The ever-increasing complexity of technology and the flood of new data are changing how businesses function and compete. Daily data production rates of 2.5 quintillion bytes indicate that 90 percent of the world's data has been generated in the last few years. Big data refers to the massive amounts of data being generated and stored, which opens up new possibilities for data mining and analysis.

The Workings of Big Data

Organizations utilize data and analytics to get useful information to influence better business choices, and they do it by following the 4 V's of big data. A few examples of sectors that have embraced the use of big data include the financial services industry, the technology sector, the marketing sector, and the healthcare sector. The use of large amounts of data continues to reshape several businesses' competitive landscape. According to estimates, around 84 percent of businesses feel that companies that do not have an analytics strategy face the danger of losing their competitive advantage in the market.

In particular, the financial services industry has increasingly utilized big data analytics to make improved investment choices that result in more consistent returns. In connection with big data, algorithmic trading maximizes portfolio returns by using huge amounts of historical data in conjunction with complicated mathematical models. The landscape of financial services will unavoidably change as big data technology becomes more prevalent. The capacity of big data to collect an ever-increasing amount of data continues to provide several important obstacles, even though it seems to have obvious advantages.

4 V’s of Big Data

Big data relies heavily on the following four characteristics: volume, veracity, variety, and velocity. Financial institutions are looking for innovative methods to harness technology to achieve efficiency as they face rising competition, legal restraints, and the demands of customers. Companies may obtain a competitive edge by using some features of big data; however, this depends on the industry they operate in.

The velocity is the pace at which data must be stored and evaluated. Every every day, the New York Stock Exchange collects one terabyte's worth of data. By 2016, it was predicted that there would be 18.9 billion network connections, which works out to around 2.5 connections for every person on the planet. Processing transactions promptly and effectively may provide financial institutions with a competitive advantage over other institutions in their industry.

Big data may be characterized as unstructured or structured data. The phrase "unstructured data" refers to information that is not ordered and does not fit into a model established beforehand. This includes data collected from sources on social media, which assists organizations in gathering information about the requirements of their customers. The information currently handled by the company in relational databases and spreadsheets is called structured data. Consequently, the different kinds of data need to be actively handled to facilitate the formation of more sound business choices.

Financial institutions face a significant obstacle in the form of a rising burden posed by the amount of market data. In addition to actively managing huge amounts of historical data, the banks and capital markets industry must also handle ticker data. Similarly, investment banks and asset management companies employ vast data to arrive at sensible investment conclusions. Insurance and retirement companies may obtain information on historical policies and claims for active risk management.

Algorithmic Trading

Because of the ever-increasing capabilities of computers, the term "big data" has become almost synonymous with "algorithmic trading." The practice of automating financial trades allows computer programs to perform transactions at speeds and frequencies that are impossible for a human trader to achieve. Within the framework of the mathematical models, algorithmic trading allows for the execution of deals at the best available prices, as well as the timely placement of trades, and it decreases the number of human mistakes caused by behavioral variables.

As a result of institutions being able to more effectively limit algorithms to include enormous quantities of data, as well as use large volumes of historical data to backtest methods, investments may be made that are less hazardous. Users can determine which data are of high value and which are of low value as a result. Because algorithms may be developed using structured and unstructured data, it is possible to improve trading choices by combining real-time news, data from social media platforms, and stock market information into a single algorithmic engine.