Decision Support Systems and Data Warehousing
Many Vendors Offer Analytical Software to Aid Data Analysis
Three concepts should be evaluated in terms of their usability for decision support and relationship to the so-called ‘real data warehouse.’ They are:
- Virtual data warehouse
- Multidimensional online analytical processing (OLAP)
- Relational OLAP.
Virtual Data Warehouse
The virtual data warehouse promises to deliver the same benefits as a real data warehouse but without the associated amount of work and difficulty. The virtual data warehouse concept can be subdivided into the surround data warehouse and the OLAP/data mart warehouse. In a surround data warehouse, legacy systems are ‘surrounded’ with methods to access data without a fundamental change of the operational data. The surround concept negates a key feature of the real data warehouse, which integrates operational data in a way that allows users to use it to meet business analysis objectives.
The data structure of a virtual data warehouse does not lend itself to DSS processing. Legacy operational systems were built to ease updating, writing and deleting and not with simple data extraction in mind. As well, there is a minimal amount of stored historical data. A real data warehouse with its two-to-five years of stored data, provides a far superior means of analyzing trends.
In direct OLAP/data marts, legacy data is transferred directly to the OLAP/data mart environment. This approach falls short of being a real data warehouse. If only a few, small applications were feeding a data mart, the approach would be acceptable, however, that there are many applications and thus many OLAP/data mart environments, each requiring a customized interface, especially as the number of OLAP/data marts increases.
Multidimensional On-line Analytical Processing (OLAP)
Multidimensional database technology is a definite step up from the virtual data warehouse. It is designed for executives and analysts who want to look at data from different perspectives and have the ability to examine summarized and detailed data. When implemented together with a data warehouse, multidimensional database technology provides more efficient and faster access to corporate data. Proprietary multidimensional databases facilitate the organization of data hierarchically in multiple dimensions, allowing users to make advanced analyses of small portions of data from the data warehouse.
As a stand-alone technology, multidimensional OLAP is inferior to a real data warehouse for a variety of reasons. The main drawback is that the technology is not able to handle more than 20 to 30 gigabytes of data, which is unacceptable for most larger corporations, whose data storage needs range in the 100 gigabyte to several terabyte range.
Furthermore, multidimensional databases do not have the flexibility and measurability required of today’s decision support systems because they do not support the necessary ad hoc creation of multidimensional views of products and customers. Multidimensional databases should be considered for use in smaller organizations or on a departmental level only.
Relational OLAP
Relational OLAP is also used with many decision support systems and provides sophisticated analytical capability in conjunction with a data warehouse. Unlike multidimensional database technology, relational OLAP lets end users define complex multidimensional views and analyze them. These advantages are only possible if certain functionalities are incorporated into the relational OLAP.
Users must be removed from the process of generating their own structured query language (SQL). Multiple SQL statements should be generated by the system for every analysis request to the data warehouse, so that a set of business measurements (e.g., comparison and ranking measurements) is established, a process which is essential to the appropriate use of the technology. The shortcoming of relational OLAP technology works well in conjunction with a data warehouse, however, by itself, the technology is limited.
The data warehouse is still the most suitable technology for larger firms. Integrated, cleansed data from legacy systems together with historical information about the business, makes a properly implemented data warehouse the primary choice for decision support.
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