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Question: Discuss about the Use of Big Data In Business Organizations. Answer: Introduction Since the emergence of technology and the social network through the internet, many businesses i.e. big and small businesses have been taking the advantage of such platforms for collecting data from their respective and targeted customers about their product. Due to large extended family created by the social media on the internet, those businesses and companies collect more information in form of responses from their customers in return that can be termed as big data. Therefore, big data can be described as the enormous volume of data that is either structured or unstructured and are complicated that they cannot be handled, analyzed or efficiently processed by traditional management tools. This research study therefore is aimed at looking at the uses of big data in Telstra business organizations towards improving its overall performance. Different types of big data will be looked at in this project ranging from structured, semi-structured to unstructured data. The data that can be s tored, is accessible and can be processed in a fixed format are referred to us structured data. Several challenges are faced in unstructured data being that they are stored in an unknown format whereas semi-structured data is the data that can exist in both the known and unknown formats Dean (2014). Project objectives Some of the objectives that will have to be met in this project upon its successful completion will be; To determine the uses of big data in business organizations, a case study of Telstra. To determine the ways in which big data analysis can present wrong information that can lead to improper strategic decisions in a business. Project questions From the above project research objectives, we have the following research questions that will help to meet the project objectives. What are the uses of big data in business organizations, a case study of Telstra? What are some of the ways in which big data analysis can present wrong information that can lead to improper strategic decisions in business? Project scope Among other cellular and mobile providers in Australia, Telstra forms part and is one of the biggest in the country and it is located Melbourne Victoria. The area of study will therefore be Dandenong Road Clayton Vic in Melbourne, Australia the place where our case study i.e. Telstra cellular and mobile providers is located. Literature review According to Wamba et al (2015), they perceived big data as having the ability to transform the ways of management in business organizations. Due to this therefore in the current era and generation, businesses are scrambling and changing from managing small data to big data (Sagirolgu and Sinanc, 2013). The need of not wanting to lose or taking for granted any of the information collected from the customers concerning the businesses have compelled the business management to acquire databases that can handle big data (Hashem et al, 2015). This has further given birth to big data analytics that help in exhausting information from the big data leaving none of the information hidden in order to gain market competitive advantage (Zhang et al, 2012). With big data, they portray the characteristics of streaming in every second and each time as they keep on being created where they need to be stored for later value extraction. In order to improve the marketing strategies Telstra is taking in to use the big data and analyzing them to hike their services as per the customers demands. Big data once collected, in the storage operational data system, they are then transmitted to the storage by using Extract Transform Load (ETL). These ETL are the tools that are used in the extraction of data from the outside sources so that the data can be changed to fit the needs for operation and then ultimately ensure that the data is loaded into the database Dean (2014). Furthermore, some non-rational databases like the No-SQL were designed purposively to manage the unstructured data where data management and storage are separated this is opposed to the rational databases Dean (2014). According to He et al, (2011), they stated that big data processing had four critical requirements where fast data loading is taken to be the first one, the second comes query processing. This second requirement is responsible for the satisfaction of heavy workload and requests for real time since most queries are response time critical. Highly efficient utilization of space for storage forms the third requirement for big data processing, this one help to handle the fast growth of user activities that also help in managing the available disk space for storage. The last and fourth requirement is strong adaptivity to highly dynamic workload pattern this is according to (He et al, 2011). All these four requirements are always put to use by Telstra whenever the company is collecting large data from their customers. Speed and efficiency by which data is conveyed is managed through the process out the four different requirements. The company do gather and manage both structured and unstruc tured data through social media from their consumers of goods they sell. Various functions of big data that are enjoyed by companies and businesses such as Telstra irrespective of their sizes are, having the privilege to dialogue with the consumers through the social media. Customers being that they know what they need from their producers, they tend to take their time to compare what they are being offered to other products from other producers. Big data therefore enable the business enterprises to take care of such customers by engaging on one-on-one talk with the customers in order to maintain their customers and stay relevant in the competitive market Sin and Muthu (2015). Through managing big data by business organizations, the panel of business analysts can be able to perform risk analysis about the issues of the business operation. According to Verburg et al (2013), they stated that the success of the company is determined by several factors not only how the company is run but also social and economic factors play vital roles in the success realiza tion of the companies. Further, Luo et al (2016) ascertained that big data ensure that there is safety of the acquired data in the company since the entire landscape within the company are allowed to be mapped by big data tools. This therefore enable the company and all the sensitive business information safe and protected in a good manner. The scramble for big data therefore by the companies are for them to enjoy the safety of their stored information in a protected way (Chen et al, 2012). This function of big data is attractive and vital for organizations that need to keep or store financial information since there is surety of data safety. By well managing big data, new revenue stream can be created since the insight for analyzing the consumers and the market is provided for (Leibowitz, 2013). Companies that are using big data can benefit more from it if they have their employees trained about the big data. Bid data analysis also referred to as big data analytics that takes the process of drawing conclusion from big sets of data. In as much as businesses