In the contemporary world, the digitization of the business processes, models, and products have shaped the nature of doing business. However, businesses are struggling with the presence of a big amount of datasets within their business models. There has been increased the use of big data analytics into the business for streamlining its processes and analysis of such big datasets within seconds. Numerous businesses nowadays are using the same technology. Therefore, this research is directed to recognize and define the significance of introducing big data expertise in the business for achieving numerous benefits. The research problem is stated as follows: Big data technology must be employed by corporations in order to bring competitiveness to the firm in the industry
Moreover, the research questions are illustrated as follows:
What is the accurate meaning of big data analytics?
What are the potential advantages of implementing big data technology?
How big data technology will contribute to the attainment of competitive advantage?
Numerous studies have been piloted for defining the relevance of big data in the organizational competitive advantage by several social scientists. This literature review discusses the meaning of big data, its advantages in business, and how it helps business to gain competitive advantage. Further, this literature review helps in studying diverse academics' research on identical studies.
Big data is the term that indicates large, complex, and fast data that is further difficult to process using the customary methods. Due to its complex nature, it needs robust technologies and more progressive algorithms to bring efficiency in the operations. According to the study conducted by Oussous et al (2018), there are three V’s of the big data as its characteristics namely velocity, variety, and volume.
Velocity: Since the data are produced in a prompting manner and thus it needs to be processed quickly in order to extract the relevant insights and information. Moreover, organizations like Facebook, Wal-Mart, generate an enormous amount of data within each hour from a wide range of sources.
Volume: There are numerous sources from which organizations gather an enormous amount of data namely social media, industrial apparatus, and other business dealings. This amount of data has been increasing day by day with the modern age of digitization. Big data analytics have the capability to handle such large volumes.
Variety: It means that the big data collected from various sources belong to numerous types or formats like structured, unstructured data. It may be public or private, complete or incomplete. Big data analytics can be the best solution to analyze such complex data.
The study by Tabesh, Mousavidin, and Hasani (2019) suggested that the major aim of big data analytics is to improve the decision making and decision implementation process of the organization. It is a well-known fact that informed decision making is an essential building block of the success of the business. In order to make essential decisions, management gathers data, produce numerous alternate strategies, and prudently assess these approaches and their consequences before making the ending decisions. In addition, these resolutions will then be realized, and the appreciated outcomes will be appraised to produce the supplementary information that is driven back into the following decision-making stages. There are four stages of analysis of big data that is as follows:
Stage 1: Initially, the unstructured data collected from the sources is processed by the advanced analytics techniques and algorithms for producing insights. Afterward, the insights are interpreted by the decision-makers.
Stage 2: In this phase, the insights are converted to the decisions by managers who contextualize the intuitions produced from their data examination and assign connotation to them.
Stage 3: In this, the decisions made are then converted into definite operational movements.
Stage 4: The outcomes are then generated from the conversion of decisions into actions that are helpful for future decision making.
According to the study by Kubina, Varmus, and Kubinova (2015), the companies collect the data regarding their customer from a variety of sources and then store for future business dealing. Usually, these sources are the gem for the companies as they can offer a glimpse into the client's minds. There is a need for an organization to implement novel processes, mechanisms, and technology like big data analytics. Their study suggested that there are five certain ways by which companies can create value from the implementation of big data. It can produce transparency by being more widely available to the novel potential. Moreover, it enables the companies to produce a more comprehensive segmentation of clients to tailor actions and make explicit services.
Furthermore, it suggested that big data can support the decision making of the organization by analyzing certain risks associated with the processes. In addition, the business report by McKinsey &Company recommended that the businesses that invest in big data technology are more affluent than others. It means that the organizations having big data analytics software into their business are practiced to achieve a competitive advantage. Their report documented the definite benefits of engaging big data analytics in business. Big data technology has permitted businesses to integrate common data fundamentals with judiciously minor struggles in a slight time period. Further, Barham (2017) stated that benefits that big data technology can bring to the company for achieving a competitive advantage in a sustained manner.
