Data Wrangling Dynamics

Abstract

Data is one of the prerequisites for bringing transformation and novelty in the field of research and industry, but the data available is unstructured and diverse. With the advancement in technology, digital data availability is increasing enormously and the development of efficient tools and techniques becomes necessary to fetch meaningful patterns and abnormalities. Data analysts perform exhaustive and laborious tasks to make the data appropriate for the analysis and concrete decision making. With data wrangling techniques, high-quality data is extracted through cleaning, transforming, and merging data. Data wrangling is a fundamental task that is performed at the initial stage of data preparation, and it works on the content, structure, and quality of data. It combines automation with interactive visualizations to assist in data cleaning. It is the only way to construct useful data to further make intuitive decisions. This paper provides an overview of data wrangling and addresses challenges faced in performing the data wrangling. This paper also focused on the architecture and appropriate techniques available for data wrangling. As data wrangling is one of the major and initial phases in any of the processes, leading to its usability in different applications, which are also explored in this paper.