DSS DEVELOPMENT PLATFORMS

6.8 DSS DEVELOPMENT PLATFORMS

Based on the technology levels described in the p r e c e d i n g section, t h e r e are several basic D S S development software platforms. T h e most i m p o r t a n t ones are t h e following:

• Write a customized DSS in a general-purpose programming language such as Visual Basic or COBOL. This strategy was viable in the 1980s a n d t h r o u g h o u t t h e 1990s, but very f e w organizations do it any longer. Sometimes, t h o u g h , ultra-large-

scale DSS, with m a n y interfaces to o t h e r CBIS, are constructed this way. • Use a fourth-generation language (4GL). T h e r e are several classes of 4 G L , such as data-oriented languages, spreadsheets, and financial-oriented languages. T h e s e tools can boost p r o g r a m m e r s ' productivity by a m a g n i t u d e of 10 or even m o r e over general-purpose languages. E v e n t h e new O L A P systems have e m b e d d e d 4GLs; for example, Cognos P o w e r H o u s e 4 G L and P o w e r H o u s e Web. For t h e most part, these languages have b e e n replaced by direct O L A P use on multidi- mensional data cubes and spreadsheets.

• Use OLAP with a data warehouse or a large database. Online analytical process- ing engines not only create multidimensional data cubes b u t also provide analysis tools that effectively function as "decision s u p p o r t suites." If a m a n a g e r wants to establish relationships in his or her data b u t p r e f e r s n o t to k n o w how it is done, data mining m e t h o d s can hide the m e t h o d s while producing reasonably effective results.

• Use a DSS integrated development tool (generator or engine). An integrated pack- age eliminates the n e e d to use multiple 4GLs. T h e best-known are Excel and Lotus 1-2-3. G e n e r a t o r s are m o r e efficient t h a n a collection of individual 4GLs, but they are subject to m o r e limitations.

• Use a domain-specific DSS generator. Domain-specific D S S generators are designed to build a highly structured system, usually in a functional area. They include O L A P systems specifically designed f o r analysis in retailing, m a n u f a c t u r - ing, and o t h e r areas.

• Develop the DSS using CASE methodology. As explained in Section 6.3, systems are developed by following a traditional life cycle, and C A S E tools can assist in

developing large, complex systems. So C A S E tools can be used in developing DSS. C A S E tools e n f o r c e consistency so t h a t a p r o t o t y p e cannot use nonexistent

data (see D S S in Action 6.23). • Develop a complex DSS by integrating several of the above approaches. This

a p p r o a c h is especially suitable for complex DSS. For example, p r o t o t y p e s can be developed with p r o g r a m m i n g languages and generators while t h e project is m a n - aged with a C A S E tool.

Most of these platforms have integrated links to t h e Web, and m a n y use W e b browser interfaces.