| Course | DSC 330 Data Communication and Visualization for Business (DSC/330) |
|---|---|
| Week | 5 |
| Paper type | Data storytelling presentation |
| Length | about 1,017 words, 4 double-spaced pages plus title page and references |
| Format | APA 7 student paper |
| School | University of Phoenix |
| Program | BS in Business |
| Updated | October 2026 |
Free sample paper for DSC 330 Week 5
Revenue Up, Guests Unhappy: Presenting a Data Story to a Hotel Company's Owners
[Student Name]
University of Phoenix
DSC/330: Data Communication and Visualization for Business
Week 5 Assignment
[Instructor Name]
[Date]
Music Row Hospitality, its hotels, owners, data and figures are composites written for a model paper.
Over four weeks, the analytics team at Music Row, the made-up hotel manager used all course, gathered requirements, chose charts, built dashboards and validated the data behind them. The validated dashboards showed something the monthly report had not: at three hotels, revenue rose while guest satisfaction fell. This paper builds and presents that finding as a data story for the hotels' owners.
Audience and Goal
Knaflic (2015) advises analysts to decide first who is listening, what those people must learn or decide and what mood the story should leave them in, before picking any visual. The audience is six hotel owners at a quarterly meeting. They care about returns on their properties, are wary of added costs and have about 15 minutes for this item. The goal is approval to add housekeeping staff at three hotels, at a cost of about $26,000 a month, for a three-month trial.
Research on Data Stories
Segel and Heer (2010) studied narrative visualizations in journalism and other fields and identified genres, such as slide shows, annotated charts and comics, and design techniques for guiding readers, including ordering, highlighting and messaging. They described a range from author-driven stories, with a fixed sequence and strong messaging, to reader-driven ones, open to exploration. A short owners' meeting calls for an author-driven story. Hullman and Diakopoulos (2011) showed that design choices in narrative visualizations, such as what to include, how to annotate and which comparisons to show, frame interpretation and can shape conclusions, which places an ethical burden on the storyteller.
The Story in Five Scenes
Scene 1, the good news. Ranked bars show year-to-date revenue per available room up 7 percent across the portfolio and up more than 10 percent at three hotels: the downtown Nashville property, the airport hotel and the Franklin hotel. Title: "Revenue per room is up 7 percent, led by three hotels." Owners relax; this is what they hoped to hear.
Scene 2, the tension. A line chart shows guest satisfaction at those three hotels falling from 8.4 to 7.6 over two quarters, while the other six hold steady. Title: "At the same three hotels, guest satisfaction has fallen for six months." The contrast with scene 1 creates the question the rest of the story answers.
Scene 3, the likely cause. A scatter plot of each hotel's rooms cleaned per housekeeping attendant against its satisfaction score shows the three hotels in the lower right: attendants now clean 17 to 18 rooms a shift, compared with 14 at the others, after higher occupancy without added staff. An annotation notes that housekeeping complaints at the three hotels doubled. Two guest comments appear beside the chart: "Room wasn't ready until 5 p.m." and "Bathroom clearly rushed."
The hotels made more money by filling more rooms with the same housekeepers, and the guests noticed.
Scene 4, what is at stake. A simple comparison shows that guests who rate a stay 6 or below return at about half the rate of guests rating 9 or 10, based on two years of booking history. If satisfaction stays at current levels, the three hotels could lose an estimated $410,000 a year in repeat business, more than the cost of added staff.
Scene 5, the request. One slide states: "Approve a three-month trial adding two housekeeping attendants per shift at three hotels, $26,000 a month. We will report satisfaction, complaints and repeat bookings monthly and recommend continuing only if satisfaction returns above 8.2."
Honest Framing
The story could be framed to exaggerate. Starting the satisfaction axis at 7 would make the drop look dramatic; the chart starts at 0, with a second zoomed view clearly labeled. Other explanations are named on scene 3's notes: renovation noise at the downtown hotel and a new front desk system at the airport hotel may also have hurt satisfaction. The analysis cannot prove that staffing is the cause, which is why the request is a trial with a measure, not a permanent change.
Rehearsing for Questions
Owners were likely to ask why satisfaction matters if revenue is rising, whether the analysis simply reflects one bad month and whether managers could fix the problem without new staff. The analyst prepared answers: repeat bookings and online ratings drive future revenue, the decline has lasted six months and managers had already tried scheduling changes without effect. Rehearsing with the chief operating officer playing a skeptical owner sharpened the answers and cut one slide that did not help.
Delivering the Story
The presentation used five slides, each with one chart and a title stating its point, and a one-page handout with the same content for owners who prefer paper. The dashboard was available for questions; one owner asked to see her own hotel's satisfaction by month, and the analyst showed it live, which built confidence in the data.
