Master Thesis
The effect of big data on firm performance.
“What is the effect of the use of big data on firm performance?”
by
Reinder Jan Klunder S1903713 Penningkruid 27 7681TJ Vroomshoop klunderrj@gmail.com Word count: 6840 University of Groningen Faculty of Economics and Business
June 2016
Abstract
This research paper shows the results of a study that examines the effect of big data on firm performance. The results suggest that the use of big data is positively related to firm performance. Besides, the interaction effect of knowledge-based resources weakens the relationship between big data and firm performance. Finally, implications for research and management are discussed.
Introduction
Nowadays, big data and big data analytics are important for science and business (Sagiroglu & Sinanc, 2013). Big data emerged as the latest matter of business intelligence and business analytics (Wixom et al., 2014), where firms are exploring big data to discover new insights and by using advanced analytics, they can study big data to understand the current state of the firm and keep up ever evolving issues (Russom, 2011). This implies that firms need large volumes of highly detailed data to know exactly what is going on (Russom, 2011). By analyzing business data a firm can better understand its business and market and make timely business decisions (Chen, Chiang, & Storey, 2012). On the other hand, the challenges of becoming a big data enabled organization can be enormous (McAfee & Brynjolfsson, 2012) and not all firms know how to deal with big data. Only few firms have made corresponding investments in organizational processes to derive business value from data and information (Bharadwaj, El Sawy, Pavlou, & Venkatraman, 2013) and many firms do not have their technology stack designed for advanced analytics and big data (Russom, 2011). Using big data can have considerable benefits but it does not come easily and the extent of the benefits depends on how firms deal with big data. This massive amount of detailed data that is made available opens up new (digital) business strategy approaches (Bharadwaj et al., 2013). Currently, firms and organizations from all sectors gain critical insights from big data, which can lead to recognizing new business opportunities (Chen et al., 2012). The goal of this study is to explore the relationship of the use of big data with firm performance. The contribution to the business practice is evidence of the fact that managers can improve firm performance by exploiting big data.
when this study tested the moderating effects of knowledge-based resources on the effect of big data on firm performance.
This research paper starts with a review of the literature, which discusses the hypotheses. Then the methodology is explained. Thereafter, the results of this study are presented. Finally, the findings of this study are discussed and the conclusions are drawn.
Research Question The main research question is:
What is the effect of the use of big data on firm performance?
Literature Review
In this section a review of the literature is presented. First, the difference and relatedness of data, information and knowledge will be addressed, because big data is concerned with turning data into knowledge. Second, the concept of big data and the relation of big data with firm performance will be further elaborated, which results in the first hypothesis. Third, this research paper examines big data with regard to strategy content research, which recently regards the resource-based view. Here, the dynamic capabilities view and the knowledge-based view will be addressed, because both are extensions of the resource-based view. The dynamic capabilities view results in the second hypothesis and the knowledge-based view results in the third hypothesis. This section ends with showing the conceptual model.
Data can be considered as the basis for creating information and knowledge (Greiner, Böhmann, & Krcmar, 2007; Nereu, Kock, Mcqueen, & Corner, 1997). However, knowledge is a complex concept, which consists of different typologies and the only consensus appears to be the idea that knowledge is more than just data and information (Greiner et al., 2007). Data, information and knowledge are different in meaning, but are strongly related to each other. Therefore, these concepts will be addressed more closely.
Big Data
Most firms are not successful at turning data into knowledge (Davenport, Harris, De Long, & Jacobson, 2001). The human contribution of analyzing and interpreting data in order to act on the insights is the most important step in the data transformation process, which most firms have neglected (Davenport et al., 2001). In line with this, Boyd and Crawford (2012) argue that big data is more about the capacity to search, combine, and cross-reference large data sets than it is just about data that is big (Boyd & Crawford, 2012). Wamba, Akter, Edwards, Chopin and Gnanzou (2015) have analyzed the definitional perspectives of big data in their paper. Many big data definitions focus on different aspects, however Wamba et al. (2015) propose a more holistic approach. Therefore, this research paper adopts the definition of big data from Wamba et al. (2015), who define big data as “a holistic approach to manage, process and analyze 5 Vs (i.e., volume, variety, velocity, veracity and value) in order to create actionable insights for sustained value delivery, measuring performance and establishing competitive advantages” (p. 235). This definition indicates that big data consists of two elements, which are the characteristics of big data and the process of big data. The characteristics of big data includes volume, variety, velocity, veracity and value and the process of big data includes the approach to manage, process and analyze these characteristics. Actually, big data is more, it’s also about the process of big data than it is just about data that is big (Boyd & Crawford, 2012; Davenport et al., 2001; Wamba et al., 2015).
