Data Marts

A Data Mart [DM] is a version of a data warehouse [DW].

Like data warehouses, data marts contain a snapshot of operational data. This is used by business decision makers to guide strategy and decisions by analyzing past trends and experiences.

The key difference between a data warehouse and a data mart, is that the creation of a data mart is based on the business requirements of a specific group of business users, who need certain grouping and configuration of select data. A data mart configuration prvoides easy access to relevant information.

There can be multiple data marts inside a single corporation; each one relevant to one or more business units for which it was designed.

DMs may or may not be dependent or related to other data marts in a single corporation. Data marts may also be dependent or independent of data warehouses. A datawarehouse dependent data mart is a logical subset [view] or a physical subset [extract] of the larger data warehouse,

Each department or business unit is usually deemed the owner of its data mart including all the hardware, software and data. This enables each department to use, manipulate and develop their data any way they see fit; without altering information inside other data marts or the data warehouse.

In other deployments where conformed dimensions are used, this business unit ownership will not hold true for shared dimensions such as customer, product, etc.

Advantages Of Data Marts

1. The main benefits of using data marts include:
2. Ease of access to frequently needed data by specific users
3. Creates collective view by a group of users
4. Improves end-user response time
5. Ease of creation
6. Lower cost than implementing a full Data warehouse
7. Potential users are more clearly defined than in a full Data warehouse
8. Provides for special data model or schema: e.g., to restructure for OLAP
9. Security - separates an authorized data subset selectively
10. Expediency - can bypass the data governance and authorizations required to incorporate a new application on the Enterprise Data Warehouse
11. A Proving Ground - to demonstrate the viability and ROI (return on investment) potential of an application prior to migrating it to the Enterprise Data Warehouse
12. Politics - acts as a coping strategy for IT (Information Technology) in situations where a user group has more influence than funding or is not a good citizen on the centralized data warehouse. May also be used as a coping strategy for consumers of data in situations where a data warehouse team is unable to create a usable data warehouse.

Next: Comparing Microsoft And Teradata BI Environments

Back To Top