Linking Organizations & Groups in a Growing City: Establishing Inter-Group Associations - Essay Sample

Introduction

This project proposal aims to establish an association between different organizations and groups in the city. The city is medium-sized with a steadily growing economy and has inadequate resources—the citizens of the city affiliates with diverse groups and activities with different needs. There exist no data models that would establish the association between groups and events; hence it is difficult for the citizens to collaborate on projects and create efficiency given the scarcity of resources. The goal of the data analytic initiative proposed in this project will be to identify the links between various groups and enhance collaboration between the town's activities and projects.

The municipal is not using data analytics to enhance the citizens' decision-making and productivity, which justifies the need for data analytics initiatives that will allow understanding of different groups of people in the town using data mining models (North, n.d). The use of data mining models will also identify the connections and attributes of the groups and, consequently, help Roger develop an association rule model that will allow efficiency regarding project execution and completion (Zhang & Wu, 2011).

Data Sources

Currently, the organizations' surveys are the data sources used in this town. The main benefit of surveys is that they can obtain extensive data from many people and collect a broad range of data that aligns with the communities and their needs (Mcleod, 2014). However, the surveys used by Roger do not ensure the confidentiality and privacy of respondents. In that regard, this project proposes an increase in member privacy to improve data accuracy. The current initiative will protect the individual respondents' privacy and enhance anonymity, thus improving the data's efficiency.

Data Needs

Among the data needs identified for enhancing the understanding and needs of the people in the town are the demographic attributes of the organizations' members. At the top of the list of demographic characteristics is the age and education of the members. Age and education are critical attributes when looking for shared interests and needs. Rogers will employ this information to identify and associate different age groups and levels of training with their pertinent needs, thus enhancing efficiency, enhancing service delivery, and other businesses in the town. Additional data needs include gender, occupation, and other affiliation, such as social club, political, support, religious, and professional groups (Gutting, 2015).

Data Analytics Initiative

Given the data need and goals, the information obtained from surveys will be sufficient to understand the organizations' capacity and their needs to enhance productivity (Chung, 2015). In that regard, the data analytics initiative should develop a shared data source. Shared data sources and analytics methods such as regressions and decision trees, will generate associations between different groups and organizations that can be used to optimize the organization's collaboration, resource sharing, and execute other collaborative projects such as environment cleaning (Loshin, 2013). The initiative will also be valuable in creating connections and links between marketing, sales, and customer service teams, hence improving the efficiency and effectiveness of the operations (Kuhn, 2018). Additionally, the initiative will be valuable to Roger's mission of improving decision making informed by data.

Proposal

Goals

The initiative's objective is to develop the association rules and framework that will show the associations between the groups or associations and other attributes of groups, such as religious affiliation in the community. It aims to create models that will find existing connections between different data stores and merge the similarities into a single platform to enhance decision making (Walker, 2015). Also, it will purpose to reduce the time spent on decision-making and research in every organization by 50%, improve project collaborations by 10%, and enhance organizational productivity. This metric will be critical to measuring initiative success. In that regard, the initiative will be in line with Rogers's objective of establishing the group relationships that will be used to enhance group interactions and collaborations.

Data Analytics Life Cycle

The first phase of the data life cycle is the determination of the data type available (Hussain & Roy, 2016). Given the project initiative, this phase will entail describing the kind of demographic data available and various demographic attributes. During this phase, the data is collected using data collection tools and organized into multiple aspects such as categorical data and continuous data, which will help develop statistical models. Given the nature of demographic data, the majority of the variables will be categorical. The second phase will be the data preparation phase, which manages and cleans the data (Morabito, 2015). It involves designing questions that will guide the analysis models. The third phase of the cycle is the model development and creation of an algorithm that will analyze data to generate inferences automatically (Regier, 2018). The algorithm will be designed based on the project questions to ensure that the model makes the correct associations. The fourth phase involves the evaluation of the model using both mathematical and logical techniques to validate its accuracy, which will be pertinent to the initiative aim of identifying the right associations to be used in decision making. This phase will also involve a pilot study to identify any anomalies of the model. The final stage will be to deploy the model to generate associations between variables for decision making (Coleman, 2015).

