Benefits of Big Data, Its Effect on Business - Case Study Example

Benefits of Big Data its effect on Business Student’s name Institution Affiliation Instructor’s name Course details Due date Introduction During the 90s the major focus of IT firms was to enabling and also recording information and transactions that were faster to the businesses so as to ensure increase in productivity in the business. However with the evolution and increase in the usage of the internet, the firms have turned their focus to ensuring that there is faster delivery of information in the IT documents that are used all sectors. The computers and IT equipment are being innovated to ensure that the information got is wide and up to date as required. This organizations are facing the challenge of finding better ways pf analyzing, monetizing, and also capitalization of the information that is being channeled and also integrated into various businesses (Mauro, 2016, pg. 125). This coming age is being touted as the age of Big Data The IT market place today, the idea of Big Data has a very great impact once it comes to the usage of large information being delivered to the firms. The shorthand usage of new technology and also the design that is done so that they achieve the economic value that is required to be obtained through the large volumes of data. High rates of capturing, analyzing and also discovery of information is the main focus. Those organizations that do impress technology and have gone ahead to implement technological solutions to most of their problems are said to make decisions that are real-time decisions through the big data hence this will make them gain a competitive hedge against those organizations that are unable to impress this technology. With the Big Data, businesses are said to take decisions that are more accurate and informed, which will make them to be more aggressive and consolidate easily in order to accommodate change (Richard, 2012 pg. 6). The emergency of Big Data for the case of IT firms is not all about the For IT organizations, it is not all about the deployment of new software technology or new application. This represents a new way of doing things in the IT industry. It brings about new administration skills, designing of the systems and also a new method of data management and other policies that required a new approach. Some of this organizations deploy. However, most of the organizations that have shown the tendency of directing their efforts towards starting small so as to utilize the existing resources. The growth of Big Data will require that the network and other infrastructure that plays an important role in the IT industry to change and be adopted (Richard, 2012 pg. 9). However, the expansion and the quick growth will require that they grow within the required systems and ensure that the growth is linked to the mission of the organization and also other critical transactions and content that is found within the environment. Body There has been increased competition in the marketplace and also innovation due to the anticipation of good results by Big Data. Therefore, those who are IT managers in the two organizations have the time and space of the approach that is meant to support Big Data analysis within their organizations. The scope and role of these projects is going to increase due to the inextricably that is seen within the business processes. Due to these, the network and internet will play a major role in determining the link that is going to be there between the investment that is going to be required and the Big Data projects hence ensure the required interconnectivity. With the anxiety, the business have always shown anxiety to get the benefits of the Big Data without experiencing too many changes within them. The changes that may be seen include; increase in the operation costs, the reduction of those investments that are similar, and also the introduction of management policies that maybe fault hence cause faults within the organization. Any of the above mentioned event may result to mixed results which may lead to poor performance. But when the organization puts the best practices, it results a well-organized datacenter design. To ensure that the above practices remain perfect within the organization then it is better that proactive approaches are employed so as to mitigate the unnecessary concerns that may rise. The IT firms have a well-structured planning process that is going to identify the various ways through which the optimal network will be chosen making the necessary configurations that are going to be supportive to the whole system (Brown & Chui, 2011 pg. 8). The development of Big Data analytic software will increasingly be deployed on a manner that is going to be massively parallel leveraging to the required system to ensure that its operation is in such a manner that is going to ensure that it has made a great impact in the organization. The platform through which this system is going to be implemented is going to be in a manner that is going to ensure that the routines within the organizations are not going to be disturbed for maximum optimization and also efficient operation within the organization (Boyd et al, 2011). With the fact that a large amount of data is being passed in the Big Data applications, then it means that the real-time delivery of services and also the speed at which the transactions are going to be done within the organization is going to increase. Due to this, the system may require a hyper scale server designs within it that is going to contain an increased number of nodes that containing more processors within the system and also an increased number of disks. All this is meant to support the network connection since it is meant to be so robust so as to ensure that the data movement is quick, smooth and efficient (Snijders, 2012, pg.3). Due to the traffic that is being experienced in the movement of data from one point to the other, then it is possible that the traffic patterns that are going to be seen are going to burst and vary due to the uncertainties that are experienced regarding the data movement over the network at any given time and also the amount of data being transported. Once a data load has been requested at a given point, then it becomes a must that the data will be distributed to the cluster through the network. However, the delay in the dispatch of data is going to be experienced to a certain extent unless the network through which it has been requested is going to be in play. Whereas consistent and predictable latency is significant, the Big Data applications are not sensitive to latency (Brown & Chui, 2011 pg. 20). The network efficiency is being improved by the use of the appropriate line rate of performance. To ensure that there is network efficiency once dealing with the applications of Big Data, then it is necessary that rightsizing of the switch capacity should be ensured. The installation of Bog Data