| Course | DAA-7030 Data Analytics and Visualization with Capstone Project |
|---|---|
| Module | Module 4 |
| Paper type | Written assignment 2: data analytic software |
| Length | About 510 words, 4 pages |
| Format | APA 7 student paper |
| School | Thomas Edison State University |
| Program | MBA in Data Analytics |
| Updated | October 2026 |
Free sample paper for DAA 7030 Module 4
The Right Tools for a First Step: Choosing Analytics Software for a Six-Studio Chain
Student Name
MBA in Data Analytics, Thomas Edison State University
DAA-7030: Data Analytics and Visualization with Capstone Project
Mentor Name
Month Day, Year
The Right Tools for a First Step: Choosing Analytics Software for a Six-Studio Chain
Where the Chain Stands
Chuah and Wong (2012) propose an enterprise business intelligence maturity model that integrates earlier models and describes stages from initial, ad hoc use of data to optimized, organization-wide BI, assessed across dimensions such as data, technology, people and process. Our chain sits at the initial stage: data live in a booking system, a payment processor and spreadsheets; no one is trained in analytics beyond me; and reports are built by hand. The right software for this stage is software one analyst can use, document and hand over, not an enterprise platform.
Comparing Options
Spreadsheets fail the reproducibility test: the midterm analysis took twenty manual steps that no one could repeat reliably. Both R and Python could do the work. R has strong packages for reading Census data, cleaning tables, clustering and mapping, and the course's tutorials use it. Python is more general but offers no advantage for this project and has a steeper start for colleagues who may inherit the work.
| Option | Cost | Skills needed | Reproducible | Sharing with owners |
|---|---|---|---|---|
| Spreadsheets | Already owned | Low | Poor; steps done by hand | Familiar but static |
| R with RStudio | Free | Moderate | Strong; scripts rerun | Through reports or a dashboard tool |
| Python with notebooks | Free | Moderate to high | Strong | Through reports or a dashboard tool |
| Visualization platform A | About $900 per user a year | Low to moderate | Moderate | Interactive dashboards |
| Visualization platform B | About $120 per user a year | Low to moderate | Moderate | Interactive dashboards; integrates with office software |
The Choice
I will use R scripts, maintained in a shared folder with comments, to collect the Census data, clean and join it, run the clustering and produce maps and charts, so the capstone's backend can be rerun as the syllabus requires. For the owners, results will be published to visualization platform B, the lower-cost option that works with the office software they already use, as an interactive dashboard with three views: the cluster map, candidate ZIP codes and monthly studio performance.
Mircea et al. (2012) describe an agile BI architecture that combines BI, decision management and service orientation, so components can be added incrementally and reused across decisions. Our setup follows that idea on a small scale: the R scripts are reusable services for data preparation and analysis, and the dashboard is a separate layer that can grow as new decisions, such as cancellation risk, are added. Loshin (2012) cautions that tools matter less than clear requirements and data quality, which is why each script begins with checks on missing values and duplicate records.
Use in the Capstone
The capstone's marketing plan will rest on these tools: R for the analysis and an appendix of code that can be rerun, and the dashboard for the owners' review of candidate areas and competitors.
Conclusion
For an organization at the start of BI maturity, the best tools are those that make analysis repeatable and understandable. R scripts with a low-cost dashboard fit our chain now and can grow with it.
References
Chuah, M.-H., & Wong, K.-L. (2012). Construct an enterprise business intelligence maturity model (EBI2M) using an integration approach: A conceptual framework. In M. Mircea (Ed.), Business intelligence: Solution for business development. InTech. https://doi.org/10.5772/35457
Loshin, D. (2012). Business intelligence: The savvy manager's guide (2nd ed.). Morgan Kaufmann.
Mircea, M., Ghilic-Micu, B., & Stoica, M. (2012). An agile architecture framework that leverages the strengths of business intelligence, decision management and service orientation. In M. Mircea (Ed.), Business intelligence: Solution for business development. InTech. https://doi.org/10.5772/35477
DAA 7030 Module 4 instructions, in plain terms
The second DAA-7030 written assignment, three pages in APA format and part of the two assignments' 10 percent, relates to data analytic software and becomes part of your final project. Expect to assess where your organization stands with BI, compare software options for preparing, analyzing and presenting data, choose a toolset and explain how it supports the capstone. The course's open readings by Chuah and Wong on BI maturity and by Mircea and colleagues on BI architecture fit this assignment well. Compare options on criteria that matter to your setting, such as cost, skills, reproducibility and sharing, and remember that the capstone must include code or files that can be rerun. Place your organization on a maturity path before choosing tools.
Inside the DAA 7030 Module 4 example
The paper begins with Chuah and Wong's maturity model, placing the chain at the initial stage and drawing the consequence for tool choice. A five-row table compares spreadsheets, R, Python and two visualization platforms on cost, skills, reproducibility and sharing. A margin note explains why reproducibility outweighs features at this stage. The choice pairs commented R scripts with a lower-cost dashboard, framed by Mircea and colleagues' agile architecture and Loshin's caution about requirements and data quality. A short section ties the tools to the capstone's rerunnable code. Each script begins with checks on missing values and duplicates, following Loshin's emphasis on data quality. A section ties the tools to the capstone's rerunnable code.
DAA 7030 Module 4 rubric: what earns full marks
Mentors grade this assignment on a reasoned assessment of the organization's readiness, a fair comparison of software on relevant criteria, a justified choice, links to the course readings and APA writing within three pages. Papers that judge tools by the organization's maturity and needs, rather than by popularity, show judgment. Including reproducibility and handover as criteria shows professional practice. Connecting the choice to the capstone shows planning. Concise tables help fit the analysis into three pages. Mentors also credit honesty about the limits of one analyst's capacity and plans for handing work over. Tying the software choice to the capstone's requirements shows planning.
DAA 7030 Module 4 help: mistakes that cost marks
Students lose points by listing software features from vendor sites, by choosing the most powerful tool regardless of skills or by ignoring the requirement that capstone code be rerunnable. Judge how mature your BI is, weigh a handful of real options, pick one with reasons and show how it will deliver the capstone. A free first draft of your DAA-7030 Module 4 written assignment can be built on your setting. Explain how someone else could rerun your work if you left, since that is the test of a sound toolset. Include cost, skills and sharing in your comparison, not only features. Keep the comparison to options your organization could realistically adopt.
Write yours, or have the desk draft it
This paper is an original model document written by our desk, not a submitted student paper and not an official Thomas Edison State University document. Read it for the moves, then write your own to the instructions in your classroom. If you want one built to your exact prompt and rubric, the first custom sample is free and arrives in 24 to 48 hours.
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DAA 7030 Module 4 questions, answered
Where can I find a free DAA 7030 Module 4 sample paper?
The written assignment above chooses analytics software for a fitness studio chain's first BI project.
What is a BI maturity model?
A framework, such as Chuah and Wong's, describing stages from ad hoc data use to organization-wide BI across data, technology, people and process.
Why does reproducibility matter in analytics?
Analyses that can be rerun can be checked, updated and handed to others, which manual spreadsheet steps cannot.
Should a small organization buy an enterprise BI platform?
Usually not at first; tools should match its maturity, skills and budget and be able to grow.
Does the DAA-7030 capstone require code?
Yes; students include the backend code or files used for visualizations so they can be rerun.