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. This discussion not only covered the advantages of such use, but also identified certain limitations, particularly when the big data is collected for one purpose and ends up being used for another purpose. In a crux, big data is a crucial tool which can help the company in keeping not only its social values but also the economic ones as well. Reference List Aaltonen, A., and Tempini, N. (2014) Everything counts in large amounts: a critical realist case study on data-based production. J. Inform. Technol., 29 (1), pp. 97-110. Anandhi, R., and Sekar, G. (2017) A Birds Eye View on Big Data Analytics. International Journal of Engineering and Technology, 9(3), pp. 1701-1706. Baesens, B., Bapna, R., Marsden, J.R., Vanthienen, J., and Zhao J.L. (2014) Transformational issues of big data and analytics in networked business. MIS Quart., 38 (2), pp. 629-632. Bholat, D. (2015) Big data and central banks. Big Data Soc., 2 (1), pp. 1-6. Chen, H., Chiang, R.H.L., and Storey, V.C. (2012) Business intelligence and analytics: from big data to big impact. MIS Quarterly, 36 (4), pp. 1165-1188. Constantiou, I.D., and Kallinikos J. (2015) New games, new rules: big data and the changing context of strategy. J. Inform. Technol., 30 (1), pp. 44-57. Davenport, T.H. (2006) Competing on analytics. Harvard Bus. Rev., 84 (1), pp. 98-107. Davenport, T.H., Barth, P., and Bean, R. (2012) How big data is different. MIT Sloan Manage. Rev., 54 (1), pp. 43-46. Davenport, T.H., Barth, P., Bean, R. (2012) How big data is different. MIT Sloan Manage. Rev., 54 (1), pp. 43-46. Fichman, R.G., Santos, B.L.D., and Zheng, Z. (2014) Digital innovation as a fundamental and powerful concept in the information systems curriculum. MIS Quart., 38 (2), pp. 329-353. Gao, J., Koronios, A., Selle, S. (2015) Towards a process view on critical success factors in big data analytics projects. Proceedings of the Twenty-First Americas Conference on Information Systems, Puerto Rico, August 1315 Ghoshal, A., Larson, E.C., Subramanyam, R., and Shaw, M.J. (2014) The impact of business analytics strategy on social, mobile, and cloud computing adoption. Proceedings of the Thirty Fifth International International Conference on Information Systems, Auckland, New Zealand, December 1417. Gillon, K., Aral, S., Lin, C., Mithas, S., and Zozulia, M. (2014) Business analytics: radical shift or incremental change?. Commun. Assoc. Inform. Syst., 34 (13), pp. 287-296. Kim, G., Trimi, S., and Chung, J. (2014) Big-data applications in the government sector. Commun. ACM, 57 (3), pp. 78-85. Laney, D. (2001) 3D Data management: controlling data volume, velocity, and variety. [Online] Garter. Available from: https://blogs.gartner.com/doug-laney/files/2012/01/ad949-3D-Data-Management-Controlling-Data-Volume-Velocity-and-Variety.pdf [Accessed on: 16/12/17] LaValle, S., Lesser, E., Shockley, R., Hopkins, M.S., and Kruschwitz, N. (2011) Big data, analytics and the path from insights to value. MIT Sloan Manage. Rev., 52 (2), pp. 21-32. Loebbecke, C., and Picot, A. (2015) Reflections on societal and business model transformation arising from digitization and big data analytics: a research agenda. J. Strategic Inform. Syst., 24 (3), pp. 149-157. Lycett, M. (2013) 'Datafication': making sense of (big) data in a complex world. Euro. J. Inform. Syst., 22 (4), pp. 381-386. Madsen, A.K. (2015) Between technical features and analytic capabilities: charting a relational affordance space for digital social analytics. Big Data Soc., 2 (1), pp. 1-15. Manyika, J., Chui,. M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., and Byers, A. H. (2011) Big data: The next frontier for innovation, competition, and productivity. [Online] McKinsey Global Institute. Available from: https://www.mckinsey.com/business-functions/digital-mckinsey/our-insights/big-data-the-next-frontier-for-innovation [Accessed on: 16/12/17] Markus, M.L. (2015) New games, new rules, new scoreboards: the potential consequences of big data. J. Inform. Technol., 30 (1), pp. 58-59. Namvar, M., and Cybulski, J. (2014) BI-based organizations: a sensemaking perspective. Proceedings of the Thirty-Fifth International Conference on Information Systems, Auckland, New Zealand, December 1417. Newell, S., and Marabelli, M. (2015) Strategic opportunities (and challenges) of algorithmic decision-making: a call for action on the long-term societal effects of 'datafication'. J. Strategic Inform. Syst., 24 (1), pp. 3-14. Newell, S., and Marabelli, M. (2015) Strategic opportunities (and challenges) of algorithmic decision-making: a call for action on the long-term societal effects of 'datafication'. J. Strategic Inform. Syst., 24 (1), pp. 3-14. Peppard, J., and Ward, J. (2004) Beyond strategic information systems: toward an IS capability. J. Strategic Inform. Syst., 13 (2), pp. 167-194. Raghupathi, W., and Raghupathi, V. (2014) Big data analytics in healthcare: promise and potential. Health Inform. Sci. Syst., 2 (3), pp. 1-10, 10.1186/2047-2501-2-3. Ransbotham, S., Kiron, D., and Prentice, P.K. (2016) Beyond the hype: the hard work behind analytics success. MIT Sloan Manage. Rev., 57 (3), pp. 3-16. Sharma, R., Mithas, S., and Kankanhalli, A. (2014) Transforming decision-making processes: a research agenda for understanding the impact of business analytics on organisations. Euro. J. Inform. Syst., 23 (4), pp. 433-441. Tamm, T., Seddon, P., and Shanks, G. (2013) Pathways to value from business analytics. Proceedings of the Thirty-Fourth International Conference on Information Systems, Milan, Italy, December 1518. Tan, C., Sun, L., and Liu, K. (2015) Big data architecture for pervasive healthcare: a literature review. Proceedings of the Twenty-Third European Conference on Information Systems, Mnster, Germany, May 2629. Van der Vlist, F.N. (2016) Accounting for the social: investigating commensuration and big data practices at Facebook. Big Data Soc., 3 (1), pp. 1-16. Zuboff, S. (2015) Big other: surveillance capitalism and the prospects of an information civilization. J. Inform. Technol., 30 (1), pp. 75-89