The Owners' Response
The owners of the airport and Franklin hotels approved the trial at once; the downtown owner asked to wait until renovation ends next month. The other three owners, whose hotels were not affected, asked for the same scatter plot of their own properties at the next meeting. The airport and Franklin trials began two weeks later, and the first monthly report showed housekeeping complaints already falling.
Looking Back at the Course
The story depended on every earlier step. Requirements in Week 1 identified satisfaction and labor as measures owners cared about. Chart choices in Week 2 produced the scatter plot that revealed the cause. Building in Week 3 put the data in one place. Validation in Week 4 meant that when an owner questioned a number, the analyst could explain exactly where it came from.
Conclusion
A good data story starts with an audience and a decision, builds tension from a surprising contrast, shows a likely cause with honest caveats, quantifies the stakes and ends with a specific request. Research on narrative visualization and framing guided a five-scene story that led Music Row's owners to approve a measured trial, and the course's earlier work made the data trustworthy enough to act on.
References
Hullman, J., & Diakopoulos, N. (2011). Visualization rhetoric: Framing effects in narrative visualization. IEEE Transactions on Visualization and Computer Graphics, 17(12), 2231-2240. https://doi.org/10.1109/TVCG.2011.255
Knaflic, C. N. (2015). Storytelling with data: A data visualization guide for business professionals. Wiley.
Segel, E., & Heer, J. (2010). Narrative visualization: Telling stories with data. IEEE Transactions on Visualization and Computer Graphics, 16(6), 1139-1148. https://doi.org/10.1109/TVCG.2010.179
What the DSC 330 Week 5 instructions ask
DSC 330's fifth paper has students present a story based on data. The assignment tends to ask students to define an audience and purpose, structure a narrative with a beginning, middle and end, select and refine visuals, add annotations and titles, make a recommendation and reflect on ethical presentation, often as slides or a written report. Some instructors add a step that recaps the whole path from requirements to presentation. Build the story from a real or realistic data set, defend each structural and visual choice with storytelling research and the textbook, cited in APA format. End with a specific request to the audience, and describe how you would measure whether the story led to action in the weeks after.
How this DSC 330 Week 5 example is built
The worked paper starts with the audience: six hotel owners at a quarterly meeting, each with 15 minutes of attention and an interest in their own property's returns. The story's goal is approval to add housekeeping staff at three hotels. It opens with good news, revenue per available room up 7 percent, then reveals the problem, satisfaction falling at the same three hotels where rooms per attendant rose, shows the likely cause with a scatter plot and evidence from guest comments, quantifies the risk to repeat bookings and ends with a request and a test plan. Research on narrative visualization and on framing effects guides structure and honest presentation, and the owners approve a three-month trial.
DSC 330 Week 5 grading rubric: where the points go
Instructors reward data stories that lead an audience to a decision. Strong papers define the audience and goal, structure the narrative with a clear beginning, tension and resolution and choose a small number of annotated visuals that carry the message. Credit goes to honest framing, such as showing uncertainty and alternative explanations, to a specific request and to reflection on how the story drew on earlier work. Graders also value research on narrative visualization and framing and a clear account of how the audience responded. Vivid examples, an easy-to-follow order and tidy APA references close it out.
DSC 330 Week 5 help: mistakes to avoid
Data story papers often show every chart from the analysis in order. Choose the few that move the audience from question to decision. Another frequent gap is a story without tension; explain what is at stake, such as a risk or an opportunity. Students also present findings as certain; show uncertainty and other possible explanations honestly. Some papers end with a summary instead of a request; ask for a specific decision. Finally, tailor the story to the audience's interests, such as owners' returns, rather than the analyst's process. A tutor can help you build a storyboard before choosing slides and rehearse the story within the time you will have.
Related DSC 330 sample papers
Other DSC 330 week samples
- DSC 330 Week 1: Gathering Requirements
- DSC 330 Week 2: Matching Charts to Questions
- DSC 330 Week 3: Building Dashboards
- DSC 330 Week 4: Validating Data
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DSC 330 Week 5 questions, answered
What does DSC 330 Week 5 usually cover?
It usually covers presenting a data story: defining audience and purpose, structuring a narrative, choosing and annotating visuals, framing findings honestly and making a recommendation.
Where can I find a free DSC 330 Week 5 sample paper?
Above you will find the owners' data story paper, Revenue Up, Guests Unhappy, free in full.
What is a data story?
A sequence of visuals and narrative that guides an audience through data to an insight and a decision, with context, tension and a clear conclusion.
How many charts should a data presentation include?
Usually only a few, each carrying one point the audience needs on the path to the decision; supporting detail can go in an appendix.
How can data stories mislead?
Through selective time ranges, cropped axes, omitted context or framing that hides uncertainty, which is why honest presentation shows limits and alternatives.
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