Assume that I have a variable whose distribution is skewed positively to a very high degree, such that taking the log will not be sufficient in order to bring it within the range of skewness for a normal distribution. What are my options at this point? What can I do to transform the variable to a normal distribution?
Try straight Box-Cox transform as per Box, G. E. P. and Cox, D. R. (1964), "An Analysis of Transformations," Journal of the Royal Statistical Society, Series B, 26, 211--234. SAS has the description of its loglikelihood function in Normalizing Transformations, which you can use to find the optimal $\lambda$ parameter, which is described in Atkinson, A. C. (1985), Plots, Transformations, and Regression, New York: Oxford University Press.
It's very easy to implement it having the LL function, or if you have a stat package like SAS or MATLAB use their commands: it's boxcox command in MATLAB and PROC TRANSREG in SAS.
Also, in R this is in the MASS package, function boxcox().
For positive skew (tail is on the positive end of the x axis), there are the square root transformation, the log transformation, and the inverse/reciprocal transformation (in order of increasing severity). Thus, if the log transformation is not sufficient, you can use the next level of transformation. Box Cox runs all transformations automatically so you can choose the best one.
Most software suites will use Euler's number as the default log base, AKA: natural log. You can use a higher base number to rein in excessively right-skewed data. How you do it syntax-wise depends on the software you are using.
If you need to get back out of you transformed values once estimations have been done, it might be a little easier to use this method because all you have to do is perform a exponential operator on your variable with whatever your log base was.