The Advantages and Disadvantages of the ACCA and New Changes

The Advantages and Disadvantages of the ACCA and New ChangesThe ACCA and New changes are both required for higher education in England and Wales. However, there are some notable differences between the two, so it is important to consider the facts. In order to compare both the documents, it is necessary to understand the advantages and disadvantages of each.The differences between the two are significant enough to merit consideration, particularly in the admissions process. Most colleges and universities in England and Wales use the same guidelines to judge applicants for admission. Therefore, both would be fair enough.However, the ACCA and New changes include a wide range of requirements that are significantly different from those of the UK government. Specifically, the ACCA now considers an applicant's academic record, extracurricular activities, and general personality traits, including his or her talents, abilities, and interests. There are also new requirements to assess a perso n's personal circumstances. For example, the college will need to know whether the applicant's family health history is sound, because he or she will be assessed for this at the same time as they are being assessed for their ability to study.Also, the college's learning style is very different. In the UK, colleges are much more concerned with research than with preparing students for the written part of the application process. This is because most universities require more than passing a competency test to gain entry.Additionally, the ACCA includes a number of new subjects that colleges consider, such as Creative Writing, Drama, Music, and Technology, and the idea of 'enhancing' a student's knowledge, rather than giving them a grade, is still a rarity in the UK. So, while there are differences, there are also similarities in the requirements of the two documents.There are also differences between the ACCA and New changes, but they are less important than the differences in the Engl ish requirements. In particular many of the areas that were cut out of the former document, such as poetry, are still on the ACCA.Still, the ACCA and New changes are probably better than the UK government document. The former allows colleges to spend their time helping students, rather than writing letters to admissions officers that are never read. Additionally, the ACCA and New changes have a far more flexible application, which allows for more flexibility in choosing which colleges and universities to apply to.

