What is big data?
The proliferation of data and increasingly complex technologies continue to change the way the industry operates and competes.In the past few years, 90% of the world’s data was created by 2.5 terabytes of data Take one day as a cycle. Often referred to as big data, this rapid growth and storage creates opportunities for the collection, processing and analysis of structured and unstructured data.
How big data works
Following the 4 Vs of Big Data, organizations use data and analysis to gain valuable insights to provide information for better business decisions. Industries that use big data include financial services, technology, marketing, and healthcare. The adoption of big data continues to redefine the competitive landscape of the industry.One estimated 84% of companies believe that companies without analytical strategies will face the risk of losing their competitive advantage in the market.
Especially in the financial services industry, big data analysis has been widely adopted to provide consistent returns for better investment decisions. Combined with big data, algorithmic trading uses large amounts of historical data and complex mathematical models to maximize portfolio returns. The continued application of big data will inevitably change the pattern of financial services. However, in addition to its obvious benefits, big data’s ability to capture ever-increasing amounts of data still presents major challenges.
Big data 4 V
4 V is the foundation of big data: Quantity, variety, authenticity and speed. Faced with increasingly fierce competition, regulatory restrictions and customer demand, financial institutions are seeking new ways to use technology to improve efficiency. Depending on the industry, companies can use certain aspects of big data to gain a competitive advantage.
Speed is the speed at which data must be stored and analyzed. The New York Stock Exchange captures 1 terabyte of information every day. By 2016, there are an estimated 18.9 billion network connections, and there are approximately 2.5 connections for every person on the planet.Financial institutions can focus on processing transactions efficiently and quickly to stand out from the competition.
Big data can be divided into unstructured data or structured data. Unstructured data is information that is unorganized and does not belong to a predetermined model. This includes data collected from social media sources that can help organizations gather information about customer needs. Structured data consists of information that the organization has managed in relational databases and spreadsheets. Therefore, various forms of data must be actively managed to provide information for better business decisions.
The ever-increasing amount of market data has brought huge challenges to financial institutions. In addition to a large amount of historical data, banks and capital markets also need to actively manage stock data. Similarly, investment banks and asset management companies use large amounts of data to make sound investment decisions. Insurance companies and retirement companies can access past policies and claims information for proactive risk management.
As the power of computers continues to increase, algorithmic trading has become synonymous with big data. The automated process enables computer programs to execute financial transactions at a speed and frequency that human traders cannot. In the mathematical model, algorithmic trading provides transactions executed at the best price and timely transaction placement, and reduces manual errors due to behavioral factors.
Institutions can more effectively reduce algorithms to integrate large amounts of data, and use large amounts of historical data to backtest strategies, thereby reducing investment risks. This helps users identify useful data to keep and low-value data to discard. Given that structured and unstructured data can be used to create algorithms, integrating real-time news, social media, and stock data into an algorithmic engine can produce better trading decisions. Unlike decision-making that may be affected by different sources of information, human emotions, and prejudice, algorithmic trading is only performed based on financial models and data.
Robo-advisors use investment algorithms and large amounts of data on digital platforms. Investments are constructed through modern portfolio theory, which generally supports long-term investments to maintain consistent returns and requires minimal interaction with human financial advisors.
Although the financial services industry is increasingly accepting big data challenge Still exists in the field. Most importantly, the collection of various unstructured data supports privacy concerns. Personal information about individual decisions can be collected through social media, email, and health records.
Especially in the field of financial services, most of the criticism is focused on data analysis. The huge amount of data requires more complex statistical techniques to obtain accurate results. In particular, critics have overestimated the signal-to-noise ratio as a model of false correlation, which represents a statistically robust result purely by accident. Similarly, due to trends in historical data, algorithms based on economic theory usually point to long-term investment opportunities. Effectively generating results that support short-term investment strategies is an inherent challenge in predictive models.
Big data continues to change the pattern of all walks of life, especially the financial services industry. Many financial institutions are using big data analysis to maintain a competitive advantage. With structured and unstructured data, complex algorithms can use multiple data sources to execute transactions. Human emotions and prejudices can be minimized through automation; however, big data analysis transactions have their own specific challenges. Due to the relative novelty of the field, the statistical results produced so far have not been fully accepted.However, as a financial service trend For big data and automation, the complexity of statistical techniques will increase accuracy.