What is the difference between data-based, rule-based, and
knowledge-based systems?
The chart below summarizes the key differences between
data-based, rule-based, and knowledge-based systems:
| |
Data-Based system |
Rule-Based system |
Knowledge-Based system |
|
Can process |
|
|
|
|
Can output |
Information |
|
Information |
|
Decisions |
|
Real-Time Decisions |
|
|
Information |
|
Decisions |
|
Answers |
|
Expert Advice |
|
Recommendations |
|
|
Commonly used for |
Hard-coded rules |
Enterprise rules |
Departmental rules |
|
Ideal for |
IT/System rules |
Simplistic business rules |
Complex business rules |
|
Best for these types of applications* |
Traditional information systems |
|
|
Advising |
|
Product Selection |
|
Recommending |
|
Troubleshooting |
|
|
Domain scope |
- |
Broad logic |
Deep logic |
|
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The problem with legacy
data-based systems is that they are hard-coded and limited to
processing data and outputting information. It's still up to the human
being to analyze all the information to make decisions and
recommendations. The result is often information-overload.
Rule-based systems
process data and output information, but they also process rules and
make decisions. They are good at processing lots of simple business
rules with broad logic. They are commonly used for
real-time decisioning systems and compliance systems.
Knowledge-based systems
also process data and rules to output information and make
decisions. In addition, they also process expert knowledge to output
answers, recommendations, and expert advice. They are good at
processing deep logic and very complex business rules. They are commonly
used for advising systems, expert systems, and knowledge automation.
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