Data Virtualization for Analytics and Business Intelligence in Big Data


Manoj Muniswamaiah, Tilak Agerwala and Charles Tappert, Pace University, USA


Data analytics and Business Intelligence (BI) is essential for strategic and operational decision making in an organization. Data analytics emphasizes on algorithms to control the relationship between data offering insights. The major difference between BI and analytics is that analytics has predictive competence whereas Business Intelligence helps in informed decision-making built on the analysis of past data. Business Intelligence solutions are among the most valued data management tools available. Business Intelligence solutions gather and examine current, actionable data with the determination of providing insights into refining business operations. Data needs to be integrated from disparate sources in order to derive insights. Traditionally organizations employ data warehouses and ETL process to obtain integrated data. Recently Data virtualization has been used to speed up the data integration process. Data virtualization and ETL are often complementary technologies performing complex, multi-pass data transformation and cleansing operations, and bulk loading the data into a target data store. In this paper we provide an overview of Data virtualization technique used for Data analytics and BI.


Data Analytics, Business Intelligence, Big data, Data Virtualization, ETL and Data Integration

Full Text  Volume 9, Number 9