In the final chapter, this research will conclude the case study, research findings, and potential academic contribution in two sections. The first section will be the summary of this research with definite and concrete lists of findings. The final section will be the research limitation and recommendation for future research.
6.1 Research Summary
At the beginning, this research target financial institutions to conduct further research regarding Big Data implementation. A few reasons are that financial institutions have
tremendous of structure and semi-structure data. In addition, the book Bank 3.0 by Brett King indicates that customer behavior is rapidly changing due to psychology of self-actualization and technology innovation. Bank can either try to reinforce traditional mechanisms and behavior, or they should participate in changing behavior and build accordingly. Big Data is changing not only our behaviors, but also our traditional services such as finances, supply chains and retails.
Therefore, bank implementing Big Data is inevitable choice.
After the case study and interview, this research is designed to discover the
implementation processes and critical factors of the implementation. This research first defined and introduced each case study. Then, the interview details are organized and explained
throughout Chapter 4. After the compared and contrasted discussion on Chapter 5, this research identified three phases of implementation and several essential factors for financial institutions implementing the Big Data. The phases and factors are organized below:
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Defining Phase: Companies will first define their own interpretation of Big Data in order to plan and coordinated their implementation.
Brainstorming Phase: Companies averagely spent most of the time in this phase. The
implementation team leads must brainstorm to find the best way to enforce and carry out the Big Data project by searching, organizing and surveying internal and externally.
Implementation Phase: Companies follow their previous made proposal steps by steps.
Although this research categorized and concluded the Big Data implementation process in three phases, the implementation process still varies for each financial institution. However, the essential critical factors during the implementation are relatively similar across each financial institution. This research conclude the most important and fundamental factors below.
1. An implementation team regardless the size to carry out the Big Data project 2. Top management’s commitment on implementation
3. Timing on the implementation 4. Big Data system selection 5. Clear goals and objectives
6. Internal and external employees training 7. Internal and external support
6.2 Research Limitation and Recommendation
There are a few limitations in this research. First, financial institutions are strictly
conservative compared to other industry. Moreover, they were generally extreme confidential on internal details such as current business strategy and institution direction. Therefore, this research only includes two financial institutions case study. Due to the confidentiality, interview questions
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are designed to collect information regarding Big Data implementation from more general perspective. Therefore, lack of depth and thorough details are disadvantages in this research. In addition, difficult in searching for more financial institutions to interview in order to justify or validate the information are also another limitation in this research.
In summary, Big Data can be applied and conducted in unlimited approaches. There are indefinite quantitative and statistic researches regarding Big Data methods and proposition. Most of qualitative researches are focused on the methods and technical components of the Big Data regarding data analytics and data processing. Although methods and approaches are important in data analytics, the business strategy after the data analytics should be influential and crucial as well. This research aims to focus on the how and why financial institutions implementing the Big Data. Moreover, the crucial factors are also discussed in this research. However, the business strategy or data analytics after the implementation are not been discussed. Therefore, future qualitative research regarding business strategy and accuracy of data-driven decisions after the data analytics is recommended.
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