The benefits are in the form of more efficient production processes, fraud detection, better monitoring of products, the right decisions for the business, better risk management, and more. Big data can predict about the future scenario regarding the market demands, past trends of prices, present rates of production, and other aspects. This further helps the company to be prepared for any risky situation as per the prediction of data. The study by Prescott (2016) inferred that the businesses who desire to achieve the competitive advantage and value for the implementation of the big data should have to realize that just gathering data and setting policy centered on that data will perhaps not lead to the advantage they desired to achieve.
However, a sophisticated approach is required to do this happen. His paper also suggested that the main motive of the big data analytics is to understand the behavior of the numbers, not only just understand the numbers by performing regression analysis methodology. Moreover, it is the actual challenge faced by big data for affecting change and attains a competitive advantage.
Alsghaier et al (2017) stated the prominence of big data into the business. It showed the relevance of the Hadoop software of IBM as the big data analytics that analyzes the data in the small-time period. It suggested that big data analytics techniques improve the competences and the operational efficiency of producing abundant incomes in the business. However, despite numerous benefits of the implementation of Hadoop, their study showed the downsides of this technique. They inferred that numerous businesses failed to get big data analytics due to a lack of availability of infrastructure for its implementation and many companies considered the privacy issues for not implementing the same. Furthermore, Alam et al (2014) indicated the role of big data in the business.
They demonstrated the goals of the big data for business namely cost reduction, time reduction, support in internal business decisions, and evolving novel big data-based offerings. The chief notion of big data analytics is to aid in the internal decisions of the company regarding the type of product, inventory requirement, and costs of the product. Their study suggested that LinkedIn has used big data analytics for a product offering like jobs of interest, people to know, and many other aspects of social media. Furthermore, the research by Erraissi, Belangour & Tragha (2017) directed a study on the significance of big data analytics for companies. They specified that contemporary big data technology has altered the way of performing operations in the business.
The study showed the welfares of performing big data analytics on the competence of business procedures. Businesses make aims for their business such as time and cost-effectiveness that can be simply achieved by realizing the big data analytics systems. Big data aid the business to attain the aim of cost reduction as software like Hadoop have an enormous amount of data that the ancient relational databases were not capable to store in a safe manner. It assists the business in calculating and exploring the datasets in a very small duration of time hence; it is time effective technology for the business organizations. In addition, it helps in essential business decisions in respect of the services and products to be offered the customers, the costs, and the number of products appropriate for the company.
Moreover, big data analytics has the potential to bring economic advantages to businesses. They suggested that the demand for big data in recent years have been arising due to the advent of artificial intelligence, cloud computing, machine learning, prediction analytics, quantum, Hadoop, and more. Today, all the companies either small or large are trying to implement these analytics due to the increasing need for this software as more and more firms are relying on large datasets. In every industry, big data is being demanded like banking & insurance; retail; manufacturing, media & entertainment; oil & gas; and more. The study by Srivastava and Gopalkrishnan (2015) showed the relevance of big data analytics in the finance business.
There are numerous aspects that are considered in this paper to study big data analytics in the banking sector. These aspects are security & fraud management; customer segmentation & profiling; channel usages; spending patterns of customers; sentiment & feedback analysis, and cross-selling of products on the base of profiling. Their study inferred that big data analytics have helped the banking organizations to provide the improved services of the customers and maintaining the privacy and security of customers. Moreover, the study by Wang, Kung, and Byrd (2018) suggested that big data is a really essential technology in the contemporary competitive business environment.
Their study defined big data as the enormous datasets and as the group of processing methods and attentiveness required to understand these huge datasets. Additionally, their study recommended that big data technology helps businesses to assess the data and incorporate them to classify the novel prospects. This technology will bring competence in business operations, more income and profits, and advanced customer satisfaction. This study presented the prominence of big data analytics into healthcare as a huge amount of data is accessible by healthcare. Hence, healthcare companies must incorporate big data analytics in order to assess the data. It recommended that the digitization of all methodical tests and corrective records in healthcare organizations has become a broadly recognized practice in this competitive world. In addition, this study stated the role of IoT (internet of things) in the healthcare sector.
