Someone in my organization just sent me a scale they developed (modified items from five separate scales used in published research) to measure an employee's readiness for change. They tested the internal consistency of the 15-item scale with Cronbach's alpha. The result was .995, and the alpha after each item was deleted was .995 (for each item). I'm pretty sure that results should not be this high and that there is a problem with the items themselves, the response scale, or perhaps the method of data collection. On the face of it, the individual items don't appear to be redundant. I know that a scale should also be tested for test-retest reliability, convergent and discriminant validity. What would be the first thing I should check to figure out what might be causing these results?
When I have ran into this kind of error, I had made a mistake during the coding process (I'm using SPSS). I usually use numbers like 99 or 999 to mark missing values. Once I forgot to specify the missing values to be excluded at the variable view, and I begun the scale analysis. I got very high C-alphas, around 0.95, just like you.
I agree with @rolando2, as I believe mistakes made during the coding process could be the cause of such a high $\alpha$. Though my guess is that it is due to item redundancy - something you mentioned in your question. Another plausible cause could be your sample. I have analyzed survey data from many sources, and from my experience, it is clear that responses to organizational surveys are often quite lazy, in that it is common to see many respondents answer the same way across the entire survey. This is why I prefer organizational surveys that are both short and easy to read, as doing both makes it less likely for respondents to answer lazily.