Towards Improved Data Analytics Through Usability Enhancement of Unstructured Big Data

Abstract

A high volume of unstructured data is being generated from diverse and heterogeneous sources. The unstructured data analytics process is used to extract valuable insights from these unstructured data sources but unlocking useful and usable information is critical for analytics. Despite advancements in technologies, data preparation requires an inordinate amount of time in unstructured data manipulation into a usable form. Although several data manipulation and preparation techniques have been proposed for unstructured big data, relatively limited research has addressed the usability issues of unstructured data. This study identifies the usability issues of unstructured big data for the analytical process to bridge the identified gap. The usability enhancement model has been proposed for unstructured big data to facilitate the subjective and objective efficacy of unstructured big data for data preparation and manipulation activities. Moreover, concept mapping is an essential element to improve the usability of unstructured big data incorporated in the proposed model with usability rules. These rules reduce the usability gap between data availability and its usefulness for an intended purpose. The proposed research model will help to improve the efficiency of unstructured big data analytics.