Despite many opportunities and benefits with the use of big data analytics in a business firm, many organizations face obstacles in the adaption of BDA. As a result, they fail to achieve their organizational goal. Even though BDA can create value by improving business process effectiveness, efficiency, and productivity, the improvement of business processes is a difficult challenge that requires complicated and robust supporting systems (Grover et al., 2018). There are severe issues for different applications in terms of data, process, analytical modeling, and management to transform BDA into strong ideas for creating value (Saggi & Jain, 2018). Based on the research study carried out by (Kache & Seuring, 2017), the main challenges of big data deployment on a corporate level can be IT capabilities and infrastructure, business strategy and objective, talent management and HR, as well as information and cybersecurity, while the challenges from a supply chain perspective can be governance and compliance, integration and collaboration, IT capabilities and infrastructure, and information and cybersecurity.
In a company, the team with clear BDA strategies that have a clear goal and can formulate the business issue would probably succeed, as the team will be able to identify the success and think logically on their strategy (Grover et al., 2018). However, it is challenging for the manager in a company to think critically about analytics techniques and analyze them based on those data (Saggi
& Jain, 2018). Similarly, an organizational structure and the governance framework plays a significant role in collecting and analyzing data over a firm and delivering ideas into where it is most required. The data should be gathered and processed in centralized governance to ensure that all the projects in a firm related to big data apply the common standards, protocols, methods, and tools. (Grover et al., 2018).
2.4.1 Talent Management and Human Resource
The lack of big data expertise, such as data scientists and big data analysts in an organization is one of the major issues that need to be addressed and solved (Mikalef, 2017; Grover et al., 2018).
For an organization, it is very challenging to manage their employees and human resources because the company needs a qualified and skilled big data personnel capable of understanding statistical modeling, operating big data systems and analyzing the streams of data, but the companies struggle to recruit such skilled employees (Kache & Seuring, 2017). The reason for this issue could be an improper job description or the lack of knowledge of what they may be required while recruiting the employees. The ignorance of the recruiter or any employees in an organization could be the
12 reason behind it. However, the quality of accepting the ignorance and turning it into knowledge is crucial for any firm since it is difficult for rivals to mimic such ability of a company (Erevelles et al., 2016). Therefore, a firm must tackle this issue and develop a team of experts with enough BDA skills and knowledge to capitalize on the promise of big data (Grover et al., 2018).
2.4.2 IT capabilities and infrastructure
Strong IT skills and infrastructure are needed to analyze and use data from a large amount of information. The IT capabilities issues include a lack of skilled and qualified IT human resources in a firm. The financial investment cycle could be the reason for the shortage of capable IT infrastructure. The modification or replacement of the current IT infrastructure is often delayed as the capital may not have been entirely amortized by the period of update (Kache & Seuring, 2017).
To manage the required IT infrastructure and to prioritize the investments in the system or technology needed for BDA is a challenge for the company.
2.4.3 Information and cybersecurity
There is a huge volume of data in a company that should be managed, but the bulk of data comes with great responsibility. Privacy and security are the popular focus of most of the massive data regulations, policies, and protocols being created (Demirkan et al., 2015). A business organization should ensure the data security that includes access authorization, data auditing monitoring of data exchange, and the structure of the governance (Zeng & Glaister, 2018). A company has access to many sensitive data of the customer, which is to be kept confidential. Such access to sensitive data and information leads to data security issues from a customer point of view. So, the customers may be more careful, or they may not want to share their information with the company, which could hinder an enterprise in data collection of their client (Kache & Seuring, 2017). In a firm, there might be a situation in which third party and workers are involved in data analytics for which a company must address any possible security, confidentiality regulation compliance, or liability concerns that might emerge from such a situation (Grover et al., 2018). Ensuring that the data of their customers in a company is not being shared with anyone or any kind of entity without the owner’s consent and managing cybersecurity is a significant challenge for the company in a digital world (Kache & Seuring, 2017). An organization should prepare and plan for cyber-attacks and develop and implement strategies to prevent such potential violations in a timely way from reducing their deleterious effect and from creating data security and data management systems secure (Grover et al., 2018).
2.4.4 Decision-making challenges
BDA has power in dealing with real-time risks that guide an enterprise to enhance its decision making for business planning (Xu et al., 2016). The use of business analytics helps an enterprise to provide valuable decision-making by using analytical methods to minimize operating costs and accurately predict market trends (Trkman et al., 2010). However, realizing the full potential of
13 BDA is indeed a real challenge. Therefore, the decision support system should be very carefully implemented (Shang et al., 2008). Similarly, maintaining the excellent quality of data in order is also vital in decision-making processes. For instance, weak quality data resulting from redundant applications and databases consumes unnecessary data storage costs and is difficult to access data when needed (Beath et al., 2012). According to (Fosso Wamba et al., 2015), there is always a risk of duplicate, inaccurate and redundant data in a system that results in an erroneous decision by the manager and also wastes the organizational resources. This will be the barrier while turning the big data into business value.
14 This page is intentionally left blank.
15
3 Research Method
This chapter presents the research and data analysis methods used for this thesis. The research strategy is derived and implemented from the concept and ideas recommended by (Oates, 2005).
Since the purpose of this research is to examine the ways in which different companies these days are handling and deploying big data analytics, the cases were studied using an interview as a data generation method, which is described briefly in section 3.1. Further, qualitative data was collected from the semi-structured interviews with two different companies with a cross-cultural background; Company A in Greece and Company B in Nepal. Similarly, the data collection processes, interview processes, and the participants are presented in Section 3.2.