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Rigor Qualitative Methodology Dissemination

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What Are The Aspects Affect Management Of Volume Of Big Data In Health Care Organization?

What Are The Effects Of Those Aspects On Health Care?

What Are The Loop Holes For Management Of Big Data In Health Care Organizations?

What Are The Recommendations And Solutions For Overcoming The Issues In Health Care Organization?




The purpose of this research project is to critically analyse and evaluate opportunities and challenges of using big data in business innovation. In particular the research narrow down to healthcare. There are various policies, procedures as well as practices in health care. These aspects are not similar in their considerations. There is a huge tremendous difference across all corners of the globe. Nevertheless, several objectives do not change irrespective of the systems in health care unit (Bates, Saria, Ohno-Machado, Shah & Escobar 2014).  These objectives include; upgrading patient experience, improving population of health as well as diminishing health care per capita. However, health care business is pressurized for containing expenses and promoting results of member. Big data offer very important asset in this sector. Health care aim to offer merits from various main developments in the field of data management and information technology. Data collection via electronic medical records as well as data sharing via information exchange system together with improvement of data analysis and data management need data warehouses and tools for data analysis. Moreover, utility of big data has number of merits as compared to health care system. Typically, utility of big data assist in finding as well as targeting the correct cohort of people. Again, proper intervention delivery process at the most appropriate point in time can be befitted by utilizing big data in business innovation and for this case in health care organization. Utility of big data helps in programs adjustments and loop closing in management of data. It also assist in keeping electronic health records safe for the patients and other stakeholders (Baro, Degoul, Beuscart & Chazard 2015). On the other hand, there are various challenges and demerits in using big data. To start with, decision making on data generated is a challenge, data privacy and consideration of ethical issues faced by end users while in the process of utilizing this application. Therefore, it is quite significant to take proper procedures which would be useful in overcoming various issues associated to utilizing big data applications in business innovation and particularly in health care system.


Project Objective.

The objective of this project need to be spelt out clearly so as to facilitate the research process. This involves determining opportunities and challenges of applying big data in health care organizations.  As health care organizations face various issues for big data applications as well as their implementation in daily operations, it is important to conduct a research on determining these issues as well as provision of recommendations so as to offer possible solutions so as to overcome these issues. In this particular outline, descriptive research design is very significant and we should employ it so as to determine a well initiated solution (Cerjan 2013). Moreover, positivism research philosophy is to be employed in the research project so as to assist in determining accurate results. Deductive research approach is also very important for the research project. This is the main reason as to why it is should be employed in the current research. 

This particular project would be useful in provision of solutions in so as to overcome these issues as well as challenges faced by the health care organizations for management of big volumes of data. This research project aims at finding out all factors relevant and responsible for management of big volumes of data in health care organizations (Chawla & Davis 2013). However, the main objective of this research project is to determine the loop holes and methods that can be applied to diminish factors that can result to effective and efficient operations of healthcare organizations. Therefore, the current research project can be used to strategize various options for healthcare organization and other ones in the business innovation systems. In addition, the project is very significant to assist managers and other staffs of the healthcare organizations so as to take effective and efficient measures in management of big volumes of data in the healthcare organizations. Still, the research project will open opportunities for further project research on related field.

Project Scope.

Big data management and implementation of associated technologies have brought numerous merits and opportunities in health care organizations. Nevertheless, the merits of big data management and applications are substantial and factual in nature. It therefore tend to remain a plethora of challenges that are need to address these issues. The main purpose of doing this is to fully realize and understand potential underlying big data management and implementation. Many of demerits are function of features of big data by the existing mechanisms and underlying models together with some demerits of present or rather the current data processing system in healthcare organizations. There are several extant study research that cover demerits in big data application in health care organizations. The current situation, healthcare organizations are facing various issues so as to be able to manage big data application based on decision making and judgments together with other factors (Davenport 2014). Hence, viable and ultimate solution is in need for the healthcare organization so as to eliminate these issues that can be built by employing proper mechanisms and strategies for healthcare organizations.

Literature Review

This particular part of this research project aims on various factors of research project concerning various aspects of management and implementation of big data volumes in this research project. Thus, in the process of performing this project, there have been prior attempts to concentrate on various theories and statistical models that have a perfect positive correlation with the present project (Fisher, Bishop, Fawcett & Magassa 2014). The project research thesis has been asserted as “evaluation of opportunities and challenges of using big data volumes in Healthcare organizations.  The core motto for this project is to offer better impacts for the project. Therefore, appropriate comprehension of the project and enhancement of knowledge and skills would be helpful in having effective and efficient termination of the project via construction of accurate results for the project (Gioia, Corley & Hamilton 2013). In addition, analysis of critical aspects would be important so as to reconsider models and theories with an objective of processing reflections of the literature gap. Still, there have been prior attempts to investigate appropriate loopholes and gaps within these theories and models which are applied and need more extensive research together with the purpose of achieving better results as per desired and anticipated results in healthcare organization application of big data.


