Uses of Big Data in Business Organizations-Free-Samples for Students

Question: Discuss about the Uses of Big Data in Business Organizations. Answer: Introduction In order to run the daily operations of the company, a lot of information is made use of in daily manner. This requires the data to be saved in a proper manner so that it can be used. And in this era of information, huge amount of data needs to be available in a ready manner to make decisions. Big data relates to the data sets which are not just big but are also high in velocity and variety, owing to which, it becomes difficult in handling if the traditional techniques and tools are made use of. In this discussion, the literature which has been published in context of the use of such big data by the companies has been elucidated. Project Objective The key objective of this project is towards identifying the manner in which the big data is used by the organizations. The other objective is to identify the manner in which the organizations have grown to rely upon such big data. Project Scope This discussion would be limited to secondary sources of research where the different literatures would be analysed, in terms of the ones which focus on the use of big data by the organizations. This would help in showing the growing reliance on big data by the companies, along with the looming threat associated with the use of such big data. Literature Review Before carrying out a discussion on how the big data is used by the organizations, there is a need to understand what big data actually refers to. In terms of Manyika el al (2011), it is the amount of data which is just beyond the technologys ability for storing, managing and processing in an efficient manner. TechAmerica Foundation have defined it as a term which is used to define the high-volume, high speed, complex, high-tech and multivariate data for the purpose of capturing, storing, distributing, managing and analysing the information. Gartner and Gursakal have defined big data as the high velocity, variety information and volume of information assets which need new forms of processing in order to be allowed for the enhanced insight discovery, process optimization and decision making (Anandhi and Sekar, 2017). As per Finchman et al. (2014), big data has gained a lot of significance as being a breakthrough in the technological development amongst the academicians, and in views of Chen et al (2012) this is also true for the business communities. Laney (2001) has defined big data the data which is based on huge volumes of broadly varying data which is processed after being generated and captured at high velocity. This makes the processing of such data through the existing technology, a difficult thing to be done (Constantious and Kallinikos, 2015). Through the adoption of analytics technology, the companies can make use of big data for the purpose of developing new and innovative products, services and insights (Davenport et al., 2012). As per Baesens et al (2014), there are a number of opportunities which are presented from the big data analytics for the companies and these are quite important. They have described big data as being the mother lode of the disruptive changes in the business environment which is networked. Through the adoption of the big data technologies, the companies are expected to attain advantages in different domains which include security, health, e-commerce, science and e-government (Chen et al., 2012). The organizations benefit from the perceived values which depend upon their strategic goals for the adoption and use of the big data (Ghoshal et al., 2014). These values are not restricted to the economic values, but also include the economic values as well. The social values which are present for the companies, in views of Cech et al., (2015) include education, in views of Raghupathi and Raghupati (2014) include healthcare, and in views of Newell and Marabelli (2015) includes security and public safety. The governments can also make use of this big data for enhancing transparency, preventing crime and fraud, improving upon the national security, supporting the wellbeing of people through healthcare and education, and increased citizen engagement in public affairs (Kim et al., 2014). As a result of this, the social value consists of the advantages for the single users and the societal benefits like consumer surplus, employment growth and productivity (Loebbecke and Picot, 2015). For an organization, their economic value can be measured through the increase in their competitive advantage, profit and business growth which results from the adoption of big data (Davenport, 2006). The economic value generally covers the monetary benefits which are usually appropriated by the companies. An example of this can be seen in the reliance being made by the organizations for guiding the strategies of the organization and towards the day to day operations of the organization which is expected to give better financial performance for the company in comparison to the other organizations (LaValle et al, 2011). Generally, the big data is deemed as being a source of the new and innovate business opportunities, products and services (Davenport et al., 2012). Apart from this, the big data results in operations being more effective and efficient, and the examples of this include the selection of right people for some jobs and tasks, minimization of quality issues and errors, improved customer relationship, optimization of supply chain flows, and the setting up of the most profitable prices for the services and goods (Davenport, 2006). Apart from this, the economic and social values can be attained from the big data through more informed strategizing and enhanced decision making (Constantiou and Kallinikos, 2015). In the opinion of Clarke (2016), the academic and the practitioner based literatures have a major focus over the opportunities which are provided for the organizations through