A data-compression analyzer can rapidly make a binary decision to compress or not compress an input data block or can use a slower neural network to predict the block's compression ratio with a regression model. A Concentration Value (CV) that is the sum of the squares of the frequencies and a Number of Zero (NZ) symbols are calculated from an un-sorted symbol frequency table. A rapid decision to compress is signaled when their product CV*NZ exceeds a horizontal threshold THH. During training, CV*NZ is plotted as a function of compression ratio C % for many training data blocks. Different test values of THH are applied to the plot to determine true and false positive rates that are plotted as a Receiver Operating Characteristic (ROC) curve. The point on the ROC curve having the largest Youden index is selected as the optimum THH for use in future binary decisions.