“I have just bought a house! I have bought a big house!” When people talk about big objects, generally there is a common sense of the word “BIG”. When people use the word “big house”, they are usually talking about the house area or the number of bedrooms. But, what does it mean when we use the word “big data”, and what differentiates “big data” from the usual usage of the term “data”?

Big data is a developing phenomenon in the field of Information Technology (Vera-Baquero et al., 2015). Big data includes data sets that can’t be analyzed by the common traditional data analysis tools (Costello & Prohaska, 2013). Big data refers to a high volume of data with a high velocity and a high variety; these properties require more efficient methods than the current ones used in conventional database systems for decision making (Laney, 2001a). Big data enables systems to manage their processes using a large volume of real-world data (Van der Aalst, 2012).

Big data entered the field of practical research in the 21^st century; there was no noteworthy research applying big data analytics in other fields before 2000. The main characteristic of big data is simply its huge volume of data, but some other characteristics have been added to this definition over the years. The first time that Big Data was defined by the 3V model (Volume, Velocity, and Variety) was in a study by Laney (2001b). Volume refers to the amount of available data; Velocity refers to the timeliness of the data; and Variety refers to the diversity of the data types, including unstructured, semi-structured, and structured data sets.

Two other important Vs have been added to the definition of big data in the most recent decade. The economic Value refers to the profit gained by analyzing a huge volume of data (Idc-Vesset et al., 2012), and Veracity refers to the considerable amount of uncertainty and imprecision in the big data (Schroeck et al., 2012). (Wamba et al., 2015) integrated all of the Vs in one place and introduced the 5V big data framework for the first time. Figure 1 represents the evolutionary timeline of the big data concept, as well as the most-cited articles using big data in manufacturing, logistics, and supply chain management.

Figure 1
Big data framework evolution during the time (Idc-Vesset et al., 2012; Laney, 2001b; Lee et al., 2013; Mashey, 1997; Schroeck et al., 2012; Waller & Fawcett, 2013; Wamba et al., 2015; Wang et al., 2016).

Big data analysis is a process that transforms terabytes of low-value data into a small amount of high-value data, which shows an overview of the company using just a small slice of the overall picture (Fisher et al., 2012). A big data system can be separated into four consecutive phases: data generation, data acquisition, data storage, and data analytics (Hu et al., 2014).

Because of recent technology developments, obtaining data is not a difficult task anymore, though the efficient use of data to achieve strategic and operational goals is still an area of concern (Gobble, 2013). Traditionally, businesses used their own data to make decisions, but the development of new technologies gives businesses access to various brand-new types of datasets (Jha et al., 2016). The usage of social networking is booming at a quick pace, and a huge volume of consumer data is being provided to businesses. Big data has become a major keyword in the technology world and has shown its useful applications in other areas as well. For example, big data has been successfully used for fraud prevention and detection in financial transactions (Jha et al., 2016).

Data plays a vital role in developing today’s operational systems. Big data can be used to increase business competitiveness, according to the recent development of data. Today’s business environment provides a huge opportunity, since a large volume of data is generated every minute. Most companies use big data for continual improvement. Four steps are commonly used in data analytics: The first step is to ensure that the available data is clean, structured and organized, which can then be used for further analysis. The second step is to ensure that the right data is accessible in the right form, the right time, and the right place. The third step is to do quantitative analyses, such as descriptive analytics. The fourth step is to apply advanced analytics such as predictive analytics, automated algorithms, and real-time data analysis. Using big data in the last step requires particular expertise in advanced data analytics (Sanders, 2016).

Various techniques such as statistics, data mining, machine learning, neural networks, pattern recognition, visualization, etc. are used to extract any valuable information out of big data (Mikavicaa et al., 2015). For example, cloud computing is one of the practices used to store, develop, and deploy big data in business processes.

Decreasing data management costs can increase the desirability of companies to use big data (Schwab et al., 2011). For example, in 2019, storing a terabyte of data using relational traditional databases could cost over $20000 for a company (Sonra, 2015), but storing the same amount of data could cost just $1000-$2000 using cheap big data technology such as a Hadoop cluster (StatSlice, 2013). Hadoop gained popularity in the area of technology development because of its price and capacity for data storage.

