is their ability to provide a global view of customers, suppliers, service providers and business partners that have relationships spanning multiple lines of business.

The primary use case of a data mart is to meet the needs of users requiring access to more granular data sets in a particular subject area. The goal is to provide those users with fast access to the data that is most relevant to their business and information needs.

A good example is an organizations sales department. The department manager needs to see data on products, customers and the sales teams performance metrics. The amount of time it would take to access and analyze the data in an enterprise data warehouse is longer and less efficient than using a repository purposely designed to meet the units specific business needs.

In addition, data marts often differ from data warehouses in the type of data they store. Many contain summary data to accelerate analysis and reporting, as opposed to the full detailed data sets. In such cases, the data is refined and customized to meet the specific needs of the target audience. Data mart administrators also build additional logical and physical constructs to speed data access performance.

One of the traditional sources for a data mart is a centralized data warehouse. Because data warehouses contain data at an enterprise level, theyre excellent sources for feeding data marts. But data marts often also take feeds from other decision-support data stores and from operational systems.

Data Mart vs Database vs Data Warehouse vs Data Lake Explained

What is the difference between data warehouse and data mart?

Both data warehouses and data marts enable BI and analytics applications that can help organizations make better tactical and strategic business decisions. But a data warehouse can be used to support decision-making for individual business units and an organization as a whole, while the use of a data mart is usually limited to a single unit.

What is a data mart?

Data marts allow one department or business unit, such as marketing or finance, to store, manage, and analyze data. Individual teams can access data marts quickly and easily, rather than sifting through the entire company’s data repository.

Should you choose a data mart or a data warehouse?

The choice between a Data Mart and a Data Warehouse hinges on a business’s specific requirements. For organizations seeking insights into particular departments, like the sales department, or looking for swift data solutions, Data Marts might be the way to go.

What factors drive the data warehouse data mart comparison?

The following are the key factors that drive the Data Warehousing Data Mart comparison: The objective of a Data Warehouse is to act as a centralized repository of data for all business lines and departments in an organization. It is the primary search point for any data asset. It contains data about multiple objects.

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