The availability of big data, retrieved from various data sources, with different data formats and sizes, can provide a more complete picture of a borrower.
Indeed, big data comes from various sources [ 23, 36].
Thus, how to use big data analytics that combine large amount of data from various data resources to quantify credit information is a challenge that needs to be addressed.
In order to ensure big data privacy, various mechanisms have been developed in recent years.
Big data has various unique characteristics, including volume, velocity, variety, and veracity.
The recent explosion of big data in various fields of science, including biology, has led to high demand for integration of big data analysis with computational and quantitative thinking into the skill set required of graduates.
Section "How to choose a platform for big data analytics?" discusses various details about choosing the right platform for a particular big data application.
We hear the term Big Data used in various contexts; when applied to health information it will likely be the smart integration of massive data sets from the 'Internet of things' with the small data about your activity, mood, and other information.
Natural resources data from various monitoring sources are becoming bigger, faster, and diverse than ever before.
With the constant deepening of Internet finance, Citibank established a big data analysis platform for its retail business by integrating data from various internal sectors and social data from external social media and e-commerce enterprises.