There have been many developments in big data collection and analysis methods in recent years. The author of this paper used the Thomson Reuters Web of Science search tool (Clarivate, 2020) to track the number of “big data” publications over the last decade. Since several studies were performed on the topic of big data, we narrowed our search to the articles that have “big data” in their title. We also tried different combinations of title and topic searches in the Web of Science and came up with the solution that the best combination was to keep “big data” in the title search and the other keywords in the topic. For example, a search for articles with both “big data” and “supply chain” as a topic for the year 2019 gave us 173 articles; several of them were not related to the focus of our study at all. On the other hand, a search for articles with both “big data” and “supply chain” in the title for the year 2019 gave us only 15 articles, with many valuable articles not included. However, a search for articles with “big data” in the title and “supply chain” as a topic for the same year resulted in 54 articles that covered topics related to our study. Our methodology here was the same as that in many other review papers (Mishra et al., 2018; Nguyen et al., 2018) to collect the literature, provide the descriptive analysis (in this section), develop categories of interest, and evaluate the papers (sections 2, 3, and 4).

Figure 2 illustrates the number of published articles from 2010 to 2019 with the word “big data” in their title and “manufacturing”, “supply chain”, and “business” in the topic. As can be seen in Figure 2, there is an increase in the number of publications in the field in recent years. It is worth nothing that not much research was published on big data before 2010. About 15% of the big data publications have the word “business” in their topic, and many of publications were dedicated to other fields of study, such as engineering and science. Among the business-related studies, about 46% of publications focused on “supply chain” and “logistics” in their topic, and about 12% have manufacturing as their topic.

Figure 2
Number of publications with “Big Data” in title (Thomson Reuters Web of Science).

Different academic journals cover different subject areas; therefore, journals that publish most of the papers in an area of research can be a useful guide for those who are looking for the available literature or submitting their own contribution. The Thomson Reuters Web of Science was also used to develop a bibliometric of big data publications containing the terms “supply chain”, “logistics”, and “manufacturing” when looking at the journal title and impact factor, as can be seen in Figure 3.

Figure 3
Publishing journal (impact factor in parentheses) with a topic of a) Manufacturing or b) Supply Chain and logistics.

Figure 3
Publishing journal (impact factor in parentheses) with a topic of a) Manufacturing or b) Supply Chain and logistics. Cont.

Figure 3 shows that “Annals of Operations Research”, “Sustainability”, “Technological Forecasting and Social Change” and “The International Journal of Logistics Management” are the journals which published the most papers that applied big data analytics in supply chain management. The greatest impact factor among these journals is for the “Journal of Cleaner Production,” with an impact factor of 6.395.

Information technology developments encouraged many researchers and practitioners to use big data analytics in manufacturing systems, logistics processes, and other functions of a supply chain (Akter & Wamba, 2019; Dubey et al., 2018a; Feng & Shanthikumar, 2018). Moreover, since the benefits of big data analytics have attracted more researchers into the field, several more papers have been written that review the literature and define possible future directions (Barbosa et al., 2018; Dubey et al., 2019; Gupta et al., 2019a; Lamba & Singh, 2017; Rialti et al., 2019). According to Figure 2, most research in this area has been performed in recent years; therefore, a current literature review can give more insight for researchers who want to focus their work on big data applications in supply chain management. Moreover, our study categorizes the literature into three key areas: manufacturing systems, logistics processes, and supply chain functions. In each category, papers are grouped based on their main topic, and quality papers are summarized in tables to give more information about each paper. The current study is trying to answer the following questions:

• What are the different categories of big data analytics that are used in supply chain management?

• What are the factors that affect the attractiveness of using big data analytics in supply chain management studies?

• What supply chain management research topics are studied more often by big data analytics?

• What are the hurdles and advantages of using big data analytics in supply chain management research? What must be done in the future?

The remainder of this article is dedicated to reviewing published research in the specialized fields. Section 2 surveys published research that looks at applying big data analytics to manufacturing systems. Sections 3 and 4 review the published research with a focus on using big data analytics in supply chain management and logistics processes. In each subsection the subjects most often studied are introduced, and some of the quality papers are summarized in relevant tables. Section 5 concludes this study by discussing the hurdles and advantages of using big data analytics. Moreover, some directions for the future studies are hypothesized in Section 5.

