Chapter 2: Literature Review
2.1 Measuring Possible Benefits of Big Data
2.1.2 Possible Benefits of Big Data
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by Mirani and Lederer (1998), Gregor et al. (2006), and Ren et al. (2017), the business value created from Big Data can be categorized as informational benefits, transactional benefits, and strategic benefits. Informational value relates to effectiveness and efficiency of decision-making and information access, such as real time decision making. Transactional value relates to efficiency improvements and cost savings.
Strategic value relates to increasing competitive advantages (Gregor et al., 2006; Mirani and Lederer, 1998; Ren et al., 2017).
Table 2-1 Three Categories of IT-enabled Business Values
(Gregor et al., 2006; Mirani and Lederer, 1998; Ren et al., 2017) Category Possible benefits
Informational Enabling a faster or easier access to information for decision-making
Improving information quality or accuracy for decision-making Providing information in more useable formats
Transactional Reducing costs
Increasing financial returns
Enhancing productivity or efficiency Growing the business
Strategic Improving partnerships or relationships with other companies Enabling a faster response to changes
Improving customer relations and segmentation
Providing better or innovative products, services, or business models
Aligning analytics with business strategy Creating competitive advantages
2.1.2 Possible Benefits of Big Data
Many related literatures mentioned many kinds of business value that can be generated by Big Data, and most of them belong to the three categories of IT-enabled Business Values.
For example, Big Data may create informational values, e.g., leading to higher information quality, reducing the needed time for data gathering, and decreasing the data processing time (Wamba et al., 2015). With Big Data, Walmart and Kohl’s improved its decision quality on pricing and product selection (Lohr, 2012), Caesars Entertainment decreased its data processing time to make real-time decisions on marketing (Davenport and Dyché, 2013), and Netflix also improved its content recommendation engine (Bughin, 2016).
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Moreover, Big Data may create transactional value, e.g., by improving the speed of business execution, growing the business, and decreasing costs (Jablonski, 2014). With Big Data, UPS reduced its fuel cost, GE improved its efficiency and increased its revenue (Davenport and Dyché, 2013), Netflix grew its business (Bughin, 2016), and Intel launched its products in the market faster and decreased its cost (“How big data analytics yields big gains,” 2017).
Big Data may also create strategic value by identifying customer segments for customized promotions, or innovating new business models, products and services (Wamba et al., 2015). With Big Data, Google understood users better (Lohr, 2012), Amazon and Netflix understood their customer better (Bughin, 2016; Ding et al., 2014;
Ren et al., 2017), and Wells Fargo, Bank of America, Discover Financial Services, and United Healthcare improved their customer relationship (Davenport and Dyché, 2013).
Moreover, a cleaner production manufacturing company provided better and customized products and service, and successfully increased customer relationship, innovated its business model, and improved strategic cooperation with other companies (Zhang et al., 2017).
A summary of Big Data’s possible benefits is presented in Table 2-2. We can measure the return on investment in Big Data by ascertaining whether companies create any of the identified business values by implementing Big Data technologies.
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Table 2-2 Possible Benefits of Big Data
(Gregor et al., 2006; Mirani and Lederer, 1998; Ren et al., 2017)
Category Possible benefits References
Informational Enabling a faster or easier access to information for
Transactional Reducing costs Davenport and Dyché, 2013;
Jablonski, 2014; Bughin, 2016 ;
“How big data analytics yields big gains,” 2017
Increasing financial returns Enhancing productivity or efficiency
Growing the business Strategic Improving partnerships or
relationships with other companies
Lohr, 2012; Davenport and Dyché, 2013; Wamba et al., 2015; Zhang et al., 2017 Enabling a faster response to
changes
2.2 Factors Affecting the Business Value of Big Data
Although Big Data applications may appear to present an attractive business opportunity, numerous challenges remain (Shim et al., 2015). Reviewing prior studies related to factors influencing the value-creation process of Big Data and data-driven decision-making, this study summarizes the following five primary factor types that are strongly associated with the business value of Big Data.