requires that the configuration of the network so as to contain 1GbE access layer switch capacity may be installed in the current environment. As time moves on, and more data is transferred from one point to the other, then the organization will need to increase its efficiency/performance ratio by ensuring that the cost is contained. The capacity will be increased as the network will need to serve more. The switching system will be up scaled through aggregation to ensure that the required results are attained (Hilbert et al, 2011, pg. 62). Big Data optimization will be required in any organization. Also, network performance will be utilized well in both the new and the old infrastructure. The connection should be such that it ensures that the desired results are maximized hence leading to a better performance. When it comes to essential transactions within the system and also through the servers then we require a well foundation for the network. Also, the things to be considered in the organization is the storage system, whether a NAS, SAN, or DAS a well laid arrangement is required (Ibrahim et al, 2015, pg. 102). Governance and other regulatory considerations that maybe required are some of the key areas that affect the implementation and the network design within the organizations. Taking an example of research institutions that maybe having multiple projects, they may require that the projects will need to be separated. Also, another example is a financial company that may be in need to separate big data projects from other regular transactions that are oriented to the line of IT in business hence a network resource, may require to be treated separately. When logical partitioning is done that is meant to ensure separation is going to add cost and also increase complexity. Apart from the other tasks that may be required in isolation process in the network, the use of hard partitioning is going to be done on the Ethernet switch. This shows us that the tasks that are performed are completely separated at the plane level of the data (Mark & Albert, 2011 pg. 7). Taking into consideration, data plane separation is required to comply with privacy and regulation requirements that are associated to a given form of data application that maybe sensitive. Big Data projects may not necessarily start big they can start small and continue scaling up increasing the size of the project. Efficient network project will easy that management of data and free flow of data will be experienced. But with the increase of data then we will require that the machines will be improved so as to be able to accommodate the data that has increased in the process. Firms have always struggled to understand the advantages that Big Data presents by implementing its huge analytical capability and also its scope when it comes to multiple data sources within them. However, the firms should understand that the implementation of this applications in the firm will ensure that that the capabilities of the firm currently are enabled beyond what it can handle. This means that the move to Big Data can pose a number of challenges organizationally. When it comes to IT staffing Big Data require new deployments of staff who are conversant with the new systems and also new administrative skills are require. All this are required to ensure that new skills are developed and its learning is done conveniently in the firm (Steve, 2011 pg. 4). Finding staff who are conversant with Big Data applications may not be easy over a short period of time. When it comes to its application in the firm, they require a lot of privacy hence requiring structured and informal approaches when addressing concerning it. Big Data is not exceptional hence it requires security governance and privacy needs meant to make it effective. Due to the expectations that are required from Big Data, it is necessary to IT organizations to ensure that the network scale out deployment has been done to ensure efficiency. For it to be effective it should be ensured that the existing network topologies have been able to ensure that the networks are able to provide the seamless paths for expansion (Yuki, 2011 pg. 16). Large organizations are at a better position in the implementation of Big Data applications as they will be able to provide the necessary required service. and also make its usage economical and one that enables them to process their transactions faster than the current state. Conclusion Big Data is an opportunity for those big transactions that are done in businesses. They enable easy analysis of this data and make it easy for the organizations to make decisions that are sound and in the real time. Support is required in ensuring that the correct and required implementation has been done so as to ensure success of the project regarding Big Data. The level of the organization determines the rate at which Big Data applications are going to be implemented in the organization. The structure of the infrastructure and network design may change as a result of Big Data application implementation. Due to the fact that Big Data is like a wave to business, then the IT organization are required to use the coordinated approach when it comes to its planning implementation. A strategy that is IT oriented is required to ensure that its maximum utilization is reached. The well coordination and the impression of the required changes within the organization will ensure that success is seen and the required details are reached. Reference Boyd, Dana; Crawford, Kate (2011). "Six Provocations for Big Data". Social Science Research Network: A Decade in Internet Time: Symposium on the Dynamics of the Internet and Society. Chui, Brad Brown, Jacques Bughin, Richard Dobbs, Charles Roxburgh, and Angela Hung Byers, (2011). Big data: The next frontier for innovation, competition, and productivity, McKinsey Global Institute. De Mauro, Andrea; Greco, Marco; Grimaldi, Michele (2016). "A Formal definition of Big Data based on its essential Features". Library Review, pg. 122–135. Ibrahim; Targio Hashem, Abaker; Yaqoob, Ibrar; Badrul Anuar, Nor; Mokhtar, Salimah; Gani, Abdullah; Ullah Khan, Samee (2015). "big data" on cloud computing: Review and open research issues". Information Systems, pg. 98–115. Hilbert, Martin; López, Priscila (2011). "The World's Technological Capacity to Store, Communicate, and Compute Information". Science, pg. 60–65. Richard & Lucinda, (2012). The Critical Role of the Network in Big Data Applications. Steve Lohr, (2012). The Age of Big Data. New York Times. Snijders, C.; Matzat, U.; Reips, U.-D. (2012). "'Big Data': Big gaps of knowledge in the field of Internet". International Journal of Internet Science, pg. 1–5. Mark Flood, H V Jagadish, Albert Kyle, Frank Olken, and Louiqa Raschid (2011). Using Data for Systemic Financial Risk Management Proc. Fifth Biennial Conf. Innovative Data Systems. Yuki Noguchi, (2011). Following the Breadcrumbs to Big Data Gold. National Public Radio. Read More