Usage of Big Data in Business Organizations-Samples for Students

Question: Discuss about the Uses of Big Data in Business Organizations. Answer: Introduction: The method of research into massive amounts of data to expose hidden patterns and correlations termed as Big data analytics. Big data is a term for massive data sets having large, more varied and complex structure with the obstacles of storing, analysing and visualizing for further methods of outcomes. Big data could supply scopes for small businesses, micro industries, independent traders as these could proceed for big corporations. When the average small business has low self-originated data than large corporate such as Apple, Microsoft or Google, it does not interpret that big data is limitless. Big data has more suitability to small businesses, as they are commonly more flexible and has ability to respond more instantly on data-driven insights (Gandomi and Haider, 2015). The big data implementations are required to be analysed and incorporated as appropriately as possible. The companies both large and small are starting to utilize big data and relevant analysis approached to gain data to better support of the company and serve the customers. This research paper represents an outline of content of big data, objectives, opportunities, processes, advantages and issues. The customer trend analysis, concepts of predictive modelling and data mining are going to discussed in the below section of the research report. Project Objectives: The big data analysis through customer transaction analysis and market structure analysis are executed. The global business trends help to shape future business research directions according to the results of past. Additionally, the data-driven business analytics and intelligence are highly applied and these could leverage scopes specified by the abundant data as well as analytics based on domain requirements in many critical and high-influencing application fields (Russom, 2011). A reputed organization considers relevant information and corresponding decision rights for the welfare of the company. The aim of the business project is to generate, transfer and analyse the big data for optimizing cross-functional cooperation and enhancing factors behind domain expertise. Project Scope: Business analytics and intelligence have deep relevance with big data analysis that is gradually becoming crucial in business communities since last five decades. The scopes associated with big data in various organizations have originated significant interest in business analytics. Big data management and warehousing is supposed to be the foundation of Business management. Simple graphics, data characteristics, data segmentation and data analysis are considered as big data mining techniques. Well-established management authority focuses on data-driven predictive modelling and various business applications (Sharda, Delen and Turban, 2013). The velocity, variety, variability, value and volume (5V) of big data help to increase the quality of decision-making (Chen and Zhang, 2014). Use of big data enables authority and managers to decide planning based on evidence rather than intuition. Because of this reason, big data has potentiality to bring revolution in management. Big data is a currently growing technology in the market that can bring vast benefits to the business organizations. The scopes are the digitisation and interlinking of analogue and unstructured data such as social media. Social media information, mobile, cloud and big data technologies converge to solve requirements of business strategy and to get the actual information to reach to consumers quickly by maintaining validity of external big data and reliability of relationships among data elements (Waller and Fawcett, 2013). Current technologies in data access and analytics lend themselves to innovatively attacking challenge of intelligent and proactive col laboration in these disciplines and tool sets. Business administrators could correlate past sales activities and demographics as well as forecasting the demand of products (Zikopoulos and Eaton, 2011). Literature Review: Big data is defined as huge amount of data that needs modern technologies and architectures so that it becomes possible to extract values by capturing and analysing data handling methods. Because of such huge size of data, it becomes very tough for performing effective analysis using the traditional techniques. It is essential for different obstacles and problems related in granting the technology brought under light. Big data technology along with its importance in the world and existing projects that are effective and crucial in transforming the idea of science into big science (Sagiroglu and Sinanc, 2013). Big data analysis reveals the forms, nature and philosophical bases in details. The big data is framed technically, economically, ethically, spatially and philosophically (McAfee, Brynjolfsson and Davenport, 2012). Big data is generally primary and quantitative in nature. It has four major qualities that are Captured, Exhausted, Transient and Derived. Big data analytics provides business abilities to gather customer data apply analytics and quickly detect potential problems. Therefore, big data analysis is becoming more and more crucial to businesses as business is the key point to obtain better data context. Mark Gallagher, the managing director of CMS Motor Sports Ltd. has developed the theory of The Data Driven Business of Winning. Driven by specialized analytics systems and software, big data analytics could focus the way to different benefit utilities involving new revenue scopes, marketing that are more effective, better customer service, enhanced operational efficiencies and competitive advantages over rivals. Data scientists and statisticians handle big data analytics for predictive modelling (Mithas et al., 2013). Useful data resources involve traditional and contemporary data, transforming activities such as identifying markets, market trend, market value and customer satisfaction. Social media, browser logs, text analytics, in-house data as well as large and public datasets such as survey data are the major resources of big-data for the business organisations. Some of the processes used to gather big data involve customer surveys, analysis of customer browsing histories and preference of social media (Liebowitz, 2013). Other ways big data is gathered are sensors utilised in public transportation and credit card spending histories. After collection of big data, it is typically in high capacity hard drives and on the cloud. Big data is used in major decision making for helping a growth of business and achievement of revenue targets. Previously, big data were mostly utilized on-premises especially in big management organisations. However, cloud platform vendors like Amazon Web Services (AWS) and Microsoft have made it easier to establish and manage Hadoop clusters in the data storing clouds (Talia, 2013). With the help of their support, the distribution of framework of big data has become easier. Potential drawbacks may trip up business organizations on initiatives of big data analytics. Therefore, it involves a scarcity of internal analytics skills of the company. To fill up the pitfalls, business and financing companies are hiring experienced data scientists, analysts and data engineers. Organizing big data and making big data sources connected with resources is a critical task. Every department