Data Visualization Model Report

Data Visualization Model Report

Nitro Mondo is an organization that is more determined in implementing modern data science strategies to address the ever-existing challenges associated with food insecurities and other problems related to food distribution. Therefore, it is important to recognize the importance of data science in Nitro Mondo's ability to handle food insecurity concerns. As a result, the organization's data scientists may use data visualizations such as maps, infographics, and dashboards to guarantee that people are aware of the variables contributing to food insecurity (Claes et al., 2018). More significantly, this data visualization model will enable the science team to collaborate with other data suppliers to get new insights about data deployment. As a result, the IT department and other staff have played a bigger role in the model's effectiveness.

Being one of the workforces has allowed me to utilize my academic knowledge in a real word situation. This report will discuss ways that the data science team can effectively put the model in action and ensure that the organization's clients are satisfied. Also, I will review the community's feedback regarding the model and evaluate how their feedback positively impacts the perfection of the visualization model. I used questionnaires, interviews, observation and revisited the feedback desk to gather the required information for the report. Questionnaires were filled by the organization's workforce and made observations on how the model impacted the organization's operations. I also held open interviews with the data science team, questioned the organization's partners, and reviewed the community's feedback and comments on refining the model for a better experience.

Specifically, I would use this data visualization model to assist current projects by collaborating with comparable organizations. In addition, I'd also use data visualization to disseminate information on Nitro Mondo's role in alleviating food insecurity in the community to all partners and stakeholders. The reasoning for choosing this model is to examine how global patterns and data transfer to the community level, guaranteeing that Nitro Mondo reaps the advantages of the chosen data rollout method (Po et al., 2020). Furthermore, the data deployment technique chosen would enable Nitro Mondo to analyze more states across the United States. Moreover, using this technique, Nitro Mondo will determine states that have similar data science capabilities; hence collaborating these states with would be critical in discovering new concepts that the organization can apply to address the food insecurity concerns.

According to the feedback, places like New Mexico, Texas, and Arizona, are enthusiastic about Nitro Mondo's initiatives. Obesity, food insecurity, poverty, and diabetes affect children and adults differently in various regions. Apart from these difficulties, these regions are influenced by a variety of additional variables. As a result, Nitro Mondo will identify how these challenges influence the community by distributing data. Nitro Mondo can utilize the input to assess the efforts required to harmonize the current statistics via community engagement campaigns and education programs. The feedback additionally reveals many strategies for improving Nitro Mondo's effectiveness (Xu et al., 2021). A representative from the organization should show how to interpret and analyze statistics in the data visualization model, such as generating charts and maps to show various initiatives being executed by the organization.  The organization could also compare the data to existing trends and patterns.

References

Claes, S., Coenen, J., & Moere, A. V. (2018, September). Conveying a civic issue through data via spatially distributed public visualization and polling displays. In Proceedings of the 10th Nordic Conference on Human-Computer Interaction (pp. 597-608).

Po, L., Bikakis, N., Desimoni, F., & Papastefanatos, G. (2020). Linked data visualization: techniques, tools, and big data. Synthesis Lectures on Semantic Web: Theory and Technology, 10(1), 1-157.

Xu, L., Francisco, A., Taylor, J. E., & Mohammadi, N. (2021). Urban Energy Data Visualization and Management: Evaluating Community-Scale Eco-Feedback Approaches. Journal of Management in Engineering, 37(2), 04020111.