Big Data

Big data may be viewed as databases that are too large to be adequately handled by current spread sheet technologies.
Big Data is the umbrella term used to describe the phenomenon and industry revolving around rich abundance of data in the modern world. It’s tightly related with web 2 and emergence of software-as-a-services used by massive amounts of people, whose corporations accumulate large quantities of data they can use for a verity of purposes. The prime example of big data is Google that scabs and indexes the whole Internet, models its users’ preferences and interests based on the content they consume. Aside from Google, other big data giants are Facebook, Instagram, Twitter. [1, pg. 22-23]
This phenomenon led to the appearance of the profession of data scientist (person who researches and finds insights in data, often using automated tools) and data engineer (person who classifies and regulates data into meaningful parts). Both of them pioneered a new branch of computer science that revolves around the artificial intelligence design and implementation known as machine learning. Primarily, big data is used to fit various machine learning models with engines such as TensorFlow or PyTorch to aid with services of artificial intelligence. [1, 99-134]

Big data and its analytics (BDA) promise to contribute to improved public policy through more information availability concerning those who are affected by public polies than could have been imagined in the past.

Sources:

Laurie A Schintler, Zhenhua Chen Big Data for Regional Science, - 2017 – 350

M.-D. Babak (2019). Data-Driven Models & Mathematical Finance: Apposition or Opposition? London: University of Oxford.

Kalil, Tom. (2012). Big Data is a Big Deal. Retrieved from: https://obamawhitehouse.archives.gov/blog/2012/03/29/big-data-big-deal