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Big Data and Its Business Impacts

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2 Big Data and Its Business Impacts Introduction Business enterprises across the world have adopted the Big Data in understanding the customer preferences and targeted demographics. Big data aims at increasing the customer satisfaction thus increasing profits. The Big Data technologies help various business entities store large volumes of data such as Hadoop and Cloud–based analytics. Big data technologies play a critical role in enabling businesses analyze information thus improving the decision making process. Characteristics of Big Data As already mentioned earlier, Big Data is the large volumes of data that has been created by societies and tools. It is usually characterized by the four V’s; volume, velocity, veracity and variety. Volume is the amount of data that is vast which is associated with traditional data sources. Variety on the other hand is the information that comes from different sources that emanates from various technologies and societies. Velocity entails the speed within which data is being produced enormously fast at all times. Finally veracity indicates that Big Data is sourced from many dissimilar sources thus a need to test the quality of the data presented (Pugna, Duțescu, & Stănilă, 2019).

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Big Data Analytics Big data analytics refers to the developed and enhanced approaches that are applied to large amounts of data. Analytics that are meant for large volumes and samples of data influence business variations. Even so, larger sets of data have proved to be more difficult to manage. Refined analytics has significantly enhanced the processes of decision making thus the process of decision making is never rigid.

The impact of Big Data on business enterprises To begin with, it is important to note that big data technology that is used for managing and analyzing large volumes of data is accessible at lower costs. Therefore, organizations are utilizing Big data in various levels together with information technology thus guiding the way organizations operate. Big Data helps business organizations to make sound and informed decisions (Pugna, Duțescu, & Stănilă, 2019). Personalized customer experience Big data entails collecting data from different sources such as social media, call logs, browsing and many others. Collecting large amounts of data about the customers from different sources is

4 beneficial to the organizations at large. In this manner, the business owners can get a clear view of the customer needs, tastes and preferences. This in turn helps in designing the products and services to meet the exact needs of the customer thus leading to customer satisfaction. Therefore, Big Data plays a critical role in enabling the business managers to design their businesses starting from their marketing criteria to the customer service. In doing so, it enables various businesses offering similar services to gain a competitive advantage over other similar companies. Product Development, Testing, and Launch Product development This entails the establishment of the development plan and the physical development of the product. The managers should ensure that the product meets the required standards of the organization. Various Big Data is collected at this stage and tests both lab tests and in-house tests are conducted to get the tastes and preferences of the people concerning the new product. Here there are milestone check points which provide for the project control management. In this stage, there is an extensive and comprehensive in house testing, alpha tests and lab testing which leads to the development of the internally tested prototype of the new product. The stage emphasizes on the analysis of the market and the customer feedback that is given by the employees (Shen, Choi & Chan, 2019). The customer opinions and views on the nature of the product will lead to the development of the prototype product that meets the needs of the customers. The first prototype product will then be taken to the customers for assessment and consequent feedback issued. Spiral development which entails building a product, testing and sending it to the customers for feedback and making any necessary adjustments according to the feedback given

5 is also employed during the developmental stage. In addition, other operations such as making of detailed test plans, market launch plans, production facilities requirements and operations and production planning begin to develop. The financial implications and budget is also drawn to determine the expenses that the product development and launch is likely to cost the organization in question. At this stage, Big data impacts greatly on business entities as it determines the amount of data collected, the variety of data from different sources and the veracity as well as velocity within which data is processed Haddad, Ameen, Isaac, Alrajawy, Al-Shbami & Chakkaravarthy, 2020). Testing and validation In this stage, the determination of the progress and continued attractiveness of the product to the intended customers is reviewed. This firstly entails a review of the development work; it is also checked to ensure that the work meets the set standards of the organization. It is also checked to ascertain whether it is consistent with the initial outlined requirements. This gate also reviews the financial analysis based on a more extensive and accurate data provided. This stage entails testing and validating the viability and feasibility of the project and the product. The production process selected is also tested for viability and reliability as well as reviewing the reactions of the customers towards the prototype product developed in the previous stage (Pugna, Duțescu, & Stănilă, 2019). Various methods can be employed in the testing and validation process which includes; In-house product tests: this would entail conducting lab tests to check on the Quality, performance of the product. This would entail giving the prototype product to the employees within the same company to assess and give feedback on the product. Further, the use of field trials will help in the verification that the product performs the intended and actual use and also determine or predict the potential customers and the level of purchase intent by the target market.

