The need for data entry services has grown worldwide with increasing demand for digitization of data. Business organizations have huge volumes of data and they need to use efficient methods to turn their data into usable information. Data describes the facts and figures that a company processes everyday and becomes information after it has been processed. It shows the trends and patterns existing in the market and enables to analyze the data and make better decisions.
As every area of research becomes data-intensive, emphasis is shifting from data generation to data analysis and this has led to the development of big data. Big data refers to the sets of large or complex data that uses predictive analytics and user behaviour analytics to extract value from data. With the emergence of Big Data, data cleansing services have also grown in demand in the business scenario.
Uses of Big Data in the Financial Industry
- Big data techniques are focused on analyzing manageable amount of data that represents the entire data set. Working with huge volumes of data can be difficult and processing it is time consuming. But Big Data is useful in that it helps researchers identify those aspects of data that are most significant and allows them to discard some data safely without compromising accuracy. Big data finance techniques are advantageous to researchers also in that they help reduce the amounts of data required to manipulate.
- Big data can be used to identify the most important factors that influence a set of behaviours. For example, financial organizations can use big data to evaluate the main drivers of stock price behaviour common to all stocks. In the financial industry, due to the advanced algorithm linkages among various financial instruments of all asset categories and ETFs (Exchange Traded Funds), a minor disturbance in one stock will affect the entire market and each individual stock. But Big Data helps to analyze the developments and figure out the valuable from the worthless.
- Big data helps in finding natural affinity groups of similar stocks and potential substitutes. Similar stocks are one of the key drivers of trading strategies such as statistical arbitrage. With Big Data Finance, statistical arbitrage can be used more effectively, enabling simple arithmetic to identify the stocks to be tracked and arbitraged. Finding stocks that are potential substitutes is also important for portfolio managers and execution traders. It is important for them to identify a liquid substitute to carry out a quick trade in low-liquidity conditions, and rebalance the portfolio to its target composition eventually.
- Big Data helps in recognition of patterns of trading strategies.
- Big Data Finance analytics also helps in reconstruction of lost data and identifying errors in algorithms.
Organizations in the financial sector need to leverage their information assets and use the Big Data available to obtain a detailed understanding of markets, competitors, customers, channels, products, suppliers, employees, regulations and other relevant factors. They can realize value by efficiently managing and analyzing the increasing volume of new and existing data, in which outsourced solutions can be effectively used. Successful firms in the finance sector have efficiently incorporated data from within and outside their organizations and produced impressive results.
