Since the crash of 2008, the importance of big data in finance has surged. The demand for big data has increased to a staggering proportion. This caused many industry experts to examine the connections between regulatory changes in finance and the need for the kind of solutions offered by Hadoop.
Business sectors are heavily impacted by big data. This include finance, healthcare, marketing and technology. Decisions are made based on both the quality as well as the quantity of information that is available at the time. Organizing information is an analytical process, and the difficulty of performing this function increases with volume. This particular problem leads developers to come up with a solution to address this specific issue.
Financial companies are required to reserve an amount of capital that is proportionate to the exposure within the industry at a given point in time. However, this amount will change with the surrounding conditions; the amount of capital reserves required could increase or decrease over time.
Required data must be reported, recorded and analyzed; however, this task is far too immense for any individual or team. Hadoop developers tackled this specific problem by creating features that simultaneously manage large amounts of data. This includes storage and sharing, but it also enables analysis of large data streams, which can be a life-line to a company mired in disorganized information.
The insurance and banking industries require the highest levels of data management. The sheer volume of data to manage, analyze and store can be resource-intensive. In addition, changes in the industry also require updating and management of the changes. The world of finance depends on accurate information in order to keep pace with all of the requirements imposed by regulators and third parties.
Volume: Around 2.5 quintillion bytes of data are generated every single day. This volume is a relatively recent phenomenon; around 90 percent of the data in the entire world was created in only a matter of years.
Variety: The type of data collected must be stored and analyzed in order to have any kind of meaning or application for businesses.
Velocity: This relates to the speed that is required of the hardware to store and analyze the date. This is a critical element for financial institutions involved in trading, for example.
Veracity: The content of the data must be analyzed in order to be applied. The veracity refers to the accuracy of the information, which requires complex processes to sort factual data considering the sheer volume of information involved.
Hadoop developers understood the need for a solution of managing big data in the financial sector. The data sets are often complex, and they include both structured and unstructured forms of data. The application of big data has a potential transformational effect on internal processes in entire sectors and industries.
The big data application of Hadoop financial analysis is important to understand. The context of risk management and detecting fraud is particularly relevant. This is one of the central challenges for business, yet until recently, there were few viable solutions. The vast quantity of data and need for accuracy means that big data is essential, and yet there are few products that can manage the volume.
Hadoop developers recognized the need of organizations to leverage big data to gain advantages over competitors. The adaptation of this technology in banking and financial sectors indicates the value of the Hadoop solution for big data. Industries that fail to adopt this solution run the risk of losing market share in significant proportions.
Big data in finance can also be applied in the customer service area of the enterprise. In a survey conducted by Oracle, almost 85 percent of the survey participates affirmed that individual, customized experiences are highly desired by customers.
Revenue streams are directly impacted by customer satisfaction, so the motivation to provide this tailored experience certainly exists. However, the problem has always been how to accomplish this daunting task. Hadoop offers solutions for leveraging big data to improve the customer’s experience. Prior customer interactions and other activity-based clues can help to craft solutions that improve customer service and prevent account closures.
Regulations also impose requirements on industries, which stipulates that records must be maintained. This creates an immense need for solutions to the data storage requirements. The problem of analyzing and applying the information is also present, but this can’t be done without solving the storage issue.
Maintaining compliance is a huge issue for businesses of all sizes, but financial institutions have additional requirements imposed after the financial crash of 2008 and subsequent legislation. Trading deals must be recorded, monitored and documented in great detail in order to satisfy compliance requirements.
Forbes did a survey that came to a shocking conclusion that over 87 percent of company representatives believe that failing to have a strategy for big data will cause their businesses to lose out to competitors. Data-driven industries are under immense pressure to create solutions for issues related to big data. Hadoop has powerful features that enable big data application in competitive industries. The Hadoop financial analysis system allows banks and financial institutions to process, analyze, store, document and track with accuracy.
Chetu was founded in the year 2000 with the vision of providing custom solutions to small businesses, Fortune 5000 companies and start-up enterprises all over the world. The Hadoop development services improve the business’s ability to understand and apply big data. This increases the flexibility of the organization, cuts operating costs and reduces redundancies. Contact us to find out how Chetu’s Hadoop development services can improve your company's bottom line.
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