Gaps opportunities of managing big data in healthcare organizations.

Management of big data application systems have been producing hype health care organizations. There are a several scenario by which healthcare is appropriately suited for big data management and implementation solutions. Several research project are aiming on the healthcare organizations on the experiment of applying of big data volumes in management and implementation in daily operations of activities in health care units (GOK & GOK 2016). Therefore, it emerge a perfect issue to struggle with various complexities of big data volumes in health care organizations. In the current project, addressing these complexities have been asserted so as to simplify big data. In 2003, the 8 vs. were coined to offer clear definition of big data volume, variety and velocity. The data analysts are required on simplistic as well as aspects to do for variability together with C for complexity and veracity. 

Under health care organization system, the huge volumes of big data have to be managed. EMRs have capabilities of collecting large quantities of data. Thus, a lot of data is typically collected from and for recreational purposes. On the other hand, velocity and volumes of data in health care organization systems require to be managed with appropriate mechanisms and measures (Harland 2014). Therefore, a range of majority of collection data require to be managed accurately and appropriately in the health care organization system. Even if the data requires to be estimated down of the track as number of applied cases increase, the various important application cases of the data need to be determined using reliable measures. There is a range in data for many of the systems such as in health care unit. Authentication of privacy and security of patient data need to be managed in health care organization. Health care organizations have to take employ appropriate procedures so as to enhance proper security of big data (Hashem, Yaqoob, Anuar, Mokhtar, Gani & Khan 2015). Typically, it flows on the open source technology. The alternative of choosing the research are needed to be implemented in health care organization. Hence, appropriate strategies and mechanisms would be very significant so as to achieve competitive advantages for health care organization as well as filling gaps available in management of big data.


Statistically, a hypothesis refers to claims that have not been proved. It can either be a null hypothesis or alternative hypothesis.

Null hypothesis. Statistically this is denoted as H0.

H0: The volume of big data has impact on health care organization.

Due to existence of huge amounts of data concerning a particular patient and services offered by health care organization, there is a need to process this data (Sagiroglu & Sinanc 2013). This has a significant implication on health care system. Therefore, it is needed to aim on the matter and apply system which will reduce the issue.


Alternative hypothesis. Statistically it is denoted as H1.

H1: The volume of big data does not have impact on health care organization.

Under health care organization, it is very significant to process huge amount of data (Trainor & Graue 2014). Therefore, health care organization employ proper strategies to manage the big volume of data. It results to no implication on the health care organizations daily operations.

It is very crucial to choose proper methods of research in a project.  Typically, there are three aspects of research philosophies. They include realism, interpretivism and positivism. Quantifiable observations are employed in positivism philosophy. Hence, analysis of statistical aspects occur under positivism philosophy. Moreover, positivism philosophy is based on scientific test. In this research project, positivism philosophy have to be applied in the project for applying factual knowledge and also lead for hypothesis testing and research questions (Trainor & Graue 2014). Therefore, it is significant to realize the purpose of personnel for management of big data in the research project as per the expectation of health care organization. However, both interpretivism as well as realism philosophy are ignored in the present research project.  The method of data collection used was survey and observations. The independent variables were the data, workers in health care organizations and patients under treatment.

Samples for the survey and questionnaire strategies are basically collected from population according to particular criteria. For instance in the current project research, personnel involved in management of volume of big data in the in health care organization are chosen for the research as sample (Vidu, Schubert, Muñoz & Duque 2014). 200 workers are approached to cite their take on aspects faced by them in the process of management of volume of big data in the research project. Out of them 85 workers in health care organization agreed to offer their take on the topic of the project research.

Research design.

Typically, there are three kinds of research designs such as descriptive, exploratory and explanatory. Exploratory research design is based on description on causes of particular phenomena as well as prediction of future occurrence of phenomena. Under exploratory research, the main concern is problem formation, hypothesis together with clarification of several concepts in health care organization. Descriptive design is based on alternatives of explanatory and explanatory design. It majorly emphasizes on describing features of the sample and data collected (Wamba, Akter, Edwards, Chopin & Gnanzou 2015). In this project, descriptive research design is applied so as to clarify research topic. In addition, so as to realize the challenges faced by workers in health care organization in management of volume of big data, descriptive research design is preferred .It is applied to realize challenges faced by employees during management of big data in health care organization. Therefore, descriptive design is selected in this project while explanatory and exploratory designs are discarded.

