Chapter 4: Research Results
4.3 Cross-case Analysis - Key Factors that Enable Big Data to Create Business
4.3 Cross-case Analysis - Key Factors that Enable Big Data to Create Business Value
In chapter two, this study summarized five types of possible key factors that enable Big Data to create business value for companies. In this section, this study analyzed and compared the findings across the four cases, and validated that whether the five key factors were important in the Big Data application process of financial industry. The findings of the cross-case analysis are as below.
4.3.1 Accessibility, Timeliness, and Quality of Data
All Interviewees Agreed with the Importance of Data Accessibility
All interviewees agreed that data accessibility significantly affects the value-creation chain of Big Data. The chief information officer of company C noted that companies should collect data in legal, reasonable, and reliable ways, besides, it is still challenging to integrate cross-subsidiary data. The interviewees of company B and company D both mentioned that the collection of internal data, such as transaction data, is not a challenge for them. However, how to manage the internal data from various departments and different subsidiaries is a critical issue. They also mentioned that although there is no obstacle for obtaining open-data and purchasing data from external data provider, but sometimes, collecting external data might be challenging. For example, recently company D cooperated with a third-party payment platform, but they still could not obtain the complete transaction data from the platform. The senior IT manager of company B also indicated that collecting web crawler data was not as easy as collecting data from external data provider.
Most Interviewees Agreed with the Importance of Data Timeliness
In the process of generating business value from Big Data, most interviewees agreed
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the importance of accessibility, timeliness, and quality of data. However, the only part of the disagreement is that the interviewees of company B indicated that timeliness of data was not always important because the problem needed to be solved by Big Data was not always immediacy, and the technological breakthrough made timeliness of data no longer a big challenge. Although the senior IT manager of company A regarded the timeliness of data as an important key factor, he proposed similar insights with the interviewees of company B. He thought that the importance of data timeliness is associated with the purpose of applying Big Data technology. Due to the technological breakthrough, the company could get the analysis results in at most one day, which met the needs of most application. However, the data timeliness issue may become a critical issue in more real-time online services.
All Interviewees Agreed with the Importance of Data Quality
The interviewees of company D noted that data quality is more important than the data volume because data quality significantly affected the quality and accuracy of the results of Big Data analysis. The interviewee of company A indicated that even though cleaning data may take a lot of time, data cleaning is still important because it affects the data quality. However, the chief information officer of company C mentioned that quality of raw data is the most important issue, because “garbage in, garbage out”, no matter how hard they cleaned the data, the analysis result may still be invalid if the quality of raw data is terrible.
In conclusion, this study found out it is undoubtedly that the accessibility, timeliness, and quality are important in the value-creation chain of Big Data in financial industry.
4.3.2 Data Policy
In addition to the interviewee of company A, all other interviewees regarded data policy as an important key factor that affected the Big Data value-creation chain.
The senior IT manager of company A said that although data policy was not that important than other key factors, but the restriction of related regulations was still a challenge for data usage. For instance, while collecting external data, companies should find a legal way to obtain customers’ data, such as social media data.
Premise of Using Data
The chief technology officer of company B and the chief information officer of company C both regarded data policy issues, such as data privacy, data security, and moral issues, as a premise of using data. It is the responsibility for companies to protect customers’ data.
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Necessary to Anonymize and De-identify Sensitive Data
The interviewees of company C and company D both mentioned that although the analysis results may be less accurate, it is still necessary to anonymize and de-identify customers’ sensitive data while implementing Big Data technologies.
Cloud-computing Brought More Security Concerns
Besides, the chief technology officer of company D noted that the usage of cloud-computing technologies and cloud storage services brought more security concerns in Big Data application.
GDPR Brought a Big Challenge
Many interviewees mentioned that the General Data Protection Regulation (GDPR) of EU brought a big challenge for data usage. Due to the strict restrictions, companies must spend more cost to obtain the agreement and authorization to use customers’ data, and the whole data usage method and data collecting method for companies may need to be changed. If such strict regulations are adopted internationally, it will be a great impact on the application of Big Data analysis around the world.
In conclusion, this research found out that most interviewees took data policy as an important key factor that affects the value-creation chain of Big Data.
4.3.3 Staff Capacity and External Support
All interviewees agreed that tools and technology is an important key factor. This study found out that while implementing Big Data technology, company A and company C preferred to train their internal talents; company B took external supports as their main source of related skills; and company D regarded both internal talents and external supports as important human resources.
