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Discuss the Issues related Big Data?




Bettencourt, (2014) presumed that big data is one of the evolving term that helps in doing the descriptions various exponential growth as well as various availability of different data in the both structured as well as unstructured manner. Big data also helps in doing the accurate decision-making. Moreover, Bulbulia, Sibley & Wilson, (2014) articulated the definition of big data into the 3 V’s: Velocity, Volume and Variety. Furthermore, big data is also described by using large nature if data sets that cannot be analyze with the help of traditional tools of data processing. Apart from this, some of the primary difficulties depending on big data are storage, sharing, searching and the visualization of various data. Big data becomes identical with various other business concepts like data mining, analytics as well as business intelligence.

Background of the study:

In this research paper it mainly discusses about the company M-Global doing the feasibility analysis focusing on various issues like economic, Legal, Social as well as ethical issues based on the selected topic of Big data. Researchers also do incorporation of related data sets, real time data, archival of data as well as various unstructured data which basically known as big data. Moreover, big data includes the incorporation of various challenges depending from the technical to the conceptual framework of data. Apart from this, it also deals with the various use of prediction of analysis of various methods in doing the extraction of values from the particular data. Now day’s big data plays a vital role to maintain large amount of both structured as well as unstructured data from doing the transaction of data to the various government regulations.

Rationale of the study:

In this particular research study, it mainly evolves with various social, economical, legal as well as ethical issues depending on big data. Moreover, it also discusses about the overall sizes of data sets that becomes one of the large part of the equation of data sets (Couldry & Powell, 2014). It mainly involves with various feasibility analyses of big data focusing on different aspects and discusses about the large nature of data sets that becomes very difficult to maintain that large nature of data sets.

Now days with the advancement of various technologies, it also discusses about the correlation of big data relation to various nature of issues associated with big data (Dhar, 2014). Therefore, most of the companies like IBM are using big data in order to maintain their all data bases as well as data sets. This particular study also focuses on various issues concerning with big data based on the different functions of M-Global.


Research aims and objectives

Aims of the research study:

The research aim of this particular study is to do the investigation of various economical, legal, social as well as ethical issues regarding the implementation of big data. The primary aim of this study mainly helps in doing the examination of various potential issues of the implementation of big data. it also includes the recommendation of various appropriate methods in doing the resolution of various issues regarding the big data implementation.

Objectives of the research study

  • To investigate the economical, social, legal and ethical issues of implementing big dataanalytic tools
  • To examine potential issues regarding big data implementation in warehousing
  • To recommend appropriate methods for resolving the issues that relates to big data implementation

Research questions

  • What are the economical, legal, social and the ethical issues in doing the implementation of big data?
  • What are the appropriate methods to resolve the issues related to big data implementation?
  • What are the potential issues that concerning the big data implementation?

Literature review

In this particular research paper, it does the identification as well as examination of various legal, social, economical and ethical issues based on the implications of big data. Moreover, with the identification of these issues as well as understanding of various positive and negative externalities by the big data it discusses about technological impacts of big data. The practices of big data mainly deal with various data from the different people as well as all these human elements reflect various social as well as moral codes in doing the business. This particular paper also offers the analysis of various opportunities in the relation to big data as well as various issues concerning the big data in the case of examining the opportunities.

Economical issues associated with Big Data:

Big data does the implication of various economic issues that have both the positive as well as negative impacts. However, big data acts as the catalyst for doing the various innovations at the time of doing the development of big data in order to incorporate all the strategies to derive the various benefits in the terms of doing the capturing various efficiencies regarding the big data of different sectors. Therefore, all these efficiencies can create the negative impacts that also include the various downsizing of different workforces. Furthermore, the explosion of big data analytics direct various industry actors, policy makers etc in order to view the data as the resources that also have the value that helps in order to boost the economy.

For instance, Big Data has largely helped many developed economy such as UK, Japan, etc. Through this tool, the economies are able to collect, transmit, store, analyze and act on the data. As a result, the economies are well able to collect right amount of data related to weather or crop plantings. On the other hand, the history of plant including rainfall, sprayings, etc is easily collected which helps in making effective plan so that best value can be generated. Moreover, through big data, the economies are able to predict the weather for any particular region. Therefore, if they found that there will be high amount of rainfall then they alert the government to prepare themselves to tackle such situation.


Social and ethical issues associated with Big Data:

Big data helps in doing the implication of various social as well as ethical issues like discrimination, privacy, trusts, manipulation as well as exploitation. Moreover, in doing the description of various big data practices it mainly deals with various data from the people and the human elements that mainly focus on both the moral as well as social codes. Therefore, all these issues are needed in doing the recognition that helps in doing the incorporation of various fundamental ethical and social issues related to the big data practices. Various issues related to the social as well as ethical issues needs to do the recognition that helps the organization in order to incorporate various fundamental ethical as well as social values related to the big data policies and practices.

