Decision table or data rule?

A data rule is an inference rule that uses input values and a data source (CSV resource) to derive one or more attribute values. A decision table is an inference rule that uses input values and/or conditions. It is a useful way to model complicated logic. It is a compact graphical representation of a decision-making process that results in an action.

One might argue in favor of data rules or decision tables. Arguments to use one or the other are enumerated below.

1. When logic and decision making needs to be modeled explicitly and be an integral part of the model, a decision table is preferred.
2. When the logic isn't all that complex, but consists of many entries, a data rule is preferred. E.g. a discount percentage based on individual postal codes (not ranges).  
3. When the logic is maintained, changed and managed by the customer who deliberately avoids the use of studio, a data rule might be used (however not preferred, since this undermines Blueriq's philosophy of the customer in control of his own rules).
4. When the frequency of changes in the rules is very high, one might argue that a change in DTAP-strategy is necessary and allowed, for instance by means of a staging area. If so, data rules are preferred.