A number of studies show that big data analytics can improve the entire operational performance in manufacturing systems. (Yadegaridehkordi et al., 2018) developed a hybrid approach to study the effect of the adoption of big data analytics on manufacturing companies’ performance. (Popovič et al., 2018) showed that big data analytics’ capability, along with organizational readiness and certain design factors, could enhance a business’s performance. In another study, Guo et al. (2017) applied data visualization and machine learning algorithms to better inform the operations manager of the product’s market situation. Some other applications of big data analytics in manufacturing systems are shown by implementing big data analytics in a manufacturing company (Dutta & Bose, 2015) and using big data to improve the trading performance of emitting companies (Liu et al., 2017).

Big data can provide useful tools for manufacturers to perform their operations in a sustainable manner, keeping the environment better for future generations. Xu et al. (2019) showed how using the available big data on used products can increase the efficiency of remanufacturing systems and save more resources. Dubey et al. (2016) performed a field study and used the responses by 405 senior managers to develop a framework that could use big data to determine the most important factors for maintaining a sustainable manufacturing system. Lowering service costs, increasing the level of trust between stakeholders, respecting customers’ privacy, and increasing data-sharing security are among the benefits that big data analytics may bring to sustainable manufacturing systems (Rehman et al., 2016). In another study, (Huang et al., 2019) developed a theoretical approach to demonstrate the application of big data analytics in the area of production safety management.

The application of big data analytics in Bosch Car Multimedia’s (Braga-Portugal) organization (Santos et al., 2017) reviews the challenges of collecting, integrating, storing and processing the data in a manufacturing environment. The Bosch organization study shows the potential opportunity that is created when the volume, variety, and velocity of data is used for sustainable innovations in a future manufacturing environment. In another article, the importance of risk management in developing sustainable manufacturing supply chains was studied (Mani et al., 2017). The paper showed that applying big data analytics in order to mitigate the supply chain’s social risk can help improve social and economic sustainability.

Big data analytics can be used in smart manufacturing to solve company problems at the speed the business requires. However, there are some organizational and technological barriers that may prevent manufacturing companies from using big data solutions to initiate a smart factory (Li et al., 2019). Big data analytics has been proven to be a valuable tool for manufacturers to help them develop strategies, share data, design predictive models, and connect factories in order to control processes (Kusiak, 2017). A study by Bumblauskas et al. (2017a) studies big data applied to designing a smart maintenance decision support system, which is shown to improve an asset’s lifecycle. Liu et al. (2019) used big data analytics for routing order pickup and delivery as well as assigning orders to laundry terminals in smart laundry service enterprises. Big data applications in strategy development and agile manufacturing have also been studied by Opresnik & Taisch (2015), Waller & Fawcett (2013), Guha & Kumar (2018), and Gunasekaran et al. (2018). Ren et al. (2019) reviewed the available research in big data applications that support sustainable smart manufacturing. Agility in a manufacturing system is the capability to better deal with unpredictable events, and deal with them in a business environment that can even turn these events into benefits (Swafford et al., 2008).

Several other studies developed quality research on using big data analytics in the manufacturing field. Some of the selected journal articles and conference proceedings are summarized in Table 1. The criterion for us consider a paper in the current review is that it must have been cited, on average, more than 10 times each year.

Table 1
High-quality articles using big data in manufacturing processes (Citation count is as of March 2020).

Management’s decision to use big data analytics in a company would be a strategic one. Management commitment positively affects the level of accepting big data analytics in a company (Gunasekaran et al., 2017). Using big data analytics lets the manager have access to analysis which based on dynamic data, and can make the supply chain more competitive (Chen et al., 2015). Applying big data analytics to planning, decision making, and supply chain coordination and control can improve the preparedness, alertness, and agility of the supply chains in question (Mandal, 2019). The aspect of value creation by big data has not been studied often in supply chain management literature. Therefore, Brinch (2018) studied the value discovery, creation, and capture that can be achieved using big data analytics in a business supply chain.

All benefits considered, there still is not enough empirical research that applies big data analytics to supply chain management, so there is a lack of ability to adopt an informed strategy just by comparing different methods (Kache & Seuring, 2017). Investing money in the required hardware and software to apply big data analytics may affect the strategies of a supply chain. As for training, our educational system can train a good number of data scientists, but may have ignored the managerial abilities of these data scientists in many cases (Carillo, 2017). As a consequence, converting the available data into applicable knowledge which can mitigate supply chain risks is still an obstacle for many supply chains (Bumblauskas et al., 2017b).

