Online Tutoring on 7116IBA Data Resource Management

Overview

You are required to prepare an annotated bibliography related to the management of data as a strategic resource rather than an administrative process in an organisation. The planning and/or implementation of the information needs of an organisation are then viewed from a enterprise viewpoint and aligned with the goals and objectives of the organisation. The requirement is to research on this topic, and you can adopt a general viewpoint of this topic or choose a specific topic within this area of DRM. You will be required to critically analyse, summarise and integrate these articles in a report.

Your annotated bibliography should identify current thinking on DRM practices. You will then use this information to guide your thinking and reporting for this assessment.

Annotated bibliographies

These resources give you examples of annotations, but you also need to refer to the requirements below to ensure that you include all elements of this assignment.

Requirements

Format

  • Your paper should be an individually written Microsoft Word document.
  • As a Griffith university student, you have free access to Office 365 Education.

Structure

Your bibliography should include:

  • Brief introductionindicating the overall topic and approach to your annotated bibliography
  • Annotations of at least FOUR (4)refereed journal articles
    • Articles must be published in the last 10 years
    • Annotations include:
        1. purpose of the study
        2. main variables in the study
        3. short summary of findings
  • Discussionthat summarises and integrates the articles
    • You may use other references in addition to the 4 articles in this section
  • Recommendationsthat link evidence to practice
  • Reference list
    • Begin the reference list on a new page
    • Not included in word count

Word limit

  • 2000 words(+/- 10%)
  • Reference list is not included

Title:

Annotated Bibliography

Description:

This is a individual assignment that will give the students an opportunity to research the topic in some depth. The report requires students to prepare an annotated bibliography on evaluating Data as a Enterprise Resource rather than just an administartive process. Students will be required to undertake research and select a minimum of 4 articles which will then have to be critically analysed, summarised and synthesised in a report.

Format:

Your paper should be an individually written report

Structure

Your bibliography should include:

  • Abstract
  • Brief introduction indicating the overall topic and approach to your annotated bibliography
  • Annotations of at least FOUR (4) refereed journal articles
  • Articles must be published in the last 10 years

Annotations include:

  1. purpose of the study
  2. main variables in the study
  3. short summary of findings

Discussion that summarises and integrates the articles

  • You may use other references in addition to the 4 articles in this section

Recommendations that link evidence to practice

Reference list

  • Begin the reference list on a new page

Word limit

2000 words (+/- 10%)

Reference list adn Abstarct are not included in word count

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Solution

Introduction:

Today, technology has its way in every sector around the world. The advent of technology has paved the way of different innovations that has improved previous mechanisms to better cater the needs of the target audience. This the same case with data resource management, this new concept has also emerged but the previous technologies aren’t efficient and accurate for the current scenario. Data resource management is the administration of data as a tactical resource instead of an administrative procedure in an enterprise. The application of data is done in accordance with the objectives of the firm. The report includes four annotated bibliography addressing the current practices of data resource management. Majority of the studies include Big Data technology that is an emerging tool in the field and has improved the previous technologies of Big Data. A discussion and recommendations are also given to provide an overview the bibliographies and research gap in them.

Annotated Bibliography:

Yu, L., Gao, W., An, Q., Zhao, J., Liang, D., & Gao, D. (2011). Data resource management according to customer requirements. Mathematical and computer modelling, 54(3-4), 895-901

This study includes the importance of data management keeping in mind the needs and requirements of a customer. The paper begins with providing the understanding of the data management that is the characteristics of the data resources that are heterogeneity and differentiation. There is an increase demand of the management of the data by different company but there aren’t good application systems to implement on the enterprise to administer data. The variables utilized by the paper are modulename, maxmid, resourcename and stablename.  The paper advised a modern application system that reduces cost, increases efficiency and decreases blocks. The advisory application system by the authors of this paper would do the above stated functions based on analysis of data resources, the lithe management and customization of data resources through the removal of strict coupling between data resources and information system data storage programs. This will provide lithe, translucent data resources management for firm application. The system designed by the authors would apply personal, local management and customize resource mechanism by enhancing the system litheness, reusability and implementation. The data resource system was applied for managing the health care resource mechanism in the medical organizations in rural areas of China with local traits. This mechanism will provide an increasing accessibility to medical sources and management procedures. Moreover, the paper included an algorithm with a descriptive detail on how the design would be implemented to formulate the mechanism in accordance to the customer requirements.

