Radio Frequency Identification (RFID) refers to wireless technology that uses radio waves to automatically identify items within a certain proximity. It is being widely used in various applications, but there is reluctance in the deployment of RFID due to the high cost involved and the challenging problems found in the observed colossal RFID data. The obtained data is of low quality and contains anomalies like false positives, false negatives, and duplication. To enhance the quality of data, cleaning is the essential task, so that the resultant data can be applied for high-end applications. This chapter investigates the existing physical, middleware, and deferred approaches to deal with the anomalies found in the RFID data. A novel hybrid approach is developed to solve data quality issues so that the demand for RFID data will certainly grow to meet the user needs. © 2015 by IGI Global. All rights reserved.