2.2.1 Accessibility, Timeliness, and Quality of Data
Accessibility refers to the degree of data openness, with high accessibility meaning
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that a user can obtain many different types of data (Cai and Zhu, 2015). Accessibility of multiple data types enables complex analyses (Ikemoto and Marsh, 2007). However, acquiring data from diverse sources constitutes a significant challenge (Sivarajah et al., 2017). A lack of data accessibility poses an important obstacle to data-driven decision-making (Marsh et al., 2006). Indeed, numerous companies have experienced challenges while gathering all available data (Bughin et al., 2010). According to a survey conducted by MIT Sloan Management Review and the IBM Institute for Business Value, more than 20% of respondents cited data accessibility as the primary obstacle to becoming more data-driven (LaValle et al., 2011). The study of Wamba and colleagues (2015) reported that 34% of reviewed articles identified accessibility as the primary challenge of the Big Data-driven value-creation process.
Timeliness relates to the velocity of Big Data. To make both timely and correct decisions, it is also necessary to access data accurately and completely (Katal et al., 2013). The "shelf life" of data is very short due to the rapid data changes; without collecting the right data on time and processing it in real time, companies might reach outdated or invalid conclusions (Cai and Zhu, 2015). Moreover, the bigger the data volume is, the more important timely data collection and real-time processing will become (Jagadish et al., 2014).
Several studies identified data quality as an important factor affecting data use (Ikemoto and Marsh, 2007; Marsh et al., 2006). Recent research determined that data quality was associated with project results (Katal et al., 2013). Another recent study also observed data quality to be the primary challenge in several Big Data projects of Softserve Inc, a global software outsourcing company (Chen et al., 2015). Poor data quality leads to creating less business value and making wrong decisions in Big Data projects (Janssen et al., 2017). Therefore, the high quality of data is the foundation of motivating data-driven actions (Black et al., 2005). As poor data quality may lead to serious errors in decision-making, while ensuring Big Data quality remains challenging, the use and analysis of Big Data must be based on accurate and high-quality data to create value (Cai and Zhu, 2015). Besides, data quality issues are obstacles for real-time and actionable use of Big Data (Grover et al., 2018).
2.2.2 Data Policy
Because of the new collecting sources and new processing ways of data, data security concerns and data privacy concerns are increasing, and they need to be emphasized and solved (Bertino and Ferrari, 2018). A survey conducted by MIT Sloan Management Review and the IBM Institute for Business Value reported that more than 20% of respondents identified data policy as the primary challenge to becoming more data-driven (LaValle et al., 2011). Research also determined that data policies were an
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important aspect of business value enabled by Big Data (Wamba et al., 2015). For instance, many studies indicated that privacy of personal data was the primary topic of concern with Big Data applications (Emani et al., 2015; Jagadish et al., 2014; Shim et al., 2015). A recent study also regarded privacy and security as the most important issues of Big Data (Katal et al., 2013). Due to the growing security threats, Big Data must be protected to keep from negative impacts (Grover et al., 2018). The ethical concerns arise regardless of whether data are used for marketing (Shim et al., 2015).
The larger amount of data that companies can collect from customers, the more individual privacy concerns will arise (Aloysius et al., 2016). That is, the security and privacy challenges result from the large scale should be solved in order to achieve the expected benefits of Big Data (Li and Gao, 2016). Addressing the privacy concerns in the digital era represents the primary challenge of Big Data (Sivarajah et al., 2017), and it is suggested to combine different techniques to address the security concerns (Xu and Shi, 2016).