The Ethical Implications of Zimbardo’s Prison Simulation Experiment in Today’s Modern Context Essay Example

The Ethical Implications of Zimbardo’s Prison Simulation Experiment in Today’s Modern Context Essay If a psychologist were to re-conduct an experiment involving a setup similar to Zimbardo’s famous 1960s prison-simulation experiment, he or she will no doubt encounter a lot of issues and be the culprit of many ethical violations. There is, first and foremost, the matter of employing state police to â€Å"arrest† the participants form their homes, search and handcuff them, and bring them via police car to the â€Å"prison†. Not only is this a direct violation of the participants’ rights to be informed of at least the most basic details regarding the experiment, but also subjects them to shame and public humiliation. One is led to ask, then: is such humiliation and de-moralizing necessary to maintain the integrity of the experiment?Another issue the setup will have to face is the matter of giving the participants the right to withdraw from the experiment any time they wish to do so. Since the participants were not informed that the â€Å"arrest† is a ctually the prelude to the experiment, they believed that it was actual, and as such, they failed top invoke their right to withdraw from it. The participants, namely the prisoners, then had to contend with sub-human living conditions and maltreatment from the hands of the â€Å"prison guards†; this when they could have opted out had they been more informed of the nature of the exercise.Finally, the matter regarding the validity of the experimental setup will have to be confronted. The experimental setup, harsh as it was, did not accurately portray the nuances of prison life, where inmates are most often forced to commit graver acts like involuntary homosexuality, drug use and abuse, and ganging up on others, not because they derive pleasure out of these acts, but because this is the â€Å"culture† prevalent in prisons, and they have to do what they can to abide by it so that they may simply survive within it.

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buy custom Cluster Development essay No single organization can exist successfully in solitude. Every company needs the support of other companies and infrastructure around it for it to succeed for instance, logistical infrastructures, suppliers etc. Cluster development cuts across the board from companies, businesses, institutions to governments. Cluster development not only helps to improve the productivity of companies, but also the economic prosperity of local communities. By developing clusters in their key positions, a firm amplifies the connection of its success and that of the local communities. The result is the sustainable growth and profitability of the firm, which in turn has multiplier effects to the local economy, from job creation in supporting industries to increased demand for secondary services. The key to successful building of clusters by firms in the local communities where they function is through identification of gaps in areas that greatly constraint their productivity such as transport, supplies etc. A company should identify those areas that it can handle single-handedly, as well as those that require collaboration with other companies. Cluster development through collaboration helps firms to create shared value; a concept that leads to sustainable productivity and prosperity of companies and communities. The concept of creation of shared value through cluster building is not only beneficial to the companies but to the surrounding communities as well. I totally agree with firms developing clusters in order to create sustainable profitability. Consider Yara, the leading mineral fertilizer company in the world, which decided to fund the improvement of the bad roads in Tanzania and Mozambique to enable farmers to access fertilizers and agricultural inputs. It is important to note that though road building is unrelated to Yaras business, it is a productivity driver to Yara since with good roads; farmers can easily travel and purchase fertilizers from Yaras selling outlets. However, not all companies have adequate capital to invest on improving the unrelated areas that are limiting their productivity, and therefore, as much as this idea sounds very good with a lot of benefits, only established companies with good financial back up can adopt it. Besides, the goal of every company is to make profits and that is why I think very few companies will be willing to adopt this idea since it involves huge financial risks without a surety about the period of time it will take for a company to start getting sustainable profits. In conclusion, I would say that the building of clusters is a promising idea that should be tried by companies to fully reap from its benefits. Buy custom Cluster Development essay

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Traffic Light Controller System Design library IEEE; use IEEE.STD_LOGIC_1164.ALL; use ieee.std_logic_unsigned.all; Uncomment the following library declaration if using arithmetic functions with Signed or Unsigned values use IEEE.NUMERIC_STD.ALL; Uncomment the following library declaration if instantiating any Xilinx primitives in this code. library UNISIM; use UNISIM.VComponents.all; entity tlc is   Ã‚  Ã‚   Port ( sensor : in   STD_LOGIC_VECTOR (3 downto 0);    hr : out   STD_LOGIC_VECTOR (0 downto 0) ;   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   hg : out   STD_LOGIC_VECTOR (0 downto 0) ;   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   hy : out   STD_LOGIC_VECTOR (0 downto 0) ;   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   fr : out   STD_LOGIC_VECTOR (0 downto 0) ;   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   fg : out   STD_LOGIC_VECTOR (0 downto 0) ;   Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚  Ã‚   fy : out   STD_LOGIC_VECTOR (0 downto 0) ); end tlc; architecture Behavioral of tlc is signal ts : std_logic_vector(3 downto 0); signal tm : std_logic_vector(3 downto 0); signal tl : std_logic_vector(3 downto 0); type state_type is (s0,s1,s2); signal state : state_type; begin process (sensor) begin if sensor