Learn about the data mart and how it can improve team efficiency, reduce costs, and facilitate smarter tactical business decision-making in enterprises.
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A data mart is a subset of a data warehouse focused on a particular line of business, department, or subject area. Data marts make specific data available to a defined group of users, which allows those users to quickly access critical insights without wasting time searching through an entire data warehouse. For example, many companies may have a data mart that aligns with a specific department in the business, such as finance, sales, or marketing.
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Data marts, data warehouses, and data lakes are crucial central data repositories, but they serve different needs within an organization.
A data warehouse is a system that aggregates data from multiple sources into a single, central, consistent data store to support data mining, artificial intelligence (AI), and machine learning—which, ultimately, can enhance sophisticated analytics and business intelligence. Through this strategic collection process, data warehouse solutions consolidate data from the different sources to make it available in one unified form.
A data mart (as noted above) is a focused version of a data warehouse that contains a smaller subset of data important to and needed by a single team or a select group of users within an organization. A data mart is built from an existing data warehouse (or other data sources) through a complex procedure that involves multiple technologies and tools to design and construct a physical database, populate it with data, and set up intricate access and management protocols.
While it is a challenging process, it enables a business line to discover more-focused insights quicker than working with a broader data warehouse data set. For example, marketing teams may benefit from creating a data mart from an existing warehouse, as its activities are usually performed independently from the rest of the business. Therefore, the team doesn’t need access to all enterprise data.
A data lake, too, is a repository for data. A data lake provides massive storage of unstructured or raw data fed via multiple sources, but the information has not yet been processed or prepared for analysis. As a result of being able to store data in a raw format, data lakes are more accessible and cost-effective than data warehouses. There is no need to clean and process data before ingesting.
For example, governments can use technology to track data on traffic behavior, power usage, and waterways, and store it in a data lake while they figure out how to use the data to create “smarter cities” with more efficient services.
Data marts are designed to meet the needs of specific groups by having a comparatively narrow subject of data. And while a data mart can still contain millions of records, its objective is to provide business users with the most relevant data in the shortest amount of time.
With its smaller, focused design, a data mart has several benefits to the end user, including the following:
There are three types of data marts that differ based on their relationship to the data warehouse and the respective data sources of each system.
A data mart is a subject-oriented relational database that stores transactional data in rows and columns, which makes it easy to access, organize, and understand. As it contains historical data, this structure makes it easier for an analyst to determine data trends. Typical data fields include numerical order, time value, and references to one or more objects.
Companies organize data marts in a multidimensional schema as a blueprint to address the needs of the people using the databases for analytical tasks. The three main types of schema are star, snowflake, and vault.
Star schema is a logical formation of tables in a multidimensional database that resembles a star shape. In this blueprint, one fact table—a metric set that relates to a specific business event or process—resides at the center of the star, surrounded by several associated dimension tables.
There is no dependency between dimension tables, so a star schema requires fewer joins when writing queries. This structure makes querying easier, so star schemas are highly efficient for analysts who want to access and navigate large data sets.
A snowflake schema is a logical extension of a star schema, building out the blueprint with additional dimension tables. The dimension tables are normalized to protect data integrity and minimize data redundancy.
While this method requires less space to store dimension tables, it is a complex structure that can be difficult to maintain. The main benefit of using snowflake schema is the low demand for disk space, but the caveat is a negative impact on performance due to the additional tables.
Data vault is a modern database modeling technique that enables IT professionals to design agile enterprise data warehouses. This approach enforces a layered structure and has been developed specifically to combat issues with agility, flexibility, and scalability that arise when using the other schema models.
Data vault eliminates star schema's need for cleansing and streamlines the addition of new data sources without any disruption to existing schema.
Data marts guide important business decisions at a departmental level. For example, a marketing team may use data marts to analyze consumer behaviors, while sales staff could use data marts to compile quarterly sales reports. As these tasks happen within their respective departments, the teams don't need access to all enterprise data.
Typically, a data mart is created and managed by the specific business department that intends to use it. The process for designing a data mart usually comprises the following steps:
With the groundwork done, you can get the most value from a data mart by using specialist business intelligence tools, such as Qlik or SiSense. These solutions include a dashboard and visualizations that make it easy to discern insights from the data, which ultimately leads to smarter decisions that benefit the company.
While data marts offer businesses the benefits of greater efficiency and flexibility, the unstoppable growth of data poses a problem for companies that continue to use an on-premises solution.
As data warehouses move to the cloud, data marts will follow. By consolidating data resources into a single repository that contains all data marts, businesses can reduce costs and ensure all departments have unfettered access to data they need in real-time.
Cloud-based platforms make it possible to create, share, and store massive data sets with ease, paving the way for more efficient and effective data access and analysis. Cloud systems are built for sustainable business growth, with many modern Software-as-a Service (SaaS) providers separating data storage from computing to improve scalability when querying data.
IBM Db2 Warehouse on Cloud is an elastic cloud data warehouse that offers independent scaling of storage and compute. Smaller data marts can use the Flex One feature, which is an elastic data warehouse built for high-performance analytics. This system is deployable on multiple cloud providers, starting at 40 GB of storage.
Another option worth considering is IBM InfoSphere® Master Data Management (MDM). This customizable system manages all aspects of your critical enterprise data, giving users access in a single-trusted view. Through this streamlined dashboard, users are empowered to conduct detailed analysis, gain actionable insight, and ensure total compliance with data governance and policies across the entire enterprise.
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