Considering the characteristics of big data, firms can analyze among others, the large amount of data that consumes huge storage and involves a large number of records data, the frequency and speed of the generation and delivery of data, the large variety of sources and formats of data, the economic benefits from the available data, and the quality of data (Russom, 2011; Wamba et al., 2015). These diverse aspects already indicate that properly dealing with big data is complex. For example, the types of data generated and stored differ for each firm, i.e. whether the data encodes video, images, audio, or text/numeric information (Manyika et al., 2011), which are not all structured data. According to Manyika et al. (2011), structured data is data that resides in fixed fields. If firms would only face huge amounts of structured data, they could manage it simply by improving storage efficiency or purchasing more storage (Wu, Zhu, Wu, & Ding, 2014). However, the value of big data is in its complexity and that is ‘represented in many aspects, including complex heterogeneous data types, complex intrinsic semantic associations in data, and complex relationship networks among data’ (Wu et al., 2014, p. 13). Accordingly, firms have been making business decisions based on data stored in databases, however big data offers opportunities that go beyond structured databases to rely on less structured data (Wu et al., 2014). Thus, firms have to acquire and organize these diverse data sources in order to discover new insights and profit from hidden relationships (Wu et al., 2014).
generate more transaction data. Only for the most structured and routine decision processes the data analysis can be automated but for other decisions skilled human resources are more important (Davenport et al., 2001). Besides, firms traditionally are inclined to begin with gathering all available data before they initiate their analysis (Lavalle, Lesser, Shockley, Hopkins, & Kruschwitz, 2011). This focus hinders the understanding of the potential uses, which may result in actions that are not the most optimal ones (Lavalle et al., 2011). The process of big data contains more than just searching and gathering all available data. Wamba et al. (2015) state that it also encompasses managing, processing and analyzing of data. Likewise, Davenport et al. (2001) argue that in order to generate business value a firm has to develop the capability to aggregate, analyze, and use data to facilitate this decision making. Put this way, the process of big data is quite similar to the concept of absorptive capacity, which Flatten, Engelen, Zahra, & Brettel (2011) have defined as ‘a firm’s ability to recognize the value of new external knowledge, assimilate it, and apply it to commercial ends’ (Flatten et al., 2011, p. 100). Following these dimensions of Flatten et al. (2011) helps to structure the concept of the process of big data: it is about engaging in data acquisition, assimilating acquired data, transforming data, and exploiting data.
Big Data and Performance
According to McAfee and Brynjolfsson (2012), using big data can be seen as a management revolution. Because of big data, managers can measure and know more about their firm (McAfee & Brynjolfsson, 2012; Russom, 2011). Besides, by analyzing business data a firm can also better understand its market and respond adequately (Chen et al., 2012), make better predictions, make smarter decisions, and target more effective interventions (McAfee & Brynjolfsson, 2012). Moreover, firms can translate that knowledge directly into improved decision making and performance (McAfee & Brynjolfsson, 2012). Brynjolfsson, Hitt, & Kim (2011) examined whether firms with decision making based on data and business analytics (i.e. data driven decision making) show higher performance. They found that data driven decision making is associated with higher productivity and market value, and they found some evidence that it is also associated with certain measures of profitability (Brynjolfsson et al., 2011). Lavalle et al. (2011) conducted a survey on opportunities associated with the use of business analytics, in which they also endorse this. They found that top-performing firms use big data analytics five times more than firms with lower performance (Lavalle et al., 2011). In summary, using big data enables firms to make decisions based on evidence rather than intuition (McAfee & Brynjolfsson, 2012) These firms show higher performance due to the use of big data.