Research Limitations

The major limitation in the health care organizations is management of data structure, data security, standardization of data, data storage as well as process used to transfer data. Moreover, data governance is also a limitation in the research under health care organizations. Again, this kind of research require access to private data in health care organization (Zhang, Qiu, Tsai, Hassan & Alamri 2015). This kind of information may include special records of patients under consideration. Health care ethics do not allow provision of such data to the public. This limited further research in the topic under consideration.



In, conclusion, it is important to note that there are many gaps and opportunities as well as challenges faced in using big data in health care organizations. The mechanisms and strategies asserted in the context need to be applied in provision of appropriate and accurate results and solutions. It is good to note that utility of big data helps in programs adjustments and loop closing in management of data. It also assist in keeping electronic health records safe for the patients and other stakeholders (Zaslavsky, Perera & Georgakopoulos 2013). Health care aim to offer merits from various main developments in the field of data management and information technology. Data collection via electronic medical records as well as data sharing via information exchange system together with improvement of data analysis and data management need data warehouses and tools for data analysis. Moreover, utility of big data has number of merits as compared to health care system. Typically, utility of big data assist in finding as well as targeting the correct cohort of people in health care organization. In summary, this topic has a greater application in the world of research today and in future.



Baro, E., Degoul, S., Beuscart, R., & Chazard, E 2015, “Toward a literature-driven definition of big data in healthcare”. BioMed research international, 2015.

Bates, DW, Saria, S., Ohno-Machado, L., Shah, A., & Escobar, G 2014, “Big data in health care: using analytics to identify and manage high-risk and high-cost patients”, Health Affairs, 33(7), 1123-1131.

Cerjan, C 2013, Numerical grid methods and their application to Schrödinger’s equation (Vol. 412), Springer Science & Business Media.

Chawla, NV, & Davis, DA 2013, “Bringing big data to personalized healthcare: a patient-centred framework”. Journal of general internal medicine, 28(3), 660-665.

Davenport, T 2014, Big data at work: dispelling the myths, uncovering the opportunities, Harvard Business review Press.

Fisher, KE, Bishop, AP., Fawcett, P., & Magassa, L 2014, “InfoMe: a field-design methodology for research on ethnic minority youth as information mediaries”. In New directions in children’s and adolescents’ information behaviour research (pp. 135-156). Emerald Group Publishing Limited.

Gioia, DA., Corley, KG., & Hamilton, AL 2013. “Seeking qualitative rigor in inductive research: Notes on the Gioia methodology”. Organizational Research Methods, 16(1), 15-31.

GOK, T., & GOK, O 2016, “Methodology of Research”, Asia-Pacific Forum on Science Learning and Teaching (Vol. 17, No. 1).

Harland, T 2014, “Learning about case study methodology to research higher education”, Higher Education Research & Development, 33(6), 1113-1122.

Hashem, IAT, Yaqoob, I., Anuar, NB., Mokhtar, S., Gani, A., & Khan, SU 2015, “The rise of “big data” on cloud computing: Review and open research issues”, Information Systems, 47, 98-115.

Sagiroglu, S., & Sinanc, D 2013, “Big data: A review”. In Collaboration Technologies and Systems (CTS), 2013 International Conference on (pp. 42-47). IEEE.

Trainor, AA, & Graue, E 2014, “Evaluating rigor in qualitative methodology and research dissemination”, Remedial and Special Education, 35(5), 267-274.

Vidu, A., Schubert, T., Muñoz, B., & Duque, E 2014, “What students say about gender violence within universities: Rising voices from the communicative methodology of research”, Qualitative Inquiry,20(7), 883-888.

Wamba, SF., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D 2015, “How ‘big data’can make big impact: Findings from a systematic review and a longitudinal case study”,International Journal of Production Economics, 165, 234-246.

Zaslavsky, A., Perera, C., & Georgakopoulos, D 2013, “Sensing as a service and big data”, ArXiv preprint arXiv: 1301.0159.

Zhang, Y., Qiu, M., Tsai, CW., Hassan, MM, & Alamri, A 2015, “Health-CPS: Healthcare cyber-physical system assisted by cloud and big data”,IEEE Systems Journal.


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