What are issues in design and development of BI and DSS?

by Daniel J. Power
Editor, DSSResources.COM

Mismatch between the perspectives of the developer and the decision maker

Decision makers, on one hand, have very little knowledge about what it takes to design and develop a decision support system. On the other hand, a programmer or developer may not have a fair idea of how complex business decision making has become in todayís highly turbulent times. Technology selection may seem puzzling and ambiguous to decision maker while the preferences, needs and expectations of a decision maker may not fit the process of system design and development. This mismatch between perspectives leads to complexities. But at the same time, this increases the likelihood of creation of an effective and appropriate DSS.

Mis-identification of specific requirements

Most of the time even decision makers have no clue about what to expect from a decision support system. This is especially the case when managers need a tool to aid in resolving ill-structured or unstructured problems. It is easy to decide what functionality a DSS must possess when problems are routine and structured. Managers can seek help from their team members and conduct brainstorming sessions, in order to chart out the functions and benefits of a decision support system.

Technology selection

technologies are changing. Interactive and responsive decision support systems that maximize effective are desired. In such a scenario, the selection of technology plays a crucial role. However, itís not always necessary to use the latest version of a particular platform. It completely depends on the objectives one wishes to achieve. However, a DSS development company may push you for the latest technology, which will certainly add to your expenses.

Choosing an approach to software design and development

There is no standard approach or methodology to design the best decision support system. There are three main approaches to system design and development: 1) the traditional system development life cycle (SDLC) approach; 2) rapid prototyping and 3) end-user approach. You may want to choose a methodology that ensures that the final product accomplishes your objectives. But each approach has its pros and cons, making it difficult to choose the best one.

Difficulty in Quantifying the Data

A decision support system relies on quantifiable data. Consequently, itís difficult to analyze intangible or indefinable data. Even though a DSS may quantify some of these aspects, the end result must be duly considered by the decision makers. They must use their own judgment when making the final decision.

Unaware of Assumptions

As a decision maker, you may not be fully aware of the assumptions used in a decision support system when analyzing data for a specific problem. Making decision without considering uncontrollable factors may prove to be dangerous. A decision maker must realize that a computerized DSS is only a support tool. You must consider an unstructured or partially structured situation in-depth and analyze the constraints and assumptions.

System Design Failure

Decision support systems are designed for the specific needs of a decision maker. If you donít know what you want a DSS to do or how it should help you, it will be difficult to design a system that fits your needs.

Difficulty in Collecting Required Data

As a decision maker, you must realize that itís not possible to capture all of the desired data automatically. While some data is difficult to record, some cannot be recorded at all.

Lack of Technical Knowledge of Users

Although decision support systems have become much more understandable and user friendly over the years, many decision makers still find it difficult to use them.

Last update: 2018-08-13 08:18
Author: Daniel Power

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