1 Copyright © 2017, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. Chapter 1 DOI: 10.4018/978-1-5225-1008-6.ch001 ABSTRACT This paper is intended to design a fuzzy based approach to assess standards and quality of big data. It also serves as a platform to organizations that intend to migrate their existing database environment to big data environment. Data is assessed using a multidimensional approach based on quality factors like accuracy, completeness, reliability, usability, etc. These factors are analysed by constructing decision trees to identify the quality aspects which need to be improved. In this work fuzzy queries have been designed. The queries are grouped as sets namely Excellent, Optimal, Fair and Hybrid. Based on the fuzzy data sets formed and the query compatibility index, a query set is chosen. A data set that has a very high degree of membership is assigned a fair query set. A data set with a medium degree of mem- bership is assigned a optimal query set. A data set that has a lesser degree of membership is assigned a Excellent query set. A data set which needs a combination of queries of all the above is assigned a hybrid query set. The fuzzy query based approach reduces the query compatibility index by 36%, compared to a normal query set approach. INTRODUCTION In today’s world with an increase in the amount of data processing and information requirement it is essential to develop strategies to effectively manage and assess the data for essential quality checks. The database forms the basis of day to day decisions taken by the organization. Data obtained from employees need to be periodically updated for effective utilization. In this work, an attempt has been made to assess data quality based on certain measures or parameters like Accuracy, Completeness, Reliability, Usability, etc as discussed by Pradheep et al in (2014). Based on these parameters the data set is queried to assess Fuzzy-Based Querying Approach for Multidimensional Big Data Quality Assessment Pradheep Kumar K. BITS Pilani, India Venkata Subramanian D. Hindustan Institute of Technology & Science, India