enjoy the use of big data, it is also in some cases associated with some limitations in their analysis that can lead to improper strategic decisions in business. Correlation between variables can be prioritized since when the variables are linked to one another the analysts tend to teas out the correlation. The link between the variables under study does not always mean that there is existence of relationship between the variables. This therefore bring to the attention of the specialists that not all the relationships are always important or meaningful. Such inferences when made by the analysts in business may lead the business to improper decision that could worsen the situation at hand (Boyd and Crawford, 2012). The analysts when using big data take control of trying to answer all the questions that might be arising in business for it s operation, but it is upon them to discern which questions are relevant and are therefore answered by the data they are managing (Raghupathi and Raghupathi, 2014). Sometimes the tools used in data collection can have an effect of resulting to inconsistency, for example when the internet through google is used, it might result to change in search experience in several ways, and this will therefore result to variation in the search findings from time to time. Over reliance of such tools for gathering the information that are used for analysis, using the dataset from those tools to carry out the correlation test may result to unreliable results. For the organizations to enjoy the usefulness of big data, they need to know and understand big data since the use of big data analytics tools to derive information is complicated (Boyd and Crawford, 2012). Changes are nowadays experienced with data as people are not just concerned with data, but they are concerned with what the meaning and importance of data (Raghupathi and Raghupathi, 2014). Better understanding of the collected data is deemed useful to business organization as it help in coming up with useful and informed decisions for the business. In the process, data analytics is carried out in order to come up with relationships, unknown patterns and information Muthu (2015). From big data, carrying out analytics helps further in extraction of hidden patterns from big data sets and also determining available relationships between variables that could help in giving surplus information to the business organization. Various additional analysis have been found common with large data sets on top of advanced data analytics methods that include clustering, decision tree, association rule and regression analysis He et al, (2011). Content sharing have become so easy nowadays through social media as the only problem remains to be failure of exploitation of the enormous content that is generated from the social media networks (Zhang et al, 2012). So the most appropriate data analysis method that can be used to extract information from such data for them to be useful to business is the social media analytics. This analytics method will tend to exhaust all the information that could be hidden in the data that could help in prediction and coming up with informed business decision. Conversations and reaction from people in social media communities can best be understood, extracted and detect useful pattern and intelligence from such interactions from what is shared through carrying out social media analytics. As a result therefore, advanced big data visualization (ADV) is to be done in response to growth in big data analytics (Zhang et al, 2012). All is not done until data can be presented in a way that it can be ef fectively consumed by people so that the business decision makers can be able analyze the available data properly so that a serious tangible action can be taken. Conclusion Well management of big data and exhaustively drawing information from all the available collected data will result to positive impact to Telstra cellular and mobile providers. This will enable the cellular and mobile provider company with all the necessary information as they are able to communicate one-on-one with their customers. Social media will be found to be providing a good platform that encourage such communication and hence building good customer relationship thus boosting their sales. This is one of the uses of big data that Telstra enjoy and that they employ to widen their market and hence giving them higher chances of existing in the market. The acquired information from the customers is broken down to the last end that the customers problems are addressed by the company. Through risk analysis conducted by analysts from the collected big data, the company will be able to know the future risks the business might face and find the solutions to the current issues and also come up with appropriate solutions that will solve the future business risks. This will further smoothen the operation of the business as it will stay free from any risk that can affect its operations at the moment and any other time in future. References Boyd, D. and Crawford, K., 2012. Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon.Information, communication society,15(5), pp.662-679. Chen, H., Chiang, R.H. and Storey, V.C., 2012. Business intelligence and analytics: From big data to big impact.MIS quarterly,36(4). Dean, J., 2014.Big data, data mining, and machine learning: value creation for business leaders and practitioners. John Wiley Sons. Hashem, I.A.T., Yaqoob, I., Anuar, N.B., Mokhtar, S., Gani, A. and Khan, S.U., 2015. The rise of big data on cloud computing: Review and open research issues.Information Systems,47, pp.98-115. He, Y., Lee, R., Huai, Y., Shao, Z., Jain, N., Zhang, X., Xu, Z.: RCFile: A Fast and Spaceefficient Data Placement Structure in MapReduce-based Warehouse Systems. In: IEEE International Conference on Data Engineering (ICDE), pp. 11991208 (2011) Liebowitz, J. ed., 2013.Big data and business analytics. CRC press. Luo, J., Wu, M., Gopukumar, D. and Zhao, Y., 2016. Big data application in biomedical research and health care: A literature review.Biomedical informatics insights,8, p.1. Raghupathi, W. and Raghupathi, V., 2014. Big data analytics in healthcare: promise and potential.Health information science and systems,2(1), p.3. Sagiroglu, S. and Sinanc, D., 2013, May. Big data: A review. InCollaboration Technologies and Systems (CTS), 2013 International Conference on(pp. 42-47). IEEE. Sin, K. and Muthu, L., 2015. APPLICATION OF BIG DATA IN EDUCATION DATA MINING AND LEARNING ANALYTICS--A LITERATURE REVIEW.ICTACT journal on soft computing,5(4). Verburg, R.M., Bosch-Sijtsema, P. and Vartiainen, M., 2013. Getting it done: Critical success factors for project managers in virtual work settings.International journal of project management,31(1), pp.68-79. Wamba, S.F., Akter, S., Edwards, A., Chopin, G. and Gnanzou, D., 2015. How big datacan make big impact: Findings from a systematic review and a longitudinal case study.International Journal of Production Economics,165, pp.234-246. Zhang, L., Stoffel, A., Behrisch, M., Mittelstadt, S., Schreck, T., Pompl, R., Weber, S., Last, H., Keim, D.: Visual Analytics for the Big Data EraA Comparative Review of State-of-the-Art Commercial Systems. In: IEEE Conference on Visual Analytics Science and Technology (VAST), pp. 173182 (2012).