Hard to Find Cross-disciplinary Talents
The senior IT manager of company A indicated that except for statistic knowledge, information technology knowledge, and business knowledge, it is also important for staffs to have the problem-solving ability. The company preferred to train their own internal talents, and they had successfully trained many excellent talents. Besides, company C was also committed to training their own Big Data talents, and involved many resources to study new techniques and new technologies.
The senior IT manager of company D noted that the skill of modelling is very important, but it is hard to find cross-disciplinary talents who are simultaneously skilled in mathematical statistics, information technology, and business knowledge.
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Hard to Train Big Data Talents
The chief technology officer of company D said that it is hard to train Big Data talents due to the rapid improvement and changes in technologies, and the technical support and consultancy service of external experts can also be regard as an important human resource.
External Support is Important
On the other hand, the chief technology officer of company B indicated that external support was important at the beginning of Big Data implementation, which helped the company to check whether there was any problems in the initial stage. In addition, the senior IT manager of company B mentioned that talents skilled in new techniques were lacking, so they obtained many external supports that were experienced and professional in implementing Big Data technology. With the help of external experts, the company were able to apply Big Data technology in a short time. However, he also noted that training internal talents must be an important issue in the long-term.
The senior IT manager of company D also indicated that due to the scarcity of Big Data talents, the support of external experts was still important now, even though it was difficult to find a good external supporting company. However, the supply and the demand of Big Data talents may be balanced in the future.
To summarize, this research found out that staff capacity and external support are important for financial companies to create business value from Big Data.
4.3.4 Tools and Technology
All interviewees agreed that tools and technology is an important key factor that enables Big Data to generate business value.
The Enabler of Data Usage
Some interviewees noted that the technological breakthroughs, such as the operational capability of computer and Big Data techniques, enables the analysis of semi-structured data and unstructured data, and also broadened the usage of data and increased the importance of data.
Setting up the Development Environment and Standard Process is Important The chief technology officer of company B thought that comparing to other more important factors, software and hardware technologies are not the most important issues in Big Data value-creation chain, but setting up the development environment and standard process is important in the early days of implementing Big Data.
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Integration of Big Data and AI is Now a Big Trend in Financial Industry
Moreover, this study found out that all companies in our case studies integrated Big Data technologies with machine learning technologies and artificial intelligence technologies to develop more application scenarios and innovative services. It seems that the integration of Big Data and artificial intelligence is now a big trend in financial industry.
In summary, this study found out that tools and technology absolutely affected the Big Data value-creation chain in financial industry.
4.3.5 Organizational Culture and Leadership
All interviewees strongly agreed that organizational culture and leadership is an important factor that influence that value generated from Big Data.
Hard to Develop Data-Driven Culture
The chief technology officer of company D mentioned that rather than other key factors such as data quality, staff capacity and external support, the most important key factor in the value-creation chain of Big Data is a good organizational culture and the support of leaders. The chief technology officer of company D indicated that having a data-driven culture is important because creating a good organizational culture is very difficult and usually takes many years. Companies in financial industry used to take performance as the most important consideration, but the return of investing in Big Data may be insignificant in short-term. That is, it is a precondition for companies to have the full support of leaders and make a lot of effort to create a data-driven culture, which means that everyone in the organization should understand the importance of data, and have more tolerance on the trial and error process of Big Data.
Teamwork Culture
The interviewee of company A indicated that not everyone in the organization agreed the value of Big Data, and Big Data needs cross-domain cooperation, so it is indispensable to have a data-driven and teamwork organizational culture.
Opening Mind Culture
The chief technology officer of company B pointed out that while introducing new technologies or new business processes, the whole company should have an opening mind to embrace all the changes. Usually, each business unit may take their daily routine as the first priority, and not willing to spend much time for changing. Thus, leaders should strive to create an organizational culture that all employees in the
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company or even all members in all subsidiaries reach a consensus to understand the benefits and importance of Big Data, integrate the resources, and do the best to support the implementation of Big Data.
Full Support of Leaders
The company’s achievements in Big Data related to the full support of leaders. The company’s operating team attached great importance to Big Data and they had invested many resources to support it. In fact, they not just took the results of Big Data analysis as reference, but a base of decision-making.
To conclude, the study found out that the organizational culture and leadership enabled Big Data to create business value in financial companies.
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Table 4-3 Summary of Key Factors that Enable Big Data to Create Business Value (5-strongly agree; 4-agree; 3-neutral; 2-disagree; 1-strongly disagree)
Company A Company B Company C Company D
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