Value of Life Cycle

The lifecycle will be critical in determining the accuracy of the generated associations and inferences. It will be particularly pertinent in creating the model which best aligns with the type of data available, and the project aims guided by initiative questions. Also, evaluation of the model will ensure accuracy as well as performance standards are maintained at the right standards (Boloyen, 2014).

Data

The data available entails the demographic characteristics of members, including age, gender, occupation, number of family members, and type of organization. The data is categorical, and it's stored in the spreadsheet. There are no names of the respondents which support members' privacy as well as allow public sharing. Data coding makes it easy to run a descriptive and inferential analysis of the demographics. However, the data will be more insightful and applicable to the initiative if it incorporates other personal attributes such as affiliations, job preferences, health information, how people spend their time, and their interests (Rose, 2016). Data insights align well with Roger's need to understand the community to allow the generation of informed decisions.

Tool Applicability to Initiative

The data tools will be critical in ensuring smooth data analytics operations from collection to generation of insights using inferences. The current data collection tool is a monkey survey. However, the tool does not support data synchronization, security, and access by other third parties; thus, more robust data collection tools such as KoBo Toolbox will facilitate the needs of the proposed initiatives (Stock & Watson, 2007). The tools for data management and evaluation will also be critical to the achievement of the proposed initiative. The available tools include R Studio, Excel, SAS, Microsoft Power BI, IBM’s BlueMix, and Qlik. Excel will be an excellent tool for generating pivot tables, descriptive statistics, and charts that are easy to interpret. However, it is not as powerful as R Studio, which is excellent in handling large sets of data. Additionally, R studio can generate all the descriptive and inferential statistics as well as interactive charts using R markdown, which is integrated by the model algorithm. Also, Power BI will be used to generate dashboards and visuals, which will give better insights regarding the associations between different variables (Guo, Lai, Shek & Wong, 2017).

Tool Applicability to Data

Excel Spreadsheet stores the data, making it easy to conduct analysis using Excel functions and Power BI. Power BI is an assortment of digital systems, connectors, and apps that function in unison to turn data sources into clear, physically engaging and interactive graphics that anyone at all levels will recognize. (Microsoft, 2019). Additionally, R Studio will generate the same analysis. It can handle a more extensive set of data than Excel spreadsheets and Power BI, with the only limitation being more time analytics coding. Additionally, R Studio can easily manage the data using various packages to fit the analytics and data requirements (Runkler, 2020).

Tool Recommendations

Given the initiative goals and the nature of the data, this project proposes the use of R Studio and Excel for data analytics. R Studio's choice and reason are that it can generate robust results, handle a large set of data, efficiently manage the data using codes and installed packages, generate great visuals, and be cost-effective (Adriaans & Zantinge, 2009). Additionally, R Studio is flexible and can analyze different types of data. Excel will be the support tool for R Studio to generate interactive charts for data visualization and interpretation (George, 2018).

Conclusion

Value

Data analytics will provide valuable insights into the attributes and interactions of different groups in the community (Forbes, 2019). Thus, decisions regarding resource allocation and project development will be data-informed. Additionally, Roger will gain insights into how the different organizations can collaborate and share resources to enhance productivity and efficient utilization of city resources using associations and inferences generated by the initiative model. Finally, Roger will identify new economic and social opportunities by studying and learning new trends.

Insights

The initiative will be pertinent to the developmental goals of the town in several ways. First, it will give insights on how to better utilize the town resources given the associations between different organizations or groups, trends, and interests of the communities (Mijungu, 2018). Additionally, the initiative will provide insights on key areas to develop, such as education, recreational facilities, health systems, or networks, among others responsible for the rapid growth of the economy. The community and municipality will understand various characteristics of the citizens (Krugger, 2019). The data will give insights on education level, age, family matters, and other demographic characteristics that will allow the development of policies for effective governance, community collaboration, and social cohesion. Also, essential insights from the data analytics will help the municipality cope with changes, make data-informed decisions, or create new opportunities for businesses, collaboration, or projects to develop further and enhance community cohesion.

Communication

Visualization

Data visualization and analytics will be valuable in communicating insights. R Studio and Excel will generate several visualization charts and graphs. Among the charts include histograms, stack bar charts, box plots, area charts, heat maps, and correlograms, which will be useful in showing trends and associations between different groups, organizations, activities in the community. Figure 1 below shows how charts can be crucial in communicating essential aspects of demographics in the city.

Figure 1

Sample Bar Graph

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