2.2.3 Staff Capacity and External Support
All forms of data require skills to translate it into information and actionable knowledge (Ikemoto and Marsh, 2007). However, for some organizations, the biggest challenge in implementing Big Data is the human capital issues, in details, the lack of Big Data experts may lead to failure on Big Data investment (Grover et al., 2018). In the survey conducted by MIT Sloan Management Review and the IBM Institute for Business Value, approximately 30% of respondents encountered a shortage of skills within their organizations (LaValle et al., 2011). A recent review observed many studies identifying the role of talent in the Big Data-driven value-creation process (Wamba et al., 2015). The demand for Big Data skills continues to rise due to the increasing speed, variety, and volume of information (Shim et al., 2015). However, many companies still lack the talent needed to create business value with Big Data (Bughin et al., 2010). For instance, care providers in the healthcare industry typically do not have the ability to perform complex analyses on Big Data (Neff, 2013).
The data lifecycle of Big Data includes planning, data collection, data preprocessing, analysis, interpretation, execution, and so forth (Blazquez and Domenech, 2017;
Jagadish et al., 2014). Technical skills are very important in the data lifecycle of Big Data, especially in data collecting, data preprocessing, and analysis phase. However, while dealing with Big Data, not only technical but also research, analytic, interpretive and creative skills are important (Katal et al., 2013). While numerous individuals possess the mathematical knowledge underlying the analytical models, few have the skills to apply such models correctly to Big Data (Madden, 2012). In conclusion, analytical and information technology skills are important to turn raw data into
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meaningful and actionable knowledge, but in order to provide useful insight and support decision-making, the ability of understanding business problems, developing solutions, and communicating are also necessary in Big Data process (Chen et al., 2012).
Qualified staff are the key success factor of Big Data, as numerous varied skills and extensive knowledge are required in Big Data projects, necessitating recruitment of talented employees, training current workers, or organizing appropriate teams according to business goals (Provost and Fawcett, 2013). For instance, GE recruited approximately 400 talented data scientists and developed a special training program for them (Davenport and Dyché, 2013). A study of Wamba et al. (2017) found out that expertise capability and management capability are important pillars of Big Data, organizations must improve their staff's technical knowledge, technological management knowledge, business knowledge, and other relational knowledge of Big Data (Wamba et al., 2017). Finding enough skilled professionals to help companies solve problems by using Big Data is important yet very difficult (McAfee et al., 2012), and it is necessary for firms to keep training their staff on analytics techniques and communication ability (Fang and Zhang, 2016). Planning skills, technical skills, managing skills, interpreting skill, and communicating skills are enablers to transform data into valuable actions.
The research of Janssen et al. (2017) indicated the importance of staff having appropriate skills and capabilities and highlighted the difficulty of identifying the suitable individuals with sufficient knowledge and communication skills to work with Big Data and interpret results. The difficulty arises from Big Data involving analysis of a large variety of parameters and variables, with knowing how to identify and apply the correct technique being a significant challenge. Overcoming this challenge may necessitate skills training of internal staff, hiring external personnel, and potentially collaborating with external organizations (Janssen et al., 2017).
It is unsurprising that outsourcing is the primary choice of many enterprises, as it helps them deploy Big Data systems quickly and return to focusing primarily on core competencies (Chen et al., 2015). Numerous large companies outsource a portion of Big Data processing to external organizations (Jagadish et al., 2014). External support is undoubtedly helpful to organizations transforming raw data into actionable knowledge (Ikemoto and Marsh, 2007).
2.2.4 Tools and Technology
Tools, especially complex ones, are an important factor in data-driven decision-making (Ikemoto and Marsh, 2007). Although the potential value of Big Data is well-recognized, numerous technical challenges remain, e.g., the task of loading a large quantity of data (Jagadish et al., 2014). It is difficult to reduce the time required to store
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and process a large amount of data (Katal et al., 2013). The large size and related storage aspects of Big Data, especially involving unstructured data that is very difficult for traditional tools to work with, are common challenges for many companies (Tankard, 2012). Most traditional analytic tools, e.g., R, SAS and MATLAB, are not designed to process a quantity of data exceeding a single computer's amount of RAM (Madden, 2012). Developing Big Data systems is very different from developing those oriented towards traditional structured data (Chen et al., 2015). Such unstructured data, as text, images, videos, and sensor data streams, are difficult to work with using traditional databases (Lohr, 2012). Furthermore, methods of querying and mining Big Data differ greatly from those of traditional data analysis, as the data are noisier and more dynamic, heterogeneous and correlated, while at the same time being less trustworthy (Jagadish et al., 2014).
