Data Management In Organizations

Developing Data Management

1. Data Management

a. Concept Map

In an organization, to generate their revenues data management plays a very important role in those organizations to mitigate risks and control costs. The increasing amount of data from an organization are protected, stored ad protected are successfully done by data management (Mao et al., 2015). Data management helps to improve the competitive advantage in business environment of an organization.

b. Discussion 

To make revenue in an organization, data management plays an important role. Data management helps to mitigate risks and control costs. Data management helps to share, retrieve and protect amount of data in an organization. To define the element of data, the structure of data, the way they are moved and stored are mainly focused by the data management (Costello & Wieczorek, 2014). Data management is concerned with accuracy, timeliness, completeness and security for multiple amounts of data. To accountants of the organizations are trained to access data and also manages to help the organization (Murdoch, T. B., & Detsky, 2014). All organizations has abundant amount of data that is increasing day by day at an alarming rate. To make which data in the organization are most relevant, it is vital and essential for all organization. The senior representatives of the organization should perform classification and identification.

According to Schadt et al. (2010) in the article “Developing a Lean Data Management System for an Emerging Social Enterprise” data collection technique is very essential for all the organization that are operating in developing countries. All organization needs data collection. For all purposes like efficiency improvements, research purposes or traditional evaluation and monitoring, collection of data is needed (Cox & Pinfield, 2014). By implementing proper tools, integrating data and design protocols in an organization, data collection is an important aspect of data management system. As described in this article, all organizations are arranging approaches to reduce the inefficiencies so that they can become famous among other researchers and entrepreneurs. In another article “Computational solutions to large-scale data management and analysis” Obeysekare, Marucci & Mehta (2016) stated that this world is evolving with huge number of new technologies. New technologies in the field of genomics which includes third generation technologies, image systems and flow cytometry that are spectrometry based generates large amount of data. These data helps to monitor genes in large amount, sequence the genome in human and also helps to score large number of SNP’s in each samples. Costello & Wieczorek, 2016 explained that there are some challenges that come from data analysis in large scale. These are: Data management, data transfer and access control faces challenge of data management as because large amount of data those are gathered increases the size of raw data. All these data are to be stored securely and is required to transfer these data around the internet. The data can be stored centrally so that to reduce their storing cost. The second challenge that is faced is to standardize the data formats. Different formats of data are generated by different centers and to synchronize them is a very difficult task. As different centers uses different ways to format data, time is wasted in re-integrating and reformatting the data several times. The third challenge faced to manage the data sis to model the results. Biological researchers have to integrate large data to build a model. So, if data management is not done properly, it is difficult for them to construct the model. In this article Wu, Battle & Madden (2015), also proposed some ways to mitigate the challenges.  The first mitigation that is proposed in this is to understand the computational problem. To address the big data and the computational challenges that are faced by an organization requires limited resources like power, people, space and money to solve the issues. The nature of data is exploited and understood in this computational process. Halperin et al. (2014) stated that computational problem can be solved by solving particular problem such as size and the complexity of data, the easiest way to transfer data over internet. It is also taken care of that the algorithm must be simple and whether the algorithm that is used can be parallelized. The computational solutions that are needed to integrate the data of new generation needs a kin approach in quantitative disciplines, climatology and in physics which have many set of large data (A, 2013). Computational environments and cloud computing is new invention that can mitigate problems that are related to access control, data management and data transfer.

According to the article proposed by Martin Courtney, electricity, water and gas suppliers of Europe homes finds way to detect large data volume to generate their smart phones mainly to gain insights among customer’s trends and also in operational efficiencies. Accurate assessments are very difficult to achieve. Smart meter helps to take all the gigabytes of data that are generated by infrastructure. In another journal “Self-Adaptive Context Data Management in Large-Scale Mobile Systems”, Mario Fanelli defined data management as mechanisms that need to represent, replicate and store data in mobile system. The scalability of system is improved and reduced the access time by the data management system (Aluç et al., 2014). Data management exploits storage architectures that are complex which includes mobile devices and physical servers. According to Fanelli 2016 data management is efficient and organizes the data effectively in the system so that the context data is kept as close as possible (Garofalakis, Gehrke & Rastogi, 2014). Data management provides mechanisms that are needed to represent, replicate and store data in mobile systems.

• What is the general subject discipline to which the research data relates?
• What is the exact nature of the research data?
• Who will own the data arising from your research, and the intellectual property rights relating to them?