6 Additionally, pre-test market would entail trying to gauge the reaction of the customers and measure the effectiveness of the intended launch plan as well as evaluate the financial obligations that would be required Haddad, Ameen, Isaac, Alrajawy, Al-Shbami & Chakkaravarthy, 2020). Launching stage This is the last gate the door open to commercialization. The product is now ready to be launched into the market and full production of the product officially commences. This stage involves the implementation of the marketing launch plan and the production operations plan. The managers should ensure that there is a detailed plan of these two crucial activities and should make the appropriate resources for the production process available (Shen, Choi, & Chan, 2019). In assessing the marketing plan, the managers should ensure that all the marketing strategies are exploited to ensure that the product is completely introduced to the market. The marketing strategies used will to a large extent depend on the nature of the product. During the post launch review, the product shall already have become a regular product and no more a new product in the market. The development team and the project team at large will be disbanded and financial reviews conducted to assess the progress of the product as well as the cost benefit analysis (Shen, Choi, & Chan, 2019). Finally, carrying out a post audit assessment will ascertain the strengths and weaknesses that might have been learned from the project which marks the end of the project. In summary, during this stage, actual production and marketing plan are incepted which will assist in checking for loopholes and areas that would need to be addressed prior to the actual implementation. Finally, during launching, the company should conduct a SWOT analysis that will help in checking for the challenges and opportunities as well as the threats that the product is

7 likely to face in the market (Haddad, Ameen, Isaac, Alrajawy, Al-Shbami & Chakkaravarthy, 2020). All these stages will depend on how fast the business is able to incorporate the use of big data in managing the product development, testing and launching stages of a new product in the market. Operational efficiency/time saving Time is a crucial factor in finishing a specific project of a business enterprise. Therefore, any organization whose processes are time conscious/saving is perceived to be efficient thus attracting more customers and clients. Big data analytics thus provides an adequate time that is used to check and assess any anomaly hence amending the entire framework of service delivery. In this way, Big data has been the best instrument in achieving the goals and vision of an organization. With Big Data, different companies have been able to optimize the supply chains by incorporating the use of sensors and smart labels (Ferraris, Mazzoleni, Devalle & Couturier, 2019). For instance, John Deere was seen to have placed sensors in each of the tractors which predicted whether a tractor was bound to fail or to succeed. In collecting large amounts of data is used to analyze and predict the direction of a business thus improving on the overall efficiency of a business. For instance, the Hadoop framework makes it faster to generate lots of information in a short period of time (Pugna, Duțescu, & Stănilă, 2019). Increased revenue and Cost reduction Big data analytics have played a crucial role in decreasing the expenses of business enterprises. In this way, it offers financial soundness to an organization that utilizes it. Any business enterprise that utilizes this kind of technology has turned things around increasing the business profits. On the other hand, an improvement in product or customer service experience

8 will automatically result in an increase in revenue thus reducing on costs (Pugna, Duțescu, & Stănilă, 2019). The organizations use the gathered information to build solutions that improve the acquisition, retention and the growth of a business. One solution that can be built from big data technology is advanced product or customer analytics that help in identifying the market gap, or the unmet customer needs. This prompts the development and launching of new products to the market that will meet the exact needs of the customers (Ferraris, Mazzoleni, Devalle & Couturier, 2019). Enhanced Accuracy Big data has brought about increased accuracy in the analysis of information. One such organization that utilizes the big data technology is the IBM. The organization has since exhibited outstanding performance and growth as compared to other organizations that do not use it. Every business is required to present clear and accurate information to its clients failure to which it may lead to loss of clientele base (Ardito, Scuotto., Del Giudice, & Petruzzelli, 2019). Therefore big data has impacted businesses in that it has enhanced the accuracy of the information generated from the variety of sources as well as its analysis. Competitive advantage Companies that have incorporated big data technologies have presented an increased demand of goods and services by various clients and customers. This is mostly due to the efficiency in how they offer their services. On the other hand, these companies are able to meet the exact needs of the customers thus resulting into customer satisfaction and a consequent customer retention. Companies are thus able to gain a competitive advantage over other

