Comparison of Operational Systems with Data Warehouses

Table Comparison of Operational Systems with Data Warehouses

Difference	Operational System	Data Warehouse/Data Mart
Purpose	Primary function is to process orders, post journal entries, complete an operational task	Primary purpose is to provide access to information to manage the business by providing insight that leads to improved revenues, reduced costs, quality customer service, and alignment of strategic goals.
History	Current information with very little history	Larger amounts of history allow multiyear trend analysis, this year versus last year comparisons.
Timeliness	Real-time information	Information extracted on a periodic basis (hourly, daily, weekly). More recently, operational data warehouses may extract information in real-time or several times throughout the day.
Level of detail	Detailed data down to the line item or level of data entry	Aggregated data with varying degrees of granularity.
Response time	Fast inputs, but slow queries	Read-only; tuned for fast queries.
Table structure	Normalized tables in thousands	Parts of the data warehouse may be normalized but the parts business users query are normally denormalized star or snowflake schemas. The data warehouse will have fewer tables than the source systems have.
Dimensions	Rarely hierarchical groupings	Hierarchical groups give level of time, chart of accounts, product groupings, customer groups, and so on.
Reporting and analysis	Fixed reports by one detailed dimension (cost center, plant, order number)	Fixed or ad hoc reporting and analysis by multiple dimensions across all business functions.

Many customers new to BI want to skip the data warehouse and deploy a BI tool directly against the operational system. This may seem like a faster approach to business intelligence. In some instances, it may be a faster approach to business intelligence. In some instances, it may be an acceptable way to start with BI, and this approach addresses operational BI needs. However, for most companies, you will want a data warehouse when:

• You need to perform cross-subject or cross-functional analysis, such as products ordered versus inventory on hand. Such information may exist in two different systems or different modules within an ERP system and are thus combined into the data warehouse.

• You want to perform analysis on summary information, aggregated by time (month, quarter) or by some other hierarchy (product groupings). These hierarchies often don't exist in transaction systems, and even when they do, running such voluminous queries within a transaction system can slow it to the point of interfering with data entry.

• You need consistently fast reporting and analysis times. Because of their different purposes and design, data warehouses allow for faster queries than operational systems.

Data Marts

A data mart is a subset of the data coming from a central data warehouse. A data mart also may be used to feed a central data warehouse. Whereas a data warehouse is designed to serve the needs of the enterprise, a data mart may serve the needs of a particular business unit, function, process, or application. Because a data mart is aligned with a particular business requirement, some businesses may want to skip the data warehouse and build an independent data mart. According to industry research, fewer companies now do this^[] as independent data marts have been met with limited success and over time have a higher implementation cost.