Big Data and Strategy
Big data provides opportunities for firms to leverage and enlarge their strategic tools (Bharadwaj et al., 2013; Woerner & Wixom, 2015). According to Manyika et al. (2011), big data can create business value in five broadly applicable ways: creating transparency; enabling experimentation to discover needs, expose variability, and improve performance; segmenting populations to customize actions; replacing/supporting human decision making with automated algorithms; and innovating new business models, products, and services. Though, Woerner and Wixom (2015) argue that big data itself does not create strategic benefits, as deriving value from big data relies on strategy and related processes and structures. Moreover, without a strategic context firms do not know on which data the focus should be on and how they should allocate their resources (Davenport et al., 2001). This research paper further examines the relationship between using big data and firm performance regarding strategy research.
Strategy research is mainly divided into two perspectives, namely strategy content and strategy process (Chakravarthy & Doz, 1992). Strategy content research focuses on the strategic positions of the firm and what positions lead to optimal performance under changing environmental contexts (Andrews, Boyne, Law, & Walker, 2009; Chakravarthy & Doz, 1992). Strategy process research regards how administrative systems and decision processes of a firm influence its strategic positions (Chakravarthy & Doz, 1992). This research paper focuses on strategy content.
Strategy content research has recently extended its attention to the resource-based view (RBV) in strategy research (Chakravarthy & Doz, 1992). According to Kraaijenbrink, Spender, and Groen (2010), the central proposition of the RBV is shared by several related analyses and two of those are the dynamic capabilities view and the knowledge-based view. This research paper addresses the dynamic capabilities view and the knowledge-based view since both are extensions of the resource-based view.
Absorptive Capacity as a Dynamic Capability
Kraaijenbrink, Spender, and Groen (2010) argue that the RBV deals with dynamic issues in a limited way:
With its focus on the possession of resources and capabilities, the RBV is inherently static, not well equipped to explain the timing of when value is created, when rents are appropriated, and how firms innovate and generate new sources of SCA. (Kraaijenbrink et al., 2010, p. 366)
& Martin, 2000). In such markets managing knowledge resources is particularly critical (Eisenhardt & Martin, 2000). An important principle of big data is that the world and the data are constantly
changing (Davenport, Barth, & Bean, 2012), which indicates that big data involves such dynamic markets where knowledge is essential.
Firms should build their big data capabilities, which will take time and the effect of
developing this superior capability will enhance the long term competitive advantage (Manyika et al., 2011). Scaling big data with business strategy will require understanding of the development of organizational capabilities in order to exploit the data, information, and knowledge that is generated continuously (Bharadwaj et al., 2013). Likewise, Zahra and George (2002) argue that in view of fostering mutual understanding, the exploitation of knowledge requires the sharing of relevant knowledge between members of the firm. Furthermore, they propose that absorptive capacity can be viewed as a dynamic organizational capability concerning the creation and utilization of knowledge whereby a firm is better able to gain and sustain a competitive advantage (Zahra & George, 2002). Zahra and George (2002) define absorptive capacity as ‘a set of organizational routines and processes by which firms acquire, assimilate, transform, and exploit knowledge to produce a dynamic
organizational capability’ (p. 186). According to Zahra and George (2002), absorptive capacity is a dynamic capability that consists of potential and realized absorptive capacities. Which is distinguished as follows, ‘potential capacity comprises knowledge acquisition and assimilation capabilities, and realized capacity centers on knowledge transformation and exploitation” (Zahra & George, 2002, p. 185). Absorptive capacity affects the ability of a firm to create and deploy the knowledge required to build other organizational capabilities (Zahra & George, 2002). These different capabilities provide the firm with a base that can produce superior performance (Zahra & George, 2002). In this research paper, the process of big data consists of similar processes as absorptive capacity, that is acquisition, assimilation, transformation, and exploitation. Since these two concepts are very similar, the question arises whether there is any difference. Absorptive capacity is more broadly defined, it concerns the firm’s ability to acquire, assimilate, transform, and exploit knowledge. Big data is primarily about turning data into knowledge, where knowledge is the outcome of data that is interpreted by its recipient.
Data is the basis for creating knowledge (Greiner et al., 2007; Nereu et al., 1997) and the goal is to turn data into knowledge. As mentioned earlier, firms that are using big data show higher
performance since they make decisions based on evidence rather than intuition. In other words, these firms show higher performance because they turn data into knowledge. Particularly now big data is part of every sector and function of the global economy (Manyika et al., 2011), knowledge has become a critical resource towards superior performance (Hitt, Bierman, Shimizu, & Kochhar, 2000).
