Data envelopment analysis (DEA) relies on efficiency scores being relative, and, therefore, the efficiency frontier is constructed by a complete set of decision-making units. In this research, a technique is proposed using a statistical sample of large datasets, where it is proven that the efficiency frontier is not so relative since it can be calculated from a sample of the dataset. In order to assist the technique, neural networks (NNs) are also employed. Furthermore, a unified technique is proposed to acquire the efficiency scores without the use of the DEA beforehand. By obtaining a representative sample, it is easier to draw conclusions about the entire structure of the dataset with a specific error probability and accuracy. A methodology is proposed to acquire a sample based on simple random sampling technique. The DEA-NN combination is applied to the sample, while tuning the sample dataset, in order to accumulate the efficiency frontier. The NN is brought to the optimum level, producing, therefore, reliable and promising results.