of a company from marketing to finance or from production cell to analytics uses big data for well-informed decisions. Connected big data resources bring more visibility and transparency in any business organization by more access to the employees and managers. Big data access grows more awareness among the business employees. The whole hierarchy of decision-making becomes clearer by connecting big data sources (Cuzzocrea, Song and Davis, 2011). Businesses become more adept at controlling the flood of data by connecting big data sources with the entire hierarchy of decision-making. Now days, social media data has exploded and correspondingly organizations have much data to make timely decisions. Combination of big data source with other customer information sources leads to more clear reflection of current and potential customers. According to the various departments, sales and marketing resources utilises data mining approach. In the field of analytics of big data, the approach of multidimensional data plays a major role by highlighting open problems and proper research trends. The analytical contribution is accomplished at the end by generating Data Warehousing. In data warehousing, data filtration and data cleaning are two crucial steps (LaValle et al., 2011). After that we could proceed for data management. Financial and trading solutions need to be solved by predictive and trend analysis driven by big data. Big data analysis supports organizations to detect new scopes. In return, it leads to smarter business moves, efficient operations, greater profits and more satisfied customers. Big data techniques like cloud-based analytics and Hadoop bring significant advantages of cost while it comes the turn of storing huge amount of data. Additionally, management authority could detect the more effective ways of trading. The cost reduction is facilitated in this way. With the speed of Hadoop and memory analytics combined with ability to analyse new sources of data, managing authority should be able to analyse information immediately. With the ability to predict customer needs and satisfaction with the help of big data analysis, organizations are producing new products to meet requirements of customers (Chen, Chiang and Storey, 2012). High performance analysis leads to indicate fleeting scopes to get new growth scopes using information resources more efficiently. After making the data ready, software operations are applied for advanced analytics methods. Big data analysis is consists of tools for data mining, predictive analytics, forecasting, machine learning and deep learning. Trend analysis and Text mining procedures are frequently used for analytic methods. The main advantage of big data analysis is that it often involves data from both external resources and internal systems. Additionally, streaming analytics in big data analysis are becoming common in big data environments. Real-time analytics are executed by open source stream processing software such as Flink and Storm. Customer service and retail service are adopting big data analytics to meet the demands. Retail sectors are not only granting in-depth understanding but also having high quality and well-governed business questions. In customer service, data mining technology helps the company to examine large amount of data by softwares like SAS and Python and assess the outcomes. The open source software like Hadoop and Python or authorised softwares like SAS helps to compute distributing models of big data. The predictive analytics uses technological data and statistical algorithms to identify the complex features of the data. In corporate services and information technological sections, business companies generally use the technology of text mining. Emails, Twitter feeds, online surveys and blogs are the most common text based sources of big data analysed by corporate houses. Best quality of refined data provides the best interpretation. It could sort out problems regarding customer experience or complex productivity. Big data techniques analyse all the various permutations to augment that gives lesson to more quickly resolve or enhance a specific solution. IT department has the ability to measure the business outcomes for planning future services and enhancing cost measurement. Most business organization has some type of asset database that involves data. In spite of the anticipated and realised utilities of data infrastructures, big data still neither have been universally welcomed nor has been proven easy to structuralize and implement. The issues regarding big data analysis are not simply technical and human-resource based (Katal, Wazid and Goudar, 2013). Sometimes the appropriate model building becomes difficult. The amount of data included in big data analysis may cause typical data management issues in the field of data quality, consistency and governance. Besides, the data of different platforms and different conditions may provide confusing results. Integrating big data tools and software like Hadoop, Spark, SAS and Python into cohesive architectures puts the needs of big data analysis of a business organisation in a challenging position. Hence, analysts and IT team identify the right mixture of technical aspects and then put the segments of big data analysis together (Bughin, Chui and Manyika, 2010). Conclusion: Big data have strong utility and high value as they generate major outcomes to the different modes of analysis in the area of business and financing organizations. It generates innovations, policies and knowledge that shape the analysis structure. The report successfully provides a broad overview of emerging set of techniques utilised to process, analyse and implement big data analysis for business purpose especially for small financial industries. The scale and velocity of big data analysis are being more widespread and accessible. Business organisations further need to observe insights of data deluge, involving structured and unstructured data handling, basic coding, data mining, data visualization, data modelling and simulation. Data driven science would transform the underlying environmental systems. Identification of specific relationships between phenomenon and methods could create new hypotheses that might establish further studies. Big data analysis gives an effort on the businesses that are slower and less efficient with more traditional business management. Speed and efficacy are needed in big data analysis for further decision making. The better business outcomes are achieved by combination of good quality data and powerful analytics. Business organisations could be successful with the inclusion of good data and powerful analytics. Therefore, we can conclude that big data analysis for business is extraordinary relevant market-driven essential curriculum vitae. The concrete issues of management from business leaders gain exposure to the real world for successful practitioner and participants. References:- Bughin, J., Chui, M., Manyika, J. (2010). Clouds, big data, and smart assets: Ten tech-enabled business trends to watch.McKinsey Quarterly,56(1), 75-86. Chen, C. P., Zhang, C. Y. (2014). Data-intensive applications, challenges, techniques and technologies: A survey on Big Data. Information Sciences, 275, 314-347. Chen, H., Chiang, R. H., Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact.MIS quarterly,36(4). Cuzzocrea, A., Song, I. Y., Davis, K. C. (2011, October). 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