Name:

	Your comments (Enlarge width)	Out of	Grade
Copy from Activity A2
Paper reference (APA):	Schadt, E. E., Linderman, M. D., Sorenson, J., Lee, L., & Nolan, G. P. (2010). Computational solutions to large-scale data management and analysis. Nature Reviews Genetics, 11(9), 647-657. Obeysekare, E., Marucci, A., & Mehta, K. (2016, October). Developing a lean data management system for an emerging social enterprise. In Global Humanitarian Technology Conference (GHTC), 2016 (pp. 54-62). IEEE.
Paper overview · A two paragraph summary of the paper (copy from previous activity)	All organization needs data collection. For all purposes like efficiency improvements, research purposes or traditional evaluation and monitoring, collection of data is needed. By implementing proper tools, integrating data and design protocols in an organization, data collection is an important aspect of data management system. As described in this article, all organizations are arranging approaches to reduce the inefficiencies so that they can become famous among other researchers and entrepreneurs. In another article “Computational solutions to large-scale data management and analysis” the authors stated that this world is evolving with huge number of new technologies. New technologies in the field of genomics which includes third generation technologies, image systems and flow cytometry that are spectrometry based generates large amount of data. These data helps to monitor genes in large amount, sequence the genome in human and also helps to score large number of SNP’s in each samples.
Checklist		3
1. Use a 5 point scale ( (1[outstanding]-5[needs significant work] or N/A. Explain your ratings a. Relevance to your topic area (1-5) b. Originality (1-5) c. Clarity (e.g. readability, structure and organization, standard of English, flow between sections): (1-5) d. Appropriateness of Research/study method/methodology (1-5)	a. The articles consists of process of data management in organizations. b. All the data that are analysed in these articles are real and orginal because all the research are qualitative and quantitative in nature. c. There is a clear understanding in english, structure of all the organizations that are mentioned in both these articles. d. The research is mainly about the qualitative and quantitaive research on the organizations that are studied in the organization.
Value added to the field: (Check as many as appropriate and explain your ratings) q New information q Valuable confirmation of present knowledge q Clarity to present understanding q New perspective, issue, or problem definition q Not much q Other	q The new information that was analyzed was how to organize the data in an organization to stable itself in the market. q The knowledge that was present very imformative. q The understanding that was presented in both the articles presents clear understanding.
Publication details explain – You will probably need to go to the source · Review type (Editor, Blind, open) · Publication status/rating	· The review type that both articles presented were open and edited. · Article 1: Published in 2010 Article 2: Published in 2016
Paper composition
Context (Title, Abstract, Introduction)		4
Title · Is it appropriate, does it describe the paper?
Abstract · Does it reflect what was done? The abstract is not the start of the introduction.  It should be able to be read independently of the paper, and the paper independently of it.
Introduction · Clearly states the research question and the background to the problem being researched. Justified why the research is important or significant.
Research (Method, Results, Discussion/Findings)		4
Methodology · Identify and describe how the research was conducted.
Results · Do the results provide a clear description of the findings of the research? Identify quantitative/qualitative data collection/analysis.
Discussion/Findings · Are claims are supported by the results? · Is ethics approval mentioned and included in the discussion? · Are limitations discussed (e.g. survey size)?
Reflection and Referencing		4
Future work · Does the paper identify “where to from here?” · How could you extend this research?
Conclusion · Clearly describes conclusions from the findings and indicates that implications, no new information added
Referencing · Does it reference other work appropriately · Do they include/cite research important to the topic · Are the references current?
		15

References

A. S. (2013). The inevitable application of big data to health care. Jama, 309(13), 1351-1352.

Aluç, G., Hartig, O., Özsu, M. T., & Daudjee, K. (2014, October). Diversified stress testing of RDF data management systems. In International Semantic Web Conference (pp. 197-212). Springer, Cham.

Costello, M. J., & Wieczorek, J. (2014). Best practice for biodiversity data management and publication. Biological Conservation, 173, 68-73.

Cox, A. M., & Pinfield, S. (2014). Research data management and libraries: Current activities and future priorities. Journal of Librarianship and Information Science, 46(4), 299-316.

Fanelli, M., Foschini, L., Corradi, A., & Boukerche, A. (2014). Self-adaptive context data management in large-scale mobile systems. IEEE Transactions on Computers, 63(10), 2549-2562.

Garofalakis, M., Gehrke, J., & Rastogi, R. (Eds.). (2016). Data Stream Management: Processing High-Speed Data Streams. Springer.

Halperin, D., Teixeira de Almeida, V., Choo, L. L., Chu, S., Koutris, P., Moritz, D., … & Xu, S. (2014, June). Demonstration of the Myria big data management service. In Proceedings of the 2014 ACM SIGMOD international conference on Management of data (pp. 881-884). ACM.

Mao, R., Xu, H., Wu, W., Li, J., Li, Y., & Lu, M. (2015). Overcoming the challenge of variety: big data abstraction, the next evolution of data management for AAL communication systems. IEEE Communications Magazine, 53(1), 42-47.

Obeysekare, E., Marucci, A., & Mehta, K. (2016, October). Developing a lean data management system for an emerging social enterprise. In Global Humanitarian Technology Conference (GHTC), 2016 (pp. 54-62). IEEE.

Santos, D. F., Silva, M. F. M., Sales, C. S., Sousa, M. J., Fernandes, C. S., & Costa, J. C. W. A. (2015). Data Management System for Structural Health Monitoring. IEEE Latin America Transactions, 13(4), 1090-1097.

Schadt, E. E., Linderman, M. D., Sorenson, J., Lee, L., & Nolan, G. P. (2010). Computational solutions to large-scale data management and analysis. Nature Reviews Genetics, 11(9), 647-657.

Wu, E., Battle, L., & Madden, S. R. (2014). The case for data visualization management systems: vision paper. Proceedings of the VLDB Endowment, 7(10), 903-906.