Dealing with varying numbers of suppliers, manufacturers, logistic providers, etc. creates big data sets that can be used for optimization projects in a supply chain. Big data analytics improves demand forecasts, reduces the safety stock, and improves a supplier’s management practices (Roßmann et al., 2018). It has been shown that big data predictive analytics can be combined with other methods such as enterprise resource planning to improve the performance of supply chains (Gupta et al., 2019c). Oncioiu et al. (2019) studied the role of big data analytics applications in improving Romanian supply chain companies’ performance and implementing assessment processes. Big data analytics has also been used by Boone et al. (2017) to improve the practices of service parts management.

In another study, Hofmann (2017) shows that the velocity of big data can be used to reduce the bullwhip effect (increasing the safety stock levels in upward echelons) in supply chains. Working with omni-channel supply chains generates a huge amount of data from different sources. Big data analytics can make more accurate sales predictions in different channels and develop optimal delivery plans to minimize transportation costs (Lee, 2017). In another application, sharing the data through a big data framework can reduce the uncertainty cost in a supply chain (Liu & Yi, 2016).

Another application of big data analytics in supply chains can be the optimization and adjustment of the operations based on sustainable objectives. Big data can improve the environmental, financial, and operational management of the supply chain in order to help combat climate change (Seles et al., 2018). Badiezadeh et al. (2018) developed a network data envelopment analysis with big data in order to help assess the performance of sustainable supply chain management. Open access to big data can facilitate innovation, create resilient supply chains, and improve the performance of the distribution network (Weerakkody et al., 2017). Liu (2019) demonstrates how applying big data analytics to the targeted advertising of products can reduce the carbon emissions in a supply chain.

It is worth noting that using big data analytics is not beneficial in all supply chains. There are several barriers that may interact together and prevent big data analytics from establishing a sustainable system (Shukla & Mattar, 2019). Cheng et al. (2018) considers a sustainable supply chain with a manufacturer and a retailer and shows that the proficiency in big data analytics depends on the service level adopted by the retailer. Available big data from transportation and logistic provider companies can be used to satisfy delivery requirements while also keeping in mind carbon emission constraints (Ji & Sun, 2017; Zhao et al., 2017). In another study, the application of Big data analytics in enabling the resilience of supply chains after disasters was studied by (Papadopoulos et al., 2017), where they used the example of Nepal to prove their analysis.

The lack of information in food supply chains can bring about huge costs in the forms of deterioration and waste. Accordingly, using the information excluded from big data analytics is gaining more attention from the decision makers in food supply chains. Big data analytics has applications in agricultural supply chain management, farm management, food sustainability assurance, consumer demand management, new product development, and food safety (Coble et al., 2018; Giagnocavo et al., 2017; Jagtap & Duong, 2019; Shukla & Tiwari, 2017; Wolfert et al., 2017). Ji & Tan (2017) considers five major benefits of using big data in food supply chain management: 1- data sharing over supply chain echelons; 2- doing experiments to find frauds and anomalies in the supply chain; 3- accurate clustering of customers in order to target the marketing of each cluster; 4- developing automated algorithms to support the decision-making processes; and 5- developing new products, services, and business models.

Big data analytics was used by (Liu, 2017) to develop new e-commerce methods for marketing fresh products with a short shelf life by keeping in mind the critical aspects of humidity and temperature. In another research paper, Mishra et al. (2017) used social media big data to determine factors that influence customers’ beef purchasing decisions. They believe that the available unstructured big data in social media can help businesses to design their supply chain to be more consumer centric. Big data applications have also been developed for cold chains (temperature-controlled supply chains). However, there is an important lack of understanding regarding what data in a cold chain should be collected, and what is the appropriate method to collect and analyze that data (Chaudhuri et al., 2016).

Any supply chain that is dealing with uncertainties in its decision-making processes is using risk management methods at some level. Risk is one of the consequences of a lack of information, and big data can be applied to reduce this lack of information. In the context of logistics processes, transportation risk can be defined as the deviation from the estimated delivery time. Big data analytics can be applied to predict these delivery time deviations, as well as prevent transportation risks such as missing cargo flights (Shang et al., 2017). Engelseth & Wang (2018) used big data analytics to manage the risks in long-linked supply chains. They used an analytical framework to mitigate the risks of a case study that looked at machine parts imported from China to Norway.