These firms derive more value from the knowledge, which they obtained from their big data, because of their ability to create and deploy knowledge on which other organizational capabilities can build, including the big data capability. Therefore, firms with a well-developed absorptive capacity can achieve better firm performance from using of big data.
Hypothesis 2: The extent to which a firm developed its absorptive capacity moderates the impact of the use of big data on firm performance.
Knowledge-Based Resources
The knowledge-based view ‘focuses upon knowledge as the most strategically important of the firm' s resources, it is an outgrowth of the resource-based view’ (Grant, 1996, p. 110). Knowledge as a resource has the best ability to contribute a firm’s sustainable competitive advantage, because of its inimitability (Grant, 1996; Wiklund & Shepherd, 2003). Wiklund and Shepherd (2003) suggest that knowledge-based resources are positively related to firm performance. Knowledge-based resources are ‘the ways in which firms combine and transform tangible input resources’ (Wiklund & Shepherd, 2003, p. 1307). In other words, the knowledge-based resources are the organizing principles, skills, and processes that manage organizational action (Galunic & Rodan, 2016). In order to create value from big data, it is important to have the required knowledge-based resources (Wamba et al., 2015). If the firm’s organizing principles, skills, and processes are well-organized, then this positively affects the ability to derive value from big data. Through improved knowledge-based resources a firm is better able to organize their processes and that of big data. Because of big data, firms know more and they can exploit this to improve firm performance (McAfee & Brynjolfsson, 2012). It allows them to make more accurate predictions about changes in the environment and the adequacy of their strategic actions (Wiklund & Shepherd, 2003). Moreover, by integrating knowledge on a broad scale, a firm increases causal ambiguity and barriers to duplication (Grant, 1996). Thus, greater breadth of knowledge enhances organizational capabilities (Grant, 1996) in such a way that it creates competitive advantage. The value of big data is in its complexity. Therefore, in order to derive value from big data, firms need to make big data trustworthy and understandable to all employees (Wamba et al., 2015). Hereby, it is important that all level of employees have sufficient knowledge (Wamba et al., 2015), which thus involves integrating knowledge on a broad scale. Thus, knowledge-based resources strengthen the effect big data has on firm performance.
Conceptual Model Figure 1: Conceptual Model
Methodology
This research paper used a theory testing approach in that the data collection consists of a quantitative approach. The hypotheses were tested using hierarchical regression analyses, which allow evaluation of the interaction effects on top of the direct effect of the independent variable (Jaccard, Wan, & Turrisi, 1990; Wiklund & Shepherd, 2003).
Data Collection
Sample. For manageability reasons, this study does not limit its sample by focusing on specific sectors or types of firms. The main part of the sample contains small and medium-sized enterprises (SMEs). These firms fit the survey well because SMEs generally consist of one whole and not of several parts. Since this study already has a small sample size, a strict SME focus was not pursued. The total sample consists of 112 responses.
Collecting data through online survey. The data was collected through an online survey by means of a survey tool, which is made available by the University of Groningen. This tool is called Qualitrics and enables online data collection. The online survey was created by means of Qualtrics Survey Software. The survey was sent by email to 180 people from the personal network of the researcher. This group produced 58 responses and has a response rate of 32%. The level of this rate is largely due to the sending of multiple reminders. The other 54 successful responses resulted from distributing the survey through social media platforms targeted at different interests related to working people (e.g. retail and manufacturing). This way a lot of people could be reached but it was not possible to keep track and send reminders.
Measurements
Big data. Big data is “a holistic approach to manage, process and analyze 5 Vs (i.e., volume, variety, velocity, veracity and value) in order to create actionable insights for sustained value delivery, measuring performance and establishing competitive advantages” (p. 235). Unfortunately, measurements of big data are absent in relevant research scholars. Hence, this definition of big data is used as the basis for creating a big data measure. As has been noted before, this definition indicates that big data consists of two elements, which are the characteristics of big data and the process of big data. The characteristics of big data includes volume, variety, velocity, veracity and value. Based on these characteristics the respondents were asked to rate 17 items about their data. The items of the process of big data are based on the processes of absorptive capacity (Flatten et al., 2011). The items of the process of big data are about searching for data, collecting and storing data, analyzing and interpreting data, converting data such that it fits us, and exploiting data. In sum, this measure measures the extent to which the data is ‘big’ in terms of the 5 Vs and the processes are extensive. All the 22 items concerning big data are measured by a Likert scale ranging from 1 = “totally disagree” to 5 = “totally agree” (α = 0.96).