the big data. Though, extensive publicity and high hopes relating to big data cannot guarantee the attainment of actual value, which could also result in the organizations believing that they can obtain more value from the big data which in reality they can actually realize in practice (Ransbotham et al., 2016). There are different sources of big data and these include the sources from within the company, which includes the transactional data and data from the ERP systems, and the external sources of data include the data offered through third party, open data, sensor data and the user-generated data (Zuboff, 2015). Due to these reasons, the data is often not produced or collected for the same reasons for which it is used in the end (Newell and Marbelli, 2015). On the basis of the granularity and the variety of data, it becomes difficult to predict which insights would be accrued from the different sources in an ex-ante manner (Constantiou and Kallinikos, 2015). The trends of big data have resulted in the creation of an attitude of collecting the data which has no pre-defined objective, promotion of bottom up, analysis, inductive approach to collection of big data and its exploration. As per Bholat (2015), this approach begins from data and later on attempts to generate a theoretical explana tion. An example of the same can be seen in the study which was performed by Madsen (2015) on the manner in which the technological features of the digital social analytics, which is simply a subset of the big data analytics, influence the project work. This inductive approach allowed for the distinctions and patterns to come to light as they were unknown previously, to emerge from the big data. As a result of this, the data collected for one reason could be made use for other purposes, as the same can be combined and analysed in different and new manners (Aaltonen and Tempini, 2014). Some of the business analytics experts were interviewed by Tamm et al (2013) for the preliminary assessment of the pathways to value from the big data. The experts in this study showed concerns in context of the value of analytic based advisory services, as the insights were gained by approaching the big data in an inductive manner for compensating for the efforts which were required to troll the data without having a clear business case or clear focus. The retention and trolling of the huge bites of unstructured data are deemed as an expensive exercise, which requires the focus of the particular business (Gao et al., 2015). Such focus is necessary for the purpose of maximization of the possibility of value realization. As a result of this, the scholars have acknowledged more deductive approach to the big data analytics which begin from the general theory and later on make use of the particular data for testing it. This hypothetic based approach is common parlance in healthcare setti ngs in which the data is collected, processed and visualized for particular purpose (Tan et al., 2015). A major risk in this approach is the confirmation bias, which takes place when the decision makes looks for the data specifically for confirming their hypotheses (Bholat, 2015). Bholat (2015) has made the argument that the induction and deduction in practice are two ideal approaches which are intertwined and which complement each other and this implies the requirement for balancing them. An example of this is the analysts which could be provided certain degree of freedom to arrive in an inductive manner at the creative and innovative ideas, though the specific boundaries could be set around the projects in a simultaneous manner which they were working upon for making certain that the business value is delivered (Gao et al., 2015). Lycett (2013) has argued that the limit to which such inductions and deductions are balanced in the real world depends partly over the influence of the pre-existent frames of mindsets or reference of the ones who interpret the data. As a result of this, the issues of human based intelligence and algorithmic intelligence as a debate at work practice level. There are also arguments in favour of the algorithmic intelligence which relate to such algorithms being a guide to analysts for the innovative analytic categorizations and concepts, whilst avoiding the pre-established and preconceived distinctions. When it comes to the artificial intelligence or the sophisticated machines learning the algorithms, there is an improvement of procedures with time (Van der Vlist, 2016). Different scholars have highlighted different examples in this regard, for instance Markus (2015) highlighted IBMs Watson, Newell and Marabelli (2015) highlighted the self-driving cars, and Sharma et al (2014) shed light over the fraud detection algorithms. The scholars considering big data at the work practice level have debated on the manner in which the different actors work for gaining the possible valuable insights from the big data. A number of studies in this regard have been based on the augmentation on empirical evidence, including that of Madsen (2015), and Nam var and Cybulski (2014). In views of Peppar and Ward (2004), in order for the organizations to develop their capabilities, they need to find the ways for effectively developing, mobilizing and using the human and technical resources which relate to the big data. There are different manners of putting big data towards innovations, and one of the manners of using this is through accessing the big data techniques and sources, and acting upon improving the present processes in terms of their effectiveness and efficiency. An example of this is IBM implementing a database system for linking its employee, which is used by them for improving upon the knowledge sharing and the efficiency across the company (Gillon et al, 2014). Conclusion The previous segments covered a brief upon the different literatures which cover the use of big data. 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