Challenges in Data Quality and Complexity of Managing Data Quality Assessment in Big Data
D.B.Shanmugam1, J.Dhilipan2, A.Vignesh3, T.Prabhu4

1D.B.Shanmugam, Asst.Prof , Department of Computer Applications,SRM Institute of Science & Technology, Ramapuram Campus, Chennai.
2Dr.J.Dhilipan, Professor & Head, Department of Computer Applications, SRM Institute of Science and Technology, Ramapuram Campus, Chennai.
3A.Vignesh, Asst.Prof , Department of Computer Applications, SRM Institute of Science & Technology, Ramapuram Campus, Chennai.
4T.Prabhu, Associate Professor, Department of Computer Applications, Dr.MGR Educational & Research Institute Chennai.

Manuscript received on August 01, 2020. | Revised Manuscript received on August 05, 2020. | Manuscript published on September 30, 2020. | PP: 589-593 | Volume-9 Issue-3, September 2020. | Retrieval Number: 100.1/ijrte.E5643018520 | DOI: 10.35940/ijrte.C5643.099320
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Abstract: High Quality Data are the precondition for examining and making use of enormous facts and for making sure the estimation of the facts. As of now, far reaching exam and research of price gauges and satisfactory appraisal strategies for massive records are inadequate. To begin with, this paper abridges audits of Data excellent studies. Second, this paper examines the records attributes of the enormous records condition, presents high-quality difficulties appeared by large data, and defines a progressive facts exceptional shape from the point of view of records clients. This system accommodates of big records best measurements, best attributes, and best files. At long last, primarily based on this system, this paper builds a dynamic appraisal technique for records exceptional. This technique has excellent expansibility and versatility and can address the troubles of enormous facts fine appraisal. A few explores have verified that preserving up the character of statistics is regularly recognized as hazardous, however at the equal time is considered as simple to effective basic leadership in building aid the executives. Enormous data sources are exceptionally wide and statistics structures are thoughts boggling. The facts got may additionally have satisfactory troubles, for example, facts mistakes, lacking data, irregularities, commotion, and so forth. The motivation behind facts cleansing (facts scouring) is to pick out and expel mistakes and irregularities from facts so as to decorate their quality. Information cleansing may be separated into 4 examples dependent on usage techniques and degrees manual execution, composing of splendid software programs, records cleaning inconsequential to specific software fields, and taking care of the difficulty of a kind of explicit software area. In these 4 methodologies, the 1/3 has terrific down to earth esteem and may be connected effectively. 
Keywords: Total Data Quality Management, Data Quality Metrics, Data Quality Assessment.