Management Through Fuzzy Matching Expressed By the Implementation of Algorithmic Processes

With the ever increasing proliferation of disparate complex data being collected and stored, it has never been more important that this information is accurate, clean, integrated, and often times in compliance with an expanding set of government regulations. This means that the data must be cleaned and standardized, duplicates must be identified and removed, and the individual data must be able to be joined or merged together in some way. However, it is often the case that this data does not have the same variables or values to make this possible with a simple Join or Merge. To that end, one has to employ a set of fuzzy logics or fuzzy matching. Simply put, fuzzy matching is the implementation of algorithmic processes (fuzzy logic) to determine the similarity between elements of data such as business names, people names, or address information. Fuzzy logic is used to predict the probability of data with non-exact matches to help in data cleansing, de duplication, or matching of disparate data sets. This paper shows the basics of using fuzzy logic by using both EXCEL and SAS functions. Fuzzy logic is in binary terms ultimately describable philosophical question witch is worth pursuing, a function closer to the way our brains work. By the process I used to aggregate data with witch I formed a number of partial truths, which I have further aggregate into higher truths in turn, so as when certain thresholds are exceeded, it could causes certain further results such as a motor reaction. Other kind of process is used in fields like neural networks, artificial intelligence and expert systems or other applications. Fuzzy logic is very important for the development of human capabilities for artificial intelligence, the representation of generalized human cognitive abilities in software such as faced with an unfamiliar task, the artificial intelligence system could find a solution. I have chosen to make a survey on management through fuzzy matching expressed by the implementation of algorithmic processes, because in recent years, the number and variety of applications of fuzzy logic have increased significantly. The applications range from consumer products from various kind of fields such as: medical instruments, electronics, to decision-support systems, and portfolio selection.