Why Data Envelopment Analysis is interesting?ĭata Envelopment Analysis is a method that enables us compare and rank records (stores, employees, factories, webpages, marketing campaigns etc) based on their features (weight, size, cost, revenues and other metrics or KPIs) without making any prior assumptions about the importance or weights of the features. That is why, in this article, I will cover the basic ideas and mathematical framework behind DEA and in the next post I will show you some novel applications of the algorithm on web applications. Despite this, today DEA is almost exclusively discussed within the context of business. I was even more amazed to see that the technique worked well outside of its usual applications (financial, operation research etc) since it could be successfully applied in Online Marketing, Search Engine Ranking and for creating composite metrics. When I first encountered the method 5-6 years ago, I was amazed by the originality of the algorithm, its simplicity and the cleverness of the ideas that it used.

Check out the package .dea to see the implementation of Data Envelopment Analysis in Java. Update: The Datumbox Machine Learning Framework is now open-source and free to download. The technique was first proposed by Charnes, Cooper and Rhodes in 1978 and since then it became a valuable tool for estimating production frontiers. It uses linear programming to estimate the efficiency of multiple decision-making units and it is commonly used in production, management and economics. Data Envelopment Analysis, also known as DEA, is a non-parametric method for performing frontier analysis.

Data envelopment analysis using excel