CONCEPTUALIZING BIG DATA QUALITY FRAMEWORK FROM A SYSTEMATIC LITERATURE REVIEW PERSPECTIVE - Ijab, Mohamad Taha; Mat Surin, Ely Salwana; Mat Nayan, Norshita (Malaysian Journal of Computer Science, 2019)
In its effort to modernize its public service delivery, Malaysia is actively leveraging on big data analytics. To support this ambitious initiative, a new framework addressing the needs of Big Data Quality for Malaysia is imperative, as big data analytics requires high quality data in order for it to be useful. Unfortunately, a proper big data quality framework, particularly one which focuses on the specific context and needs of Malaysia’s Public Sector Open Data initiative is missing. This paper thus focuses on the proposed development of the Big Data Quality Framework for Malaysia’s Public Sector Open Data Initiative (MyPS-ODI). Using Systematic Literature Review (SLR) approach, we conceptualize and propose a framework for big data quality that can contribute in enabling the sharing of quality data widely, so as to increase the transparency of the Malaysian government's services, and provide people and the business community the opportunity to increase creativity and innovation in developing new products and services through high quality data. The proposed framework will benefit IS managers in government sectors and help them better understand and meet their consumers’ data quality needs, as well as help them to facilitate big data analytics readiness of the public sector in the country. It will also assist in providing a high-quality platform to the citizens to get quality information from official government sources. Finally, the framework will help in saving the time and effort needed in correcting the results of data analysis due to poor data quality, by providing quality data from the data preparation stage.