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Let's see the different techniques and I'll explain what kind of data they apply to and how to effectively use them.

The best thing I can say at this point is to use common sense when evaluating the need for data validation.

When you think of data validation, what's the first thing that comes to your mind?

Is it, by any chance, to enter numbers when an application is expecting numbers out of you, the user?

For the sake of the customer and the financial institution that will be loaning the money.

What this means is data validation, in order to be as successful as it can be, must be implemented at all parts that get the data, processes it and saves or prints the results.

Let's take the time and list those parts here and explain why they should be considered.

That is, of course, once the data validation is debugged.

But making sure that these are implemented in the development process rather than after the program is done will help minimize the burdon of integrating data validation in your application. Essentially everything depends on the reason why you'd want to do data validation, it's degree of importance (as far as the data available to the application) and the actually type of the data as well. Let's say you're making a mortage calculation program (that will be used by professionals all over the the continent).

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If the initial data ends up being wrong, can you imagine just how bad the amortization table would end up being at the end of the calculation?

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