Dhar, S., & Mazumdar, S. (2014, June). Challenges and best practices for enterprise adoption of big data technologies. In 2014 IEEE International Technology Management Conference (pp. 1-4). IEEE.

The purpose of this study was to present the audience with the outstanding techniques of 3-legged Big Data ecological approach as well as with the issues of firm adoption of the tools. The main reason for conducting this study was to help the companies in managing the data easily so that they can take multiple decisions adequately. As well as, it is an important addition to the academic literature as there is not much research conducted on the topic. The paper provides an overview on the type of data, the need for the technique to be adopted by the companies and the characteristics of Big Data as well as the technique. The main variables used in the study are Big Data, Data Mining and Business analytics. Moreover, the paper provided different superlative stages of the main technique that can be used for Big Data management, the tool is a 3-legged approach that incorporates three stages. The first one includes adopting the technique through three environments that are developer community (minor cluster with restricted data), analyst community (small cluster but has a capacity for bigger data) and business community (a larger cluster that entails agile progress). The second practice advised by the authors is to construct a normal “abstraction layer” for data display, incorporation, conception and management to hide the intricacy of data miscellany, detach the security apprehensions and provide general channel of integration with the entire firm mechanism. The last stage is to produce an incorporated Big Data worktable and the procedure surrounding the same to administer the different environments and constructs. Nevertheless, the adoption of the Big Data is multiple enterprises is still not done efficiently. Moreover, Big Data in this era is a main practice to manage data resource that can also be useful in decision making for the organizations.

Wu, L., Yuan, L., & You, J. (2015). Survey of large-scale data management systems for big data applications. Journal of computer science and technology, 30(1), 163-183.

This paper also includes the application of the technology, Big Data on the different organizations of the community.  The purpose of the study was to present the readers with a clear mindset on how different data resources can by managed by the enterprises through the utilization of large scale management mechanisms. As per the literature provided by the paper the large scale management systems have extraordinary traits like flexibility, lower cost, scalability and manageability. The variables of this study are system architecture, scalability, consistency model and data model. The paper started with evaluating different data models on the bases of physical outlines and theoretical depictions. Moreover, the study also emphasized on the application and design of the mechanism architecture, the authors came up with “architecture taxonomies” for applying large scale database mechanism to categorize the mutual architectural designs and compare the ability of the system for scale out. Then the article compares and contrast two types of consistency frameworks and classified prevailing mechanisms in accordance to the corresponding arrangements. Through this mind mapping the authors attained insights into the exchange between scalability and consistency. At the last, the paper than detect principles for the application of large scale data management mechanism such as application of SEDA/MapReduce architecture to attain higher scalability, the enterprises shouldn’t opt for BASE because of its weak consistency and many other perceptions that have gained through the research of this article. Therefore, the paper focuses on laying an inclusive taxonomy model which negates the path towards the analysis of large scale data management mechanism for Big Data applications although it also provides principles and references for long term application.

Wang, S., & Wang, H. (2019). Big Data Resource Management in Business: A Multiple-Case Analysis. Journal of Information Technology Management., 30(1), 1-13.