The establishment of big data analytics could resolve bargain issues between a supplier and a retailer. Tsao (2017) used big data analytics and game theory to show the way that a supplier and a retailer could determine the period in which to use their credit in order to minimize their risk of defaulting. A type of common risk is that of hazardous materials and waste in closed supply chains (supply chains with remanufacturers and recyclers) (Deleris et al., 2004; Van Asselt et al., 2017). Big data analytics has also proven to be useful in recognizing powerful demand signals and minimizing the negative environmental impacts of remanufacturing (Niu & Zou, 2017; Wu et al., 2017).

Big data analytics can help inform marketing managers of current trends in product sales beyond simply the demand forecasts. For example, product reviews can be approved more easily to help influence sales performance in many studies (Li et al., 2016). The impact of big data analytics on improving sales forecasting was studied in an analytical review by Boone et al. (2019). Sagaert et al. (2018) shows that using big data analytics can improve the transparency of market dynamics to sales managers. Using big data analytics in the case study of a tire company could improve forecasting accuracy by 16.1% over the traditional method (Sagaert et al., 2018). Moreover, Li et al. (2018) showed that managing a demand chain with big data and electronic commerce works much better than traditional methods of supply chain management.

Using product-in-use data has been proven to reduce the uncertainty for aftermarket (spare parts) demand planning (Andersson & Jonsson, 2018). Gawankar et al. (2020) studied the impact of new technologies—such as the Internet of Things and big data analytics—on the retail environment in India. They found that the retailing industry in India is eager to use new technologies in the retailing environment that they call “Retail 4.0” in their study. Big data analytics was also used by (Liu & Yi, 2017) to show the correlation between the price and the products’ environment friendliness degree. It shows that the available data can used for targeted advertisements in a supply chain’s green environment. Another study in big data pricing application was done by (Liu, 2017), in which he considered the data company to be an echelon in the supply chain, and determined its benefits using the Stackelberg game.

Analyzing social media data can help supply chains increase their number of customers in the system through personalized services. Companies can analyze social networks, mobile, and web data to track the way that a customer wants to use the product (Agrahri et al., 2017). On the other hand, Aloysius et al. (2018) survey of a group of retail store customers showed that many people have concerns about how much of their personal information is collected, which can negatively affect the store’s image.

Some of the selected journal articles regarding big data applications in supply chain management are summarized in Table 2.

Table 2
High-quality articles using big data in supply chain management practices (Citation count is as of March 2020).

Despite of all the big data benefits for businesses, applying big data has not been accepted at the management level of many companies yet; this may be because the cost of using big data requires a high initial investment. Management support has a crucial role in the successful implementation of a big data analysis system. An initial cost-and-benefit assessment of big data in terms of how it will be utilized long-term is a very difficult task. It is not easy to determine applying big data analysis is beneficial for businesses that make fewer than a certain number of transactions every day. In some companies, such as Amazon, Walmart, Google, etc., traditional systems cannot be used to analyze the data because of how enormous the volume is. However, in some other companies, the V characteristics of big data are more questionable, and there is no rule of thumb to help tell the manager that the available data is suitable for establishing a big data analytics framework.

One of the problems with big data applications is knowing how, where, and by which means to collect useful data. Another issue has to do with inadvertently separating valuable information out of the available data. The analyst should know the information that he/she wants to exclude from the data; additionally, the available data should be able to answer the analyst’s questions. Yet another problem regards finding the methods by which one can provide the most accurate answer while still using a reasonable amount of time and financial cost. There is an increasing demand for employees who are qualified to analyze big data as companies respond to the rapid pace of technology developments. It is also a challenge to create trust between data analysts and the managers. Most systems originally resist change, so it is vital to have the higher-level managers’ support in order to use the results of data analysis to change a system.

A computers’ Central Processing Unit (CPU) could be an obstacle for big data analysis because of the underdeveloped capability of traditional computers to store and effectively process a big data set. On the other hand, not all of the available raw data is complete and consistent; therefore, effective cleaning and integration methods are required to make the dataset ready for analysis.

The 5^th “V” added to the definition of big data refers to the value that can be obtained from a big data analysis. Unfortunately, whenever value comes in, hacking can start to crop up as well. Information security can be a hurdle to applying big data analysis in companies. A huge data volume increases the probability of having confidential and valuable information in the system, and this may increase data vulnerability and the chance of cybercriminals (Kshetri, 2014).

