Analytics: The Real-World Use of Big Data in Financial Services

Financial and banking companies and markets globally, have discovered the significance of "Big data". Big data is no longer a tool for technology alone but the financial and global markets leverage on it for the transformation of their organizations and processes. A research was conducted through the partnership of the Said Business School and the IBM Institute for Business Value. The study found out that 71 percent of the financial and banking firms agree that creating a competitive advantage is achieved by the use of big data. The social sites further stretches the growth of big data information thus reducing the influence of the banking sector on its control (Cheng et al., 2016).

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The study also found out the following information concerning big data; one is that big data customers analytics drives the initiatives of big data, the second finding is that the scalability and extensibility of big data is depended upon by big data, the research also indicates that the main focus of big data is on gaining insights from new and existing internal data sources, also, strong capabilities and analytics are needed in dealing with big data. The study thus provides the following recommendations for the cultivation of big data adoption; the customer-centric outcomes need initial investment of efforts, the strategy of big data should be defined with a blueprint that is business-centric, analytics capabilities should be built on the basis of business priorities, and that companies should begin with existing data for the achievement of results that are near-term.

One of the main challenges companies face in their implementation of big data activities include the inadequacy of technologies and infrastructure for big data implementation. The process of integrating the big data information often requires new components of infrastructure like NoSQL, Hadoop analytical appliances, among others. However, the banking and financial sector lags behind when it comes to compliance based on these technologies. Another main challenge for big data implementation is the inability for the connection of data across departmental and organizational silos. This has been a challenge for business intelligence for many years. Acquisition and mergers have created costly and countless data silos. Also, the fact that the banking sector is often characterized with massive transactions on a daily basis. All these transactions adds to the pool of data rows every day. Therefore, the management of these data for connecting them across departmental and organizational silos have been time consuming, costly and labor intensive.

The processes of formal project closure include:

Arrangement of a post mortem

The management of a project will need learning critical lessons as the project wraps up. It will then be important to gather all the information and feedbacks about the project. They will later be used in providing more insights and information.

Completion of paperwork

For a good and accountable ending, documents will need to get signed off with stakeholders' approvals. Therefore, close attention and thoroughness is needed for this process, even as contracts are being closed along with the vendors and resources involved.

Release of resources

After the project team complete its tasks, it must be released. This is normally a formal and crucial process. That will be important for the commencement of a new project.

Archive documents

The project ends with a lot of information gathered and lessons learned. Therefore, all the information, documents and lessons must be kept well for future use.

Reference

Chen, S. E., Yang, L., & Xu, S. (2016). Analytics: The real-world use of big data in financial services studying with judge system events. Journal of Shanghai Jiaotong University (Science), 21(2), 210-214.