Business intelligence has transformed the way critical decisions can be made in the digital age. Early generations included Decision Support System (DDS) that has both a data warehouse system and online analytical processing (OLAP) system. However, there are important differences in the data warehouse system and OLAP system regarding suitable applications, system architecture, and system functions.
A data warehouse provides data for decision making. The online analytic processing or OLAP helps data analysis and visualization. Data warehousing systems are designed to support OLAP. The DBMS that runs decision making queries is the Decision Support System.
The purpose of the data warehouse is detailed historical data whereas the OLAP server is for analytics. Even though the two have differences, they can work together to achieve business goals.
Regarding access, data warehouses have read-only access and singular list-oriented queries and reports. OLAP servers have both read and write access with iterative and comparative analytic investigation access modes. Also, where a data warehouse can have slow query response, the OLAP server is fast with more consistent query response.
Regarding data storage, the data warehouse has cross-subject data, a single subject area data mart and houses historical data. With the OLAP server, there are many cubes where each cube is a single subject area.
In an OLAP server, the data is dimensional and hierarchical. The design goal of the data structure in a data warehouse is list-oriented query whereas the OLAP server design goal is analysis. However, the data warehouse can also store terabytes of data whereas the OLAP server typically deals more with gigabytes. Comparatively, the hardware investment in a data warehouse is not cheap whereas the cost of the OLAP server can vary.
Regarding implementation, the data warehouse is also slow taking months or years whereas the OLAP implementation can happen in days or weeks. And, the adaptability of a data warehouse is low whereas the OLAP server is easily modified.
A Decision Support System (DSS) analyzes business data and presents it so that users can make business decisions. The data warehouse for decision support takes data from different sources and then uses advanced tools and technologies to support the development of Decision Support Systems.
The Decision Support System starts with information sources, then moves through the data warehouse, to the OLAP servers that then go to the client for querying, reporting and data mining. OLAP provides high levels of functionality for decision making in analyzing large collections of historical data stored by the data warehouse.
With DSS focused on flexibility and adaptability to accommodate changes in the environment and decision making approach of the user, advancements are likely to continue to evolve over the next few years.
Overall, the data warehouse and OLAP can work together to provide information needed to support business goals.
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