Identifying the appropriate tools and techniques for analysis and visualization of Big Data is challenging due to its complexity (Janssen et al., 2017). A recent study observed that technology was the most important challenge to Big Data-enabled business value (Wamba et al., 2015). However, many companies do not have the key technologies to create information and value from data (Bughin et al., 2010). In particular, many companies encountered technology-related difficulties; e.g., Walmart experienced difficulties transmitting a million transactions per hour to a database (Swan, 2013).
Technology selection affects many aspects of system performance, consistency, availability, latency, scalability and modifiability (Chen et al., 2015). Big Data involves numerous innovative technologies due to its complexity (Provost and Fawcett, 2013).
It is important to use scalable technologies to store data, such as Hadoop, NoSQL, HBase, MongoDB, Cassandra, Microsoft Azure, or Amazon Web Service ( Mikalef et al., 2017). For example, non-traditional data, such as large-scale log data, sensor data, or social media data, needs new technology such as Hadoop to handle, because non-traditional data is hard to handle by non-traditional data warehouse (Katal et al., 2013).
Many organizations, such as NASA and Quantcast, succeeded in using Big Data following installations of new technology and tools (Simon, 2013). Moreover, United Healthcare was able to analyze voice data after adapting a variety of tools and techniques, such as “natural language processing” software, Hadoop and NoSQL storage (Davenport and Dyché, 2013). New technology and methods need to be adopted to create value from Big Data due to its scale (Katal et al., 2013), and the growth of the data scale appears to outpace advances in computer technologies (Jagadish et al., 2014;
Sivarajah et al., 2017). Therefore, technology will remain a key component of Big Data (McAfee et al., 2012).
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2.2.5 Organizational Culture and Leadership
Sometimes organizational barriers, such as lacking a culture of collaboration, may pose a challenge to companies trying to benefit from Big Data (Beath et al., 2012). A trusting and data-driven culture enables complex data-driven decision-making (Ikemoto and Marsh, 2007). The head of analytics at Macys.com, a well-known online retailer, mentioned that the company’s ROI-driven culture is the primary reason for failing to prioritize the Big Data technologies (Davenport and Dyché, 2013). The survey conducted by MIT Sloan Management Review and the IBM Institute for Business Value reported that more than 20% of respondents cited the organizational culture as an obstacle to becoming more data-driven (LaValle et al., 2011). It is important for organizations to have a data-driven culture, meaning that all employees are aware of data’s importance to decision-making and understand that data are not merely the responsibility of management or the IT department, but rather everyone’s business (Provost and Fawcett, 2013). That is, organizations should avoid intuition-driven decisions and avoid pretending to be more data-driven than is the case (McAfee et al., 2012).
In addition to the organizational culture, leadership might be a key factor affecting the value-creation process with Big Data. According to the survey conducted by MIT Sloan Management Review and the IBM Institute for Business Value, the primary challenge for leaders is not knowing how analytics can help business (LaValle et al., 2011). Leaders with data-driven visions increase the openness and collaboration within an organization (Ikemoto and Marsh, 2007; Marsh et al., 2006). To succeed in the era of Big Data, companies should be led by those who can set clear goals, define the path taken to reach them, and ask the right questions (McAfee et al., 2012). For instance, medical doctors often refuse to invest in Big Data over concerns with the expense of time, resources and personnel, with the perceived cost being much higher than the expected benefits, hence posing an obstacle to creating value from Big Data (Neff, 2013). In conclusion, the leaders must take the responsibility for the organization becoming aware of the importance and benefits of Big Data, motivate and provide clear goals to staff, monitor all processes and resources, and understand the data that can provide the needed insights (Provost and Fawcett, 2013).
