Data integrity

Data integrity refers to the fact that data must be reliable and accurate throughout its lifecycle.

This means that the value must be:

• of high quality

• appropriate to the overall context

Isolated variable

Data quality

The values of the variables must be individually of good quality.

• Typing
• Validation

Access control

• Mode
• Properties

Overall coherence

conherence

An isolated variable can be considered to be of quality but become inconsistent depending on the context.

Overall coherence is initially managed by personalized validators which will validate the value of a variable in relation to others. But it also depends on the availability of the variable depending on the context.

Consistency

consistency

Consistency is validating the value of a variable in relation to other variables in the context. This does not only concern the variables themselves, such as their type. Of course there is a typed base validation.

The aim is to validate a variable in a configuration, the structured data and the user data based on the situation.

This is what we call a “context”—meaning the dataset to which a variable is linked.

For example, if a minimum value and then a maximum value are requested, the minimum must be lesser than the maximum. This is not type consistency, but this is yet consistency.

Context access control

Access control occurs as soon as an attempt is made to access a variable.

Remember, we talked about the hidden variable and disabled variable variables.

These properties become fully meaningful when managing overall consistency.

Why ask for the domain name of a service if we haven’t activated that service just before?