Fintech Applications - Big Data 
Every day, analytical technologies analyze vast volumes of personal data to determine client behavior, which is then included into the decision-making process. Fintech apps, automated/algorithmic trading, automated advising and robo-advisers, mobile banking, decentralised finance (DeFi), and crypto assets are a few examples of the various forms of intelligent automation applications. 

Examining Big Datasets
Why do social media businesses offer services that are 'free'? They aren't truly free, is the explanation. Large volumes of personal data, including information about our likes and dislikes, where we travel and how we get there, and our buying patterns, are how we pay for them.

Financial analysts now evaluate more than just market indications, financial accounts, and economic statistics. Additionally, they go in-depth with the narratives presented by non-traditional data from non-traditional sources. These sources generate massive amounts of data that need to be incorporated into the decision-making process. Fintech assists with this process by creating analytical tools.

Big  data 
Big data, also referred to as alternative data, includes information produced by individuals, corporations, governments, financial markets, sensors, and the "Internet of Things." 

Over the past ten years, there has been an explosion in big data, particularly in the form of unstructured data from online news sites, social media, email and text traffic, and other electronic sources. Big data is being used by investment professionals more and more in their decision-making as they combine unstructured and structured data to enhance their financial models and projections.

Big data has various features that set it apart from ordinary data. Volume, variety, velocity, and veracity are the four main areas of distinction, or the four Vs.