For example, the big data has lead to development of mHealth Alliance which is a public health group. It helps in analyzing the data that has been transmitted from the mobile phones of patients in nations such as South Africa and Bangladesh. Therefore, it helps in providing security and privacy of the patients in healthcare. Moreover, the healthcare is well-connected with the patients and they are able to acquire enough data about the patients’ well-being.

On the other hand, in order to understand the ethical issues, an example will be useful. It has been seen that Snapchat, a mobile messaging tool, has failed in protecting the privacy of message exchanged between two people due to lots of ad popping up. As a result, it raised question on ethicality of the company which badly hit them. On the other hand, WhatsApp worked on No Ads philosophy which helped in protecting the data of messengers. It shows ethical conduct of the company and as a result, the company gained 450 million in quick time as users.

Legal issues related to the big data:

Both the processing and collection of big data raises to the numbers of legal issues related to the context of contracting, property right and licensing. Therefore, big data causes various implications for doing the data protection as well as privacy that can leads to the numbers of jurisdictions problems. Moreover, the numbers of potential legal issues helps to raise both the negative and positive externalities that include various rights in doing the processing of big data. Legal issues arises mainly in the relation to the context of big data that includes various intellectual rights, contract issues and also the privacy risks associated with the implications of the due process. Furthermore, legal issues also highlight the existing gaps between the legal framework and the technological capabilities based on various uncertain outcomes for the economic development. Legal issues help in doing either the support or control the development of big data industry as well as recognition in the terms of doing the understanding about the framework based on big data.

Legal concern has risen with the rise of big data. The States of the nation is well involved in establishing privacy laws in relation to use of big data such as California Online Privacy Protection Act. This helps in protecting the data and pose legal action if anyone found guilty. On the other hand, European Union has also imposed number of laws and restrictions on the companies in regards to the breach of data.


Research Methodology

According to Mangiameli, (2015), research methodology depicts in doing the overview of various composition of different components regarding the entire research that includes various research approach, research data and design. Therefore, the research design and methodology is mainly deals about the various procedures based on which the researcher becomes able in drawing the conclusion of this particular research study.

Hence, the researcher imposes the concentration in doing the recognition of the significance for the every step of the research methods (Minnitt, 2014). In order to analyze the selected information researcher uses several research methodology tools such as research approach, research philosophy, research design, research methods, sample and sampling techniques etc.

Research approach

Mainly the research studies any one of the two existing research approaches. Moreover, the two research approaches are deductive approaches as well as inductive approaches. The deductive research approaches helps in doing the evaluation of various existing approaches with the various support of data analysis (Sowe & Zettsu, 2014). On the other hand, the inductive approach helps in doing the various observations, data analysis, data collection as well as various theory building models. The deductive approach of this particular research study follows various theories, testing of hypothesis, confirmation as well as observation of various objectives of this particular research study.  The fundamental reason in doing the adoption of the deductive research approach will be suitable which also involves in this particular research study with the evaluation of the various existing theories about the topic of big data and also the various factors which can causes various impacts in the occurring of big data (Steinhaus, 2014). However, on the other side, both the analysis as well as findings part of the research study justify all the mentioned research theories in doing the relation of the social, legal as well as economical factors of big data. Therefore, depending on current information as well as according to the nature of research study, the deductive approach will become suitable for doing the present research study (Xu, Zhao & Wang, 2013).

Research Design

The research design helps in doing the research study by doing the establishment of suitable framework of the entire research study. The research works helps in doing the research design in order to assemble the various objectives depending on the research study. The researcher helps in doing the adoption descriptive nature of design depending on the present research study (Zurigat, Sartawi & Aleassa, 2014). Therefore, the reason in doing the adoption of the descriptive design for this particular research study helps the researcher in the terms of doing the relation of various findings of the research study concerning both the aims as well as objectives. The descriptive nature of the research study also helps it support the researcher with the various research questions including who, what, when and whom of this particular research study. Therefore, due to the descriptive nature of the research designs mainly relates the analysis of quantitative data. The different collected data are evaluated in doing the data analysis sections with the help of various diagrams, graphs, charts etc. Apart from this, this particular research study requires the in-depth description in the terms of doing the conclusion about the various concerning issues regarding big data.  


Sampling method

At the time of doing this particular research study, the techniques of doing sampling is becomes vital purpose, particularly during the gathering various information of primary data with the help of the survey. Moreover, the probability of doing the sampling procedure mainly follows the different aspects like cluster sampling, random sampling and stratified sampling. According to the aim and objectives of this research study, the researcher needs to deal with both the primary as well as secondary data (Zwitter, 2014). On the other side, the sample size mainly implies the numbers of the people who are surveyed in doing the research study. The sample size also depicts the various numbers of the respondents who are chosen to collect the primary data with the help of various questionnaire methods.  The researcher therefore, decided to take the 60 respondents as the sample size. It becomes easier to evaluate all the concepts with the various review questions.