The main purpose of the study is to present a business model for organizational big data resource management by analyzing many business cases. The themes of the paper include strategic use of data, design requirement, IT implementation and outcome. The paper gathers and evaluates sixteen business case studies regarding data resource management in the Big Data era. The article provides a descriptive literature review of the previous academic scholarly journals that provide business model addressing the Big Data resource management mechanism. The methodology employed by the article is a qualitative research method by investigating the data models about the business big data resource. Secondary qualitative research method was employed to come up with a good business data model. Along with the reviewing of the cases a Joint Analytical Process was utilized by the researchers for the data concluded to be reliable and valid. Not only this but coding of the data was also manually done so that the data can be divided without any biasness. The design of the business model entails strategic utilization of data, design requirements, IT implementation and outcome. The four strategies are interconnected with feedback loops and causal relationships. The former specifies the administration control in big data resource management. The paper also included some of its limitations like the range of the cases is not that big, the coding of the research method can entail errors. Subsequently, the theoretical business model advised in the article needs more authentication and confirmation. However, this model is a guide for future studies to improve it to better cater the needs of the enterprises trying to manage big data resource management.

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Discussion:

The research in the data resource management deliberates data as “raw material” which needs to be converted into information that can be called a final product. Moreover, the propagation of computer mechanisms, firms start to describe data as a resource that is “a means for production” (Otto, 2015, p. 235). The management of the data resource is measured as a firm task involving for instance, data architecture management, data access management and database management (Otto, 2015).

The annotated bibliography included one of the main topics of the area of data resource management that is Big Data. This type of technology is categorized by veracity, volume, velocity and variety. Big data is an evolving tool in the field of data resource management which attracts attention of multiple researcher to come with a smart technological product that connect the future generation. Moreover, this technological is increasingly used in the field of medicine where the medical institutions are paving their way to manage large scale data through Big Data management system (Bhatt, Dey & Ashour, 2017). The last three bibliographies included the technology of Big Data in the data resource management that will help the different enterprises either medical or internet based to manage large scale data. However, the previous technology of Big Data wasn’t accurate and efficient but the researchers have introduced different theoretical business models that can help the engineers and researchers to store large scale data. Moreover, this can help the companies to get knowledge about efficiently managing the data without any hassle.

Recommendation:

The previous conventional technologies of Big data are not that efficient along with they don’t have that flexibility that is required to manage large scale data by the firms. The performance and precision of the traditional Big Data technologies aren’t up to the mark. Many of the researchers have tried to improve the previous technologies that lack in accuracy and performance (Ossous & Belfikh, 2018). Moreover, the business models aren’t implemented practically in any insititutions but are only advised by different authors. The type of business models provided by the researchers only included an incomplete algorithm that was just for recommendation but doesn’t provide a practical solution. The discussion of the papers includes the limitations that the specific model needs to be improved by future authors so that application of the business model can be successful. On the other hand, the mentioned research papers don’t have a descriptive literature review rather they only have a brief one. This indicates that the academic research upon the topic isn’t available but that is not the case as there is abundance of research conducted on the specific issue. The studies mentioned above should include a good amount of literature review so that it seems that the study done is authentic and appropriate. Moreover, this areas needs attention of more scholars and academics so that a practical business model can be designed so that it can help the stakeholders that have to manage good amount of data resources. The need of a good application system is needed in the health care as well as in the internet based enterprises.

Conclusion:

In conclusion, data resource management needs attention of the engineers and academics for extensive research on this area to build a business model that can help the enterprise to manage large scale data easily. The Big Data technologies are needed to help the data resources to be managed easily and stored efficiently. The technology needs to be flexible and accurate inorder to provide best performance in haning the data.

References

Dhar, S., & Mazumdar, S. (2014, June). Challenges and best practices for enterprise adoption of big data technologies. In 2014 IEEE International Technology Management Conference (pp. 1-4). IEEE.

Wang, S., & Wang, H. (2019). Big Data Resource Management in Business: A Multiple-Case Analysis. Journal of Information Technology Management., 30(1), 1-13.

Wu, L., Yuan, L., & You, J. (2015). Survey of large-scale data management systems for big data applications. Journal of computer science and technology, 30(1), 163-183.

Yu, L., Gao, W., An, Q., Zhao, J., Liang, D., & Gao, D. (2011). Data resource management according to customer requirements. Mathematical and computer modelling, 54(3-4), 895-901.

 

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