Another issue can be selecting the appropriate type of decision-making data for most of a system. More explicitly, not all of the available data in a system is used for making each and every decision. These decisions are based on the knowledge and experience of the data analyst that determine the part of a dataset that should be used. Moreover, it is an unfortunate fact that the available big data may not necessarily be created by the target population. For example, there is a huge volume of information on Twitter, but not everyone in a community will have Twitter accounts. Thus, there is a part of community which creates a lot of data, while the other part is not involved in creating any of the available information in a dataset. This fact continuously emphasizes statistical uncertainties such as biased statistics.

Applying the results of a non-real-time data analysis can lead to a significant difference between the analysis for both the historical data and the real-time data; for example, the initial assumption of the forecasting analysis that “the future follows the past” would not be true. As an additional example, when the data shows the proficiency of a transportation path in terms of cost and time (many companies have access to this data), other companies may start to use this same path. By increasing the demand for the mentioned path, it may lose its attractiveness both in terms of cost and time.

Another old and common challenge is sharing data between the echelons of a supply chain, or even various departments of a plant. It is challenging to ensure that all the stakeholders who share data receive some benefits from this cooperation. Moreover, it is vital to have a supportive information technology department which provides both the hardware and software requirements for working with big data. Data analysts who can work with big data should be hired, or knowledgeable instructors should be easy to contact so that they can educate the big data analysts for the plant.

Big data analytics can help companies better understand their business needs. Many companies plan their business growth model according to a demand boom, which may be the result of business growth in general. However, they should consider that a change in market growth may leave them with several empty warehouses and idle manufacturing plants. Big data enables these firms to predict the market direction and plan development strategies based on this analytical information.

Using big data enables companies to simulate a digital model of an entire manufacturing process. Collected data from the customers can be used to improve the marketing and sales processes. Clustering the customers into various groups, providing service according to the needs of each group, and using customers’ data to target where/when to advertise these new products are the other applications of big data in marketing.

The importance of big data analytics in improving customer loyalty is not negligible. Most customers would be loyal to the company which provides them a high-quality service the first time. Companies can use big data analysis to predict customers’ needs and satisfy those needs in order to make them loyal customers.

Optimizing activities which are out of the normal boundaries of the company—such as supplier selection or technological adjustments—require a high level of information-sharing between the stakeholders in a supply chain. Sharing information has always been an obstacle in supply chains, but big data technological developments can help simplify and speed up this process (Swaminathan, 2012).

From a logistical point of view, big data can be applied in order to forecast delivery times or optimize delivery routing by using traffic, weather, and drivers’ information. Another possible application is to use the products’ information in making inventory management and sales decisions (Waller & Fawcett, 2013).

Both the literature and a review of companies’ experience in the area of using big data analytics in manufacturing shows that the number of applied case studies are more than the number of theoretical publications. It is likely that researchers will develop more novel applications for big data in manufacturing systems, such as developing methods that can obtain high-quality solutions using less time and money.

Big data has been widely used for predictive studies in the literature, but there are not many prediction error measurement studies in big data. More precisely, beyond simply the quality of the input data, the accuracy of big data analysis is significantly affected by the quality of the model used to analyze the data. We still have a way to go regarding developing measures which can determine the accuracy of a big data analysis method.

The current studies regarding big data applications in supply chain management are mostly theoretical and conceptual, and there is a noticeable shortage of studies of analytical models. Moreover, the existing analytical models mostly study big data applications in modeling sustainability. Therefore, there is still a gap in the application of big data regarding optimizing operations (such as logistics and procurement) in a supply chain.

There are some study directions in big data that can significantly improve the performance of logistics systems: 1- Developing an efficient collaboration among all the decision makers, transporters, retailers, and door-to-door delivery service providers; and 2- Applying cloud-based services in smart transportation systems and integrating them in an online planning framework in order to provide a connection between vehicles, traffic managers, and the final customers.

Finally, a good research topic to follow would be to find out if analyzing a sample of a big data set could give us the same quality results as the basic big data analytics. Design of experiment methods can be used for categorizing big data into different groups in order to save time regarding analysis.

*ighalehkhonda@ollusa.edu