Absorptive capacity. For measuring the absorptive capacity, this study follows Flatten et al. (2011). Flatten et al. (2011) developed and validated a multidimensional measure of absorptive capacity, which make it possible to compare to those of other firms. The 14 items of this variable consist of 3 items on the acquisition of knowledge, 4 items on the assimilation of knowledge, 4 items of the transformation of knowledge, and 3 items on the exploitation of knowledge (Flatten et al., 2011) (α = 0.93).
Knowledge-based resources. For the measurement of knowledge-based resources this study follows Wiklund and Shepherd (2003). These scales measure the firm's knowledge position in relation to competitors (Wiklund & Shepherd, 2003). Wiklund and Shepherd (2003) had 11 items referring to market and technological knowledge:
“Compared to other companies in your industry, does your company have a weak or strong position in terms of: staff with a positive commitment to the company's development, technical expertise, expertise regarding development of products or services, highly productive staff, expertise in marketing, special expertise regarding customer service, special expertise regarding management, innovative markets, staff educated in giving superior customer service, staff who like to contribute with ideas for new products/services, and staff capable of marketing your products/services well.” (p. 1311)
Firm performance. For measuring the performance of the firm the performance measures of Newbert (2008) are used. Newbert (2008) mentioned data on objective performance are not available unless there are private firms in the sample. Since private firms won’t be in the sample the objective performance measure is not used. Thus, performance is measured through a subjective scale that includes both financial (sales and profitability) and nonfinancial (marketing and market share) measures (Newbert, 2008). These items are positively coded to the firm performance (Newbert, 2008). This firm performance variable is operationalized by summing the responses to the four items (Newbert, 2008) (α = 0.83).
Control variables. This study controls for firm size and firm age. First, the firm size is operationalized as the firm’s number of employees (Newbert, 2008; Zona, Zattoni, & Minichilli, 2013). Second, the firm age is operationalized as the number of years that the company exists. This variable is based on the difference between the current date and the specified year of the firm’s establishment, which the respondents were asked to indicate in the survey. Besides, the environmental effects, which measures the degree to which the respondent perceives that the firm's environment is characterized by competition and risk (Newbert, 2008), was excluded from this research paper because it was not a reliable scale (α = 0.53).
Survey pre-test. Since the constructs and measurements are adopted from other studies (Newbert, 2008; Wiklund & Shepherd, 2003), which all administered full studies that resulted in all constructs and measures to be consistent, a pre-test of the survey is not considered to be necessary.
Results
The skewness and kurtosis statistics showed that the dependent variable is approximately normally distributed, with a skewness of -0.291 (SE = 0.228) and a kurtosis of 0.307 (SE = 0.453) (Razali & Wah, 2011; Shapiro & Wilk, 1965). Table 1 shows the means, standard deviations and correlations of all variables.
Table 1
Means, standard deviations and correlations for all variables
The hierarchical regression was conducted with firm performance as the dependent variable. The results are displayed in Table 2. Model 1, with control variables only, controls for firm age and firm size. This model is not significant and shows that the control variables only explain 1% of the variance in firm performance (R2 of .010). Model 2 predicts firm performance based on the big data of the firm. This model makes a significant contribution (F(3, 108) = 4.407, p = .000), with a change in the R2 value of .203. The significant and positive effect of big data supports hypothesis 1, which is that big data is positively related to firm performance. Model 3 and 4 with the control variables and the independent variable calculates the contribution of the moderating effects separately. Model 3 includes the absorptive capacity and Model 4 includes the knowledge-based resources of the firm. In order to obtain the interaction terms, each moderator variable was multiplied with the independent variable. Model 3 shows the change in the R2 value is .226 and not significant (p = .174). Thus, adding absorptive capacity in this model does not improve the prediction in a statistically significant way. Moreover, the coefficients of the regression also show that absorptive capacity is not significant (β = .339), therefore Hypothesis 2 is not supported. Model 4 shows the change in the R2
value is .136
Table 2
Independent and contingency models
Model 1 Model 2 Model 3 Model 4
B (SE) β B (SE) β B (SE) β B (SE) β
Constant 3.439** (.099) 2.183** (.254) 2.477** (.332) 2.858** (.272) Control Firm Age .000(.002) .005 .001 (.002) .030 .001 (.002) .040 .001 (.002) .038 Firm Size .000 (.000) .097 .000 (.000) -.036 .000 (.000) -.048 .000 (.000) -.054 Main Effect Big Data .401** (.076) .469 .131 (.211) .153 -.447* (.192) -.523 Interactions
Big Data x Absorptive
Capacity .026 (.019) .339
and is significant (p = .000). Thus, adding knowledge-based resources in this model does improve the prediction. Moreover, the coefficients show that knowledge-based resources is significant at the level of 0.01 (β = 1.062, p = .000) and big data is significant at the level of 0.05 (β = -.523, p = .022). The beta of big data is negative and the beta of knowledge-based resources is positive, which means that the more positive knowledge-based resources is, the more negative the effect of big data on firm performance becomes. However, Hypothesis 3 proposed that knowledge-based resources enhance the positive relationship that big data has with firm performance. Therefore, Hypothesis 3 is not supported.
