Big data have become an important asset due to its immense power hidden in analytics. Every organization is inundated with colossal amount of data generated with high speed, requiring high-performance resources for storage and processing, special skills and technologies to get value out of it. Sources of big data may be either internal or external to organization, and big data may reside in structured, semi-structured or unstructured form. Artificial intelligence, Internet of Things, and social media are contributing to the growth of big data. Analytics is the use of statistics, maths, and machine learning to derive meaningful insights from data to make timely decisions and enable data-driven organization of the future. This paper sheds light upon big data, taxonomy of data, and hierarchical journey of data from its original form to the high level understanding in terms of wisdom. The paper also focuses on key characteristics of big data and challenges of handling big data. In addition, big data storage systems have also been briefly covered to get the idea on how storage systems help to accommodate the requirements of big data. This paper scrupulously articulates the eras of evolution of analytics varying from descriptive, predictive and prescriptive analytics. Process models used for inferring information from data have been compared and their applicability for analyzing big data has also been sought. Finally, recent developments carried in the domain of big data and analytics are compared based on the state-of-the-art approaches.
