Abstract
Recently, Big Data analytics has been one of the most emerging topics in the business field. Data is collected, processed and analyzed to gain useful insight for their organization. Big Data analytics has the potential to improve the quality of life and help to achieve Sustainable Development Goals (SDG). To ensure that SDG goals are achieved, we must utilize existing data to meet those targets and ensure accountability. However, data quality is often left out when dealing with data. Any types of errors presented in the dataset should be properly addressed to ensure the analysis provided is accurate and truthful. In this paper, we have addressed the concept of data quality diagnosis to identify the outlier presented in the dataset. The cause of the outlier is further discussed to identify potential improvements that can be done to the dataset. In addition, recommendations to improve the quality of data and data collection systems are provided.
1. Introduction
In recent years, Big Data and data analytics has become exceptionally valuable in many areas such as health, finance and retail. The benefits of Big Data analytics are not restricted to a specific type of industry since analytics has proven vital for organisations to stay on top its competitors. The growing enthusiasm for making a decision based on data creates the importance of accurate and precise prediction. Agenda for Sustainable Development Goals (SDG) demands a significant investment in time and resources because of its call to ‘leave no one behind’ [1]. To ensure this agenda is achieved, Big Data is seen as one of the important elements that can unravel the disparities in society that were previously hidden. There has been some research conducted shown that 65 SDG indicators could directly or indirectly benefit from Big Data sources [2]. The use of Big Data analytics has the potential to help organisations to realize the opportunities in supporting SDG, while at the same time targeting growth through a range of advantages in various business activities, including supply chain activities [3].