9 companies’ offering similar services and products. The companies therefore enjoy increased revenue and profits (Ardito, Scuotto., Del Giudice, & Petruzzelli, 2019). Enhanced decision making Decision making is a critical process in every company. Every company’s success majorly depends on how well they are able to make critical decisions in matters important to the company. Big data has generally improved the process of decision making Customer intelligence and customer analytics In the past, marketing was traditional mass marketing. Personalization and customer experience was never the focus in marketing. The introduction of big data technology has resulted into a paradigm shift in customer intelligence and analytics. There are no longer generalizations and assumptions about the interests, desires, tastes and preferences as well as the needs of the customers. Instead, Big Data has enabled different organizations to understand the exact needs of the customers (Ferraris, Mazzoleni, Devalle & Couturier, 2019). This is important as the customers are satisfied with the services offered hence leading to customer retention. On the other hand, having identified the customer needs, in the event of product development, the products will be developed to meet the exact needs of the customers. Challenges of Big Data For one to increase a comprehension of huge information, there are different issues from a few measurements that must be tended to. For example, related protection approaches, security concerns, licensed innovation and even the risk of data are significant viewpoints that must be tended to in a conversation about the effect of huge information. Accordingly, business

10 associations should actualize the suitable abilities and innovation along with strengthening work process plots nearby motivating forces to improve the utilization of huge information. By and large, access to data is a critical idea, and consequently associations should as often as possible coordinate data from different wellsprings of information, as a rule from outsiders or different areas. Besides, there have been expanding questions with respect to how to store and examine data of bigger sizes, assortment, and speed despite the fact that the contemporary examination doesn't give a response to that (Abbasi, Sarker and Chiang, 2016).

The greatest test for executing huge information examination is to part supportive data from gatherings. The data required for examination is a mix of both sifted through and messy data which is hard to get a handle on. Notwithstanding this is the inadequacy of fit staff who have the secret sauce to look good out of enormous data. From enlistment to getting ready and from intending to planning, using tremendous data examination goes with indistinguishable number of troubles from likely results (Abbasi, Sarker and Chiang, 2016). Then again, another issue of enormous information has been demonstrated not exclusively to be sheer information volume yet in addition the possibility of the information types that associations must arrangement with is dynamic. In this manner, the methods of information stockpiling have changed throughout the years to oblige the fast changes in information. The idea of information stockpiling can be followed to the presentation of information stockrooms, information shops, information solid shapes which at that point changed into information the board, information organization and different practices which remember for memory lists. Be that as it may, the providers of databases are as yet battling to adapt to enormous volumes of data, additionally, the rise of

11 enthusiasm for huge information to the interest for capacity and the executives of huge volumes of data (Abbasi, Sarker and Chiang, 2016). Conclusion The abuse of Big Data Analytics in industrialization philosophy can propel the nimbleness and industrialization execution. The communicate toward colossal data assessment shore the exhibition pointers which empower pioneers to use additional data in considering various exercises while trying the hierarchical destinations when organizations use large information examination, they can best envision formally strange things and upgrade the procedure execution. For the most part, business associations recognize operational systems benefits by cost decline, best operational arrangement, lower levels of stock, best various leveled work drive and crash wasteful resources. Moreover, they sway improvements in exercises profitability. The enormous information examination ability of an association (like data resourcing, getting to, planning, and passing on) and various leveled parts (like gigantic data examination method) could quicken of capable misuse of huge information investigation in their tasks.

12 References Ardito, L., Scuotto, V., Del Giudice, M., & Petruzzelli, A. M. (2019). A bibliometric analysis of research on Big Data analytics for business and management. Management Decision.

Ferraris, A., Mazzoleni, A., Devalle, A., & Couturier, J. (2019). Big data analytics capabilities and knowledge management: impact on firm performance. Management Decision.

Gnizy, I. (2019). Big data and its strategic path to value in international firms. International Marketing Review.

Haddad, A., Ameen, A., Isaac, O., Alrajawy, I., Al-Shbami, A., & Chakkaravarthy, D. M. (2020). The Impact of Technology Readiness on the Big Data Adoption Among UAE Organisations. In Data Management, Analytics and Innovation (pp. 249-264). Springer, Singapore. Pugna, I. B., Duțescu, A., & Stănilă, O. G. (2019). Corporate attitudes towards Big Data and its impact on performance management: A qualitative study. Sustainability, 11(3), 684.

Shen, B., Choi, T. M., & Chan, H. L. (2019). Selling green first or not? A Bayesian analysis with service levels and environmental impact considerations in the Big Data Era. Technological Forecasting and Social Change, 144, 412-420. Abbasi, A., Sarker, S., & Chiang, R. H. (2016). Big data research in information systems: Toward an inclusive research agenda. Journal of the Association for Information Systems, 17(2).

13 Fan, S., Lau, R. Y., & Zhao, J. L. (2015). Demystifying big data analytics for business intelligence through the lens of marketing mix. Big Data Research, 2(1), 28-32....