Paper on Data Driven Decision Making Using Real World Data
MRN4LF
DataDrivenDecisionMakingRevision4.doc2
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Data Driven Decision Making
Bryan West
WGU
Table of Contents
4 3. Graphical Representation of Data Analysis
6 4. Effect of unethical dealings in financial institutions
6 5. The technique used for financial institution data analysis
1. Summary
For many years, many financial institutions have been criticized and had been linked with a contrary understanding and the impression. There are those institutions who claim they inspire greediness and encourages pleasure and thereby causes anxiety for their customers as they utilize their products and services.
The basis of analyzing financial data is advantageous to all financial intuitions as it will bind the baking organizations’ information so that it can ease the identification of better business environment and chances for expanding the business structure.
The idea of implementing data analysis in banks has been in the business discussion forums, and some researches have done about the most appropriate ways of how to gather all the information that revolves around the business organizations and how useful information can be retrieved from these data.
2. Data Collected Report
From all spheres of industries, data is the most crucial component that can reflect the image of the company and it can also change and determines how the business organization can function. This data in the form that can be read and interpreted by the machine as well as the human-readable information.
Figure 1 Process of analyzing in financial institutions
The process of collecting data is dependent mostly on how that information will be used. Financial institution data collected for market analysis would need to undergo a comprehensive procedure that entails a calculated searching technique by the analysts.
There are two types of data that are very important to the organizations. The primary data which is collected directly by those carrying out analysis and research and they are usually significant in addressing the issues that the organization is facing in the present. Secondary data, on the other hand, refers to data that has been collected and is readily available for use by those carrying out the analysis. Secondary information is beneficial especially in cases where the primary data are not available (Barth, & Levine, 2016).
3. Graphical Representation of Data Analysis
Sometimes the organizational data can be devastating, businesses are often challenged by the tremendous amount of information and therefore having some means to summarize this data would be more sensible even for the organization especially during decision-making process or in the analysis.
Financial institution data can be represented in the histogram, bar chart etc. the graph representation of the financial institution data is usually used when the organization wants to illustrate this information for analysis and to assist in predicting the business growth, competition, and expansion where necessary.
Figure 2 Capex Finance
Figure 3 Data Analysis in Financial Institutions
4. Effect of unethical dealings in financial institutions
The perception of the dishonest dealings in financial institutions has a different impact on its consumers and their customers such as providing inaccurate information and deceptive illustrations of productions and services, and it will also fail to recognize exactly what the client needs hence a greater disappointment to the client on giving the proper recommendation. Further to this, there is also an absence of necessary skills and information.
5. The technique used for financial institution data analysis
A study that was done in Toronto, Canada in 2013 shows that the financial institution data is one of the top three crucial issues that matter most in every organization. Various techniques can be used to analyze data as stated below;
4.1 Classification tree analysis
In this technique, the numerical data are identified, and the findings are grouped depending or subject to the observation made. The team carrying out the view would also need to do specific training mainly using the past results while comparing to the present representation.
4.2 Genetic algorithm
Genetic algorithm technique is usually motivated by the solution and the development of analysis progression. It typically employs the natural methods as well as the inheritance technique. Such plans are handy when developing very valuable resolutions to the hitches that would need more improvements to be made.
4.3 Regression Analysis
Basically, the regression analysis technique comprises the aspects of deploying some of the self-determining variances to examine how it affects those variables especially on the issues such as the duration that was taken during the whole process. The technique is more suitable when applied to situations such as a progressive quantitative technique like that of speed and weight.
4.4 Social Network Analysis
This technique was implemented in telecommunication production and almost immediately was also performed by the sociology to learn interactive relationships. The method is currently practical especially in examining the associations among personnel in different fields as well as those of money-making firms (Lone, 2016).
6. Conclusion
There are numerous and various ways of presenting defining and presenting financial institution data during the analysis. Tools such as frequency data tables, histogram, and bar charts are some of the convenient and valuable tools for necessary in presenting a summarized data. The data the represented on a frequency table depicts the existence of precise data including the particular data at a specified interval typically.
Additionally, the other means to observe the data can be concluded by the use of the percentages. The percentages depict the amount that is recorded and analyzed at a particular given time in the financial institution data score set.
7. References
Barth, J. R., & Levine, R. (2016). Regulation and governance of financial institutions. Cheltenham, UK: Edward Elgar.
Global financial development report 2017/2018: Bankers without borders. (2018). Washington, DC: World Bank Group.
In Cavanillas, J. M., In Curry, E., & In Wahlster, W. (2016). New horizons for a data-driven economy: A roadmap for usage and exploitation of big data in Europe. Switzerland: SpringerOpen.
International Halal Conference, & In Nurhidayah, M. H. (2018). Proceedings of the 3rd International Halal Conference (INHAC 2016).
Lone, F. A. (2016). Islamic Banks and Financial Institutions: A Study of their Objectives and Achievements. (Springer eBooks 2016 [recurso electrónico].)