Data collection method

The appropriate data collection method helps the researcher in the terms of doing the achievement of various expected results. Moreover, the process of doing the data collection varies from one research study to the other (Zwitter, 2014). The objectives of the research study helps in doing the evaluation of various impacts of big data in order to establish the link between the objectives of the research study. Furthermore, the precise of data collection also helps in providing the reliable and valid results based on this particular research study. 

The two different type of data collection methods are primary and secondary data collection methods. The primary collection method mainly evolves with the quantitative method of data analysis. On the other hand, the secondary data are collected from the various historical consequences of data sets.

Data analysis

In doing, the analysis of data with the help of appropriate analytical tools at the time of the data analysis supports the researcher in order to illustrate the research study. Mainly the selected methods of doing data analysis evolved with various numerical data that are collected from various primary and secondary resources (Zurigat, Sartawi & Aleassa, 2014). Moreover, the strengths in doing the research study with the help of survey helps to test both the reliabilities as well as validity of different data. In addition, this particular technique of data analysis is more time consuming in order to collect as well as analyze the data.

Ethical issue

In order to discuss various ethical issues of the research study may follows various codes of different ethics. This particular code also helps in order to depict the researcher the process to conduct the research study.


This particular research study mainly concludes various feasibility aspects concerning the big data. It also helps in order to focus all the concerning issues associated with the analysis of big data. Moreover, in this research paper it also discusses about the various issues regarding the concepts of doing the data analysis as well as various ethical issues. This particular study also helps in doing the examination of various impacts of big data on the all the fields of M-Global. All the data needs to be more accurate which will helps in drawing various ideas concerning big data.


Reference List

Bettencourt, L. (2014). The Uses of Big Data in Cities. Big Data, 2(1), 12-22. doi:10.1089/big.2013.0042

Bulbulia, J., Sibley, C., & Wilson, M. (2014). Thin and Thinner: Hypothesis-driven Research and the Study of Humans1. Numen, 61(2-3), 166-181. doi:10.1163/15685276-12341314

Couldry, N., & Powell, A. (2014). Big Data from the bottom up. Big Data & Society, 1(2). doi:10.1177/2053951714539277

Dhar, V. (2014). Big Data and Predictive Analytics in Health Care. Big Data, 2(3), 113-116. doi:10.1089/big.2014.1525

Dhar, V. (2014). Big Data and the Rise of Machines in Financial Markets. Big Data, 2(2), 65-67. doi:10.1089/big.2014.1524

Harvey, N., & Porter, R. (2014). User-driven sampling strategies in image exploitation. Information Visualization. doi:10.1177/1473871614557659

Hooper, C., & Gunn, R. (2013). Recognition as a framework for ethical participatory research: developing a methodology with looked after young people. International Journal Of Social Research Methodology, 17(5), 475-488. doi:10.1080/13645579.2012.753980

Hoskins, M. (2014). Big Data 2.0: Cataclysm or Catalyst?. Big Data, 2(1), 5-6. doi:10.1089/big.2014.1519

Kitchin, R. (2014). Big Data, new epistemologies and paradigm shifts. Big Data & Society, 1(1). doi:10.1177/2053951714528481

Mangiameli, G. (2015). BMC Medical Research Methodology reviewer acknowledgement 2014. BMC Medical Research Methodology, 15(1). doi:10.1186/s12874-015-0003-4

Minnitt, D. (2014). Sampling 2014—What does academia get from Sampling and WCSB conference(s)?. TOS Forum, 2014(3), 21. doi:10.1255/tosf.27

Patrinos, H., & Savanti, M. (2014). The Screening Hypothesis and the Returns to Schooling in Argentina. Research In Applied Economics, 6(3), 28. doi:10.5296/rae.v6i3.5157

Smidth, F. (2014). Sampling 2014—an industry perspective. TOS Forum, 2014(3), 22. doi:10.1255/tosf.28

Sowe, S., & Zettsu, K. (2014). Curating Big Data Made Simple: Perspectives from Scientific Communities. Big Data, 2(1), 23-33. doi:10.1089/big.2013.0046

Steinhaus, R. (2014). Sampling 2014—A manufacturer’s perspective. TOS Forum, 2014(3), 23. doi:10.1255/tosf.29

Xu, Q., Zhao, Z., & Wang, W. (2013). Volume-Based Data Representation of Big Data Analysis. AMR,798-799, 680-684. doi:10.4028/

Zurigat, Z., Sartawi, I., & Aleassa, H. (2014). Empirical Investigation of Free Cash Flow Hypothesis: Evidence from Jordanian Capital Market. International Business Research, 7(3). doi:10.5539/ibr.v7n3p137

Zwitter, A. (2014). Big Data ethics. Big Data & Society, 1(2). doi:10.1177/2053951714559253


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