3 v’s of big data
volume, variety, and velocity. Facing increasing competition, regulatory constraints and customer needs, financial institutions are seeking new ways to leverage technology to gain efficiency. Depending on the industry, companies can use certain aspects of big data to gain a competitive advantage.
=> Big data in past years has come from the financial sector. This should come as no surprise to most, as there are large amounts of money to be made in finance. World markets run on data, with investors, funds, and governments all trying to use the information at hand to value investment and risk. Interpreting the data correctly can lead to billions of dollars in instant profits, while incorrect interpretations can ruin entire companies
How big data has transformed the industry, and where is it going?
A good example of this is the housing crisis of 2008. Many investment banks and firms did not have an accurate picture of the risk they were taking on, and this hole in the data led to some of the largest bankruptcies in US history. Another breakthrough is in machine learning most financial experts use what is called technical analysis to detect patterns and trends in the financial sector. If you haven't heard of technical analysis, the concepts, at least, are worth learning.
How Big Data has also Changed Finance
Industries that have adopted the use of big data include financial services, technology, marketing and health care, to name a few. The adoption of big data continues to redefine the competitive landscape of industries. An estimated 89 percent of enterprises believe those without an analytics strategy run the risk of losing a competitive edge in the market. Financial services have widely adopted big data analytics to inform better investment decisions with consistent returns
Challenges faced by industries
Despite the financial services industry increasing embrace of big data, significant challenges still exist in the field. Most importantly, the collection of various unstructured data supports concerns over privacy. Personal information can be gathered about an individual’s decision making through social media, emails and health records.
Conclusion
Big data continues to transform the landscape of various industries, particularly financial services. Many financial institutions are adopting big data analytics in order to maintain a competitive edge. Through structured and unstructured data, complex algorithms can execute trades using a number of data sources. Human emotion and bias can be minimized through automation; however, trading with big data analysis has its own specific set of challenges The statistical results produced so far have not been fully embraced due to the field’s relative novelty. However, as financial services trend towards big data and automation, the sophistication of statistical techniques will increase accuracy.
