Data envelopment analysis (DEA) is a method to measure how efficient a decision-making unit is compared to others. When dealing with big data, traditional DEA methods can be slow. This study introduces new algorithms to speed up the process. By dividing large groups of decision-making units into smaller ones and identifying the most efficient ones, the researchers were able to evaluate the efficiency of the less efficient units more quickly. They also developed a method to handle cases with multiple inputs or outputs. Testing these methods on simulated data showed promising results in various scenarios.