Research Paper- Social Media Analytics for Competitive Advantages

Research Paper- Social Media Analytics for Competitive Advantages

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In disseminating the research paper, various electronic databases will be researched ACM Digital Library, InfoSci-Journals as well as LearnTechLib to identify the potential study for inclusion. The research will allow for formulating an explicit framework that will be designed for guiding the dissemination activity. Three reviewers will screen the references of the paper, and the disagreements that arise will be discussed. The source, the date of publication, description for the major topics of the framework will be addressed. Furthermore, explicit or implicit references that relate to the underlying argument presented by the paper will be extracted. Additionally, a narrative synthesis of the central concept of the paper will be conducted.

Upon identification of the audience, the developed content published. The findings will be later released into the national journals as well as the databases for technical articles. Furthermore, the article will be presented at national conferences. The topic will also be posted through the mainstream media, while summarized reports will be used to seek funding. The key audiences will include schools, local government, universities, technology firms, as well as the state and regional extension offices.

Discuss How Visual Analytics Supports Exploration, Discovery, and Complex Reasoning About Data and Data-Driven Problems.

Visual analytics alludes to a bod of knowledge that enables individuals to employ techniques that have interactive visualization algorithms as well as methods of analyzing data in light of supporting the analytical reasoning that facilitates decision making. It is used in diverse areas like science, business, engineering, and government.

Visual analytics allows for the drawing of data from sources like Relational Database Management Systems (RDBMS). It combines automatic and visual analysis techniques with human interactive exploration. Visual analytics presupposes that combining the quantitative capabilities of the computer system and the social cognitive capabilities yields into powerful means in knowledge creation. Thus, interactive exploration and visual manipulation are crucial to visual analytics. However, Raghavan, Gudivada, Govindaraju, and Rao (2016) fails to explore the level of human capability necessary for the creation of knowledge. Hence, further research is required to determine the level of the social-cognitive skills needed for generating knowledge.

Alternating between visual and automatic methods allows for a continuous refinement validation of the preliminary results. Thus, misleading results in an immediate step of the analysis can be discovered at an early stage using the data analytics techniques. This factor guarantees enhanced results and builds higher confidence in the data and data-driven problems. During the process, if data exploration is conducted, then the user is required to confirm the generated hypothesis through an automated analysis hence allowing for the discovery of knowledge. However, Keim, Mansmann, Oelke, and Ziegler (2008) fails to portrays how the discovered knowledge can be weighed against the formulated hypothesis to ascertain the discovery of knowledge. Hence, further research is required to explain this relation.

Finally, Stopler, Perer, and Gotz, (2014), suggest that the visual analytics allows for decision-makers to combine their creativity, flexibility as well as the background knowledge with the results generated to gain insight into complex data and data-driven problems. However, the study does not account for the level of creativity or the background knowledge required to facilitate effective decision making about complex data. Hence, further research is needed to help us understand how creativity, flexibility, and background knowledge can be applied to support decision-making.

References

Keim, D. A., Mansmann, F., Oelke, D., & Ziegler, H. (2008, October). Visual analytics: Combining automated discovery with interactive visualizations. In International Conference on Discovery Science (pp. 2-14). Springer, Berlin, Heidelberg.

Raghavan, V. V., Gudivada, V. N., Govindaraju, V., & Rao, C. R. (2016). Cognitive computing: Theory and applications. Elsevier.

Stolper, C. D., Perer, A., & Gotz, D. (2014). Progressive visual analytics: User-driven visual exploration of in-progress analytics. IEEE Transactions on Visualization and Computer Graphics, 20(12), 1653-1662.