The big data created by using this IoT technology is helpful for better inspection and predictions in healthcare. Furthermore, this data aids in staff health nurturing, representing the extent of illness, and determining ways to cover a definite disease outbreak. Liu et al (2016) suggested the applications of using big data analytics into the business. In the retail sector, big data analytics can be useful in monitoring and obtaining consumer behavior insights that enable companies to improve their performance and thereby reducing the costs by taking strategic decisions. These benefits enable companies like Amazon, Facebook, Google to invest in big data analytics in order to preserve their competitive position in the market. Furthermore, it suggested that this will be useful for analyzing the huge data produced by social networks like Twitter, Facebook, Weibo, and more.
Therefore, firms can assess the big data to learn about the associations underlying social networks that describe the communal behavior of persons and groups by using advanced analytics. The study by Balachandran and Prasad (2017) established that a business can gain numerous benefits by employing big data analytics in the cloud computing. These benefits are data & information over the net; resource pooling; rapid elasticity; cost-effective; and more. Their study suggested that big data along with cloud computing can increase the predictability of the information and also it helps the analysts to discover novel interactive data like websites visited or place on a frequent basis.
The massive knowledge specified by the big data analysis aids in understanding the needs of the customers in an improved manner. This further helps in developing better and innovative products that lead to improving the performance of the firm. Their paper demonstrated the applications of big data analytics as automation, faster decision making, and in-depth insights. Frizzo-Barker et al (2016) suggested that there are frequent projections for growth and development for all the businesses that are reliant on huge datasets with the arrival of big data analytics. The business managers have to highlight on the occupations related to products, clients, and movements for increasing the chief accountabilities and classify novel predictions for novel growth. Moreover, these technologies affect the resolutions of the business organization. They must have correct data at the exact time and in the precise procedure so that decisions can be formed on statistics.
Alam, J.R., Sajid, A., Talib, R., and Niaz, M. 2014. A review of the role of big data in business. International Journal of Computer Science and Mobile Computing, 3(4), pp.446-453.
Alsghaier, H., Akour, M., Shehabat, I. and Aldiabat, S. 2017. The importance of Big Data Analytics in business: A Case study. American Journal of Software Engineering and Applications, 6(4), pp.111-115.
Balachandran, B.M., and Prasad, S.2017. Challenges and benefits of deploying big data analytics in the cloud for business intelligence. Procedia Computer Science, 112, pp.1112-1122.
Barham, H. 2017, July. Achieving competitive advantage through Big Data: a literature review. In the 2017 Portland international conference on management of engineering and technology (PICMET) (pp. 1-7). IEEE.
Erraissi, A., Belangour, A., & Tragha, A. 2017. Digging into Hadoop-based big data architectures. International Journal of Computer Science Issues (IJCSI), 14(6), 52-59.
Frizzo-Barker, J., Chow-White, P.A., Mozafari, M., and Ha, D. 2016. An empirical study of the rise of big data in business scholarship. International Journal of Information Management, 36(3), pp.403-413.
Kubina, M., Varmus, M., and Kubinova, I.2015. Use of big data for the competitive advantage of the company. Procedia Economics and Finance, 26, pp.561-565.
Liu, O., Chong, W.K., Man, K.L., and Chan, C.O., 2016. The Application of Big Data Analytics in Business World. In Proceedings of the International MultiConference of Engineers and Computer Scientists (Vol. 2).
Oussous, A., Benjelloun, F.Z., Lahcen, A.A., and Belfkih, S. 2018. Big Data technologies: A survey. Journal of King Saud University-Computer and Information Sciences, 30(4), pp.431-448.
Prescott, M.E.2016. Big data: Innovation and competitive advantage in an information media analytics company. Journal of Innovation Management, 4(1), pp.92-113.
Srivastava, U., and Gopalkrishnan, S. 2015. Impact of big data analytics on the banking sector: Learning for Indian banks. Procedia Computer Science, 50, pp.643-652.
Tabesh, P., Mousavidin, E., and Hasani, S., 2019. Implementing big data strategies: A managerial perspective. Business Horizons, 62(3), pp.347-358.
Wang, Y., Kung, L., and Byrd, T.A. 2018. Big data analytics: Understanding its capabilities and potential benefits for healthcare organizations. Technological Forecasting and Social Change, 126, pp.3-13.
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