In conclusion, company culture that is data-driven and evidence-driven, and
leadership teams that can ask the right questions and have clear goals, are both helpful for generating value from Big Data (Grover et al., 2018).
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Table 2-3 Factors Affecting Big Data
Factors Description References
Accessibility,
Ikemoto and Marsh, 2007; Bughin et al., 2010; LaValle et al., 2011; Katal et al., 2013; Jagadish et al., 2014; Cai and Zhu, 2015; Chen et al., 2015;
Wamba et al., 2015; Janssen et al., 2017; Sivarajah et al., 2017; Grover et al., 2018
Data Policy The privacy, security, and ethical concerns of data.
LaValle et al., 2011; Katal et al., 2013;
Jagadish et al., 2014; Shim et al., 2015; Wamba et al., 2015; Aloysius et al., 2016; Li and Gao, 2016; Xu and Shi, 2016; Sivarajah et al., 2017;
Bertino and Ferrari, 2018; Grover et al., 2018 restore the focus on core competencies.
Marsh et al., 2006; Ikemoto and Marsh, 2007; Bughin et al., 2010;
LaValle et al., 2011; Madden, 2012;
McAfee et al., 2012; Davenport and Dyché, 2013; Katal et al., 2013; Neff, 2013; Provost and Fawcett, 2013;
Jagadish et al., 2014; Chen et al., 2015; Shim et al., 2015; Wamba et al., 2015; Fang and Zhang, 2016; Janssen et al., 2017; Grover et al., 2018
Ikemoto and Marsh, 2007; Bughin et al., 2010; Lohr, 2012; Madden, 2012;
McAfee et al., 2012; Tankard, 2012;
Davenport and Dyché, 2013; Katal et al., 2013; Provost and Fawcett, 2013;
Simon, 2013; Swan, 2013; Jagadish et al., 2014; Chen et al., 2015; Janssen et al., 2017; Mikilef et al., 2017;
Sivarajah et al., 2017;
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Organizational Culture and Leadership
Employees should know that data is important to decision-making and fully engaged in the project.
Leaders should make staff aware of importance and benefits of Big Data, set clear goals, monitor processes and resources, and understand the data that can provide the needed insights.
Marsh et al., 2006; Ikemoto and Marsh, 2007; LaValle et al., 2011;
Beath et al., 2012; McAfee et al., 2012; Davenport and Dyché, 2013;
Neff, 2013; Provost and Fawcett, 2013; Grover et al., 2018
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Chapter 3: Research Methodology
3.1 Research Design and Process
The research process of this study is shown in Table 3-1. The research objective is to determine whether Big Data provides sizable benefits to companies and identify the key factors affecting the Big Data-driven value-creation process. A qualitative case study is a useful tool for analyzing complex phenomena; to understand and compare similarities and differences of various cases, multiple case studies will be conducted (Baxter and Jack, 2008). This study will choose four enterprises that invested in Big Data as study targets, and use a semi-structured questionnaire (Appendix A) which is designed based on the reviewed literature to conduct an in-depth interview. However, prior to interviewing the selected firms, a contextual analysis was completed. To accomplish the first objective, this study summarized the possible benefits of Big Data according to multiple related stories that can help measure the business value created by Big Data in practical cases. To accomplish the second objective, this study summarized five primary key factors, enabling beneficial applications of Big Data according to multiple related studies.
Subsequent to the interviews, this study will try to validate the benefits Big Data has created for the respective companies and will attempt to analyze each key factor hypothesis, proposed based on the literature review, in four practical cases. Finally, this study will systematically compare the findings across the four cases and draw a conclusion based on the research results of the multi-case study and the literature review.
Subsequent to the interviews, this study will try to validate the benefits Big Data has created for the respective companies and will attempt to analyze each key factor hypothesis, proposed based on the literature review, in four practical cases. Finally, this study will systematically compare the findings across the four cases and draw a conclusion based on the research results of the multi-case study and the literature review.