Discussion and Conclusion
In this research paper, three hypotheses were tested by examining the relationship between the use of big data and firm performance, and the interaction effect that absorptive capacity and knowledge-based resources have on this relationship.
Firstly, the main contribution of this research paper is that enough evidence was found to support the positive effect that big data has on firm performance. Using big data results in better firm performance probably because they endeavor to turn their data into knowledge. This enables them to better understand and predict its environment and act on it. These firms are more able to make predictions and decisions based on evidence, which results in achieving better performances. Accordingly, this findings supports data driven decision making theory in that decision making based on data improve performance. Secondly, having more knowledge-based resources attenuates the effect of big data on firm performance. There was enough evidence to test whether the knowledge-based resources enhance the positive relationship that big data has with firm performance. However, the opposite of what was hypothesized was true: the knowledge-based resources attenuate the positivity of this relationship. The most likely reason is that knowledge-based resources influence the firm performance more strongly than big data does. Put this way, the effect of big data is probably not as direct as that of knowledge-based resources. Knowledge-based resources could be the outcome of big data and therefore more a mediator than a moderator in their relation with firm performance. Thirdly, there was not enough evidence to support absorptive capacity as an influencer of the relationship between big data and firm performance. Firms that use big data to a large extent have a higher absorptive capacity. However, absorptive capacity does not improve the prediction of firm performance.
Theoretical implications
paper contributes by providing evidence-based results that big data has a positive effect on firm performance. Second, this research paper tested the moderating effects of absorptive capacity and knowledge-based resources on the effect of big data on firm performance. This paper found that only knowledge-based resources moderate this relationship: the knowledge-based resources attenuate the positivity of the relationship between big data and firm performance. These findings help to improve the existing understanding of this relationship. Besides, further research on knowledge-based resources as a mediator would provide new and better insights in explaining the role of big data with regards to knowledge-based resources and firm performance since the effect of big data becomes more indirect when the knowledge-based resources are added to the regression model. Third, this research paper contributes by having created a big data scale. Since there is yet little empirical research done on the effect of big data, no supported measures have been developed. This measure provides a basis on which further research can develop an improved measure.
Managerial implications
From a practical perspective, several implications for managers of firms arise from the research results, which are discussed here. Firstly, the result that big data positively effects firm performance confirms that firms have to adopt big data and do exploit it. However, becoming a big data enabled organization is very challenging (McAfee & Brynjolfsson, 2012) and apparently not all firms are successful at dealing with big data. But the firms that take on the challenge to derive value from big data are most likely to have considerable benefits. Second, this research paper argues that the effect of big data is more indirect and that improved knowledge-based resources are the outcome of big data. Therefore, managers have to focus on turning their data into knowledge. Hereby, aligning the firm’s data with the strategy of the firm most likely will improve the knowledge-based resources, which are the most critical for firm performance.
Limitations
concerning management, is more appropriate since these positions are more concerned with strategic issues regarding big data and firm performance. Finally, this study did not identify sectors in order to carefully draw a sample. According to Newbert (2008), it is important to collect data from a carefully drawn sample for a resource-based empirical study. An appropriate sample for a big data study would include firms from healthcare, public sector, retail, manufacturing, and personal location data. According to Sagiroglu and Sinanc (2013), the potential of big data can be identified in these five main sectors: healthcare, public sector, retail, manufacturing, and personal location data.
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