| Course | BUSI 3014 Data Driven Decision Making Workplace Certificate |
|---|---|
| Week | Week 9 |
| Paper type | Dashboard read |
| Length | About 860 words, 6 pages |
| Format | APA 7 student paper |
| School | Walden University |
| Program | BS in Business Administration |
| Updated | October 2026 |
Free sample paper for BUSI 3014 Week 9
Reading the Monday Dashboard for the Saturday Route Decision: Why a Green 2.6-Day Average Wait and 148 Open Requests Mean the Longest Waits Are Missing From the Average
Student Name
Walden University
BUSI 3014: Data Driven Decision Making Workplace Certificate
Instructor Name
Month Day, Year
Reading the Monday Dashboard for the Saturday Route Decision: Why a Green 2.6-Day Average Wait and 148 Open Requests Mean the Longest Waits Are Missing From the Average
The Decision, Before the Screen
Each Monday morning, Kestrel's owner (the business is this course's composite Knoxville exterminator) opens a one-screen dashboard. This read asks one question of it: does the dashboard for the week ending April 21, 2028, tell the owner anything about whether new customers are waiting too long, the problem a Saturday route would address? Panels that do not bear on that question are noted briefly and set aside.
The Screen
The dashboard has six panels, shown in the table as they appeared.
Table 1
The Dashboard, Week Ending April 21, 2028
| Panel | Value shown | Display | |
|---|---|---|
| Jobs completed | 141 | Bar against last week, 138 | |
| Weekly revenue | $31,400 | Line, up from last week | |
| Callbacks | 6 | Number | |
| Average first-visit wait | 2.6 business days | Green tile; target 3.0 or less | |
| Open requests | 148 | Number | |
| Customer rating | 4.7 out of 5 | Stars | |
Kestrel Pest Solutions and its dashboard are composite.
The Two Panels That Matter
Two panels bear on the Saturday route question: average first-visit wait and open requests.
Read alone, the wait panel is reassuring. It shows 2.6 business days, below the target of 3, and it is colored green. An owner checking it on a Monday would conclude that new customers are being served promptly.
Read alone, the open requests panel says little. It shows 148, a bare number with no comparison. But the dispatcher's records show that open requests ran between 55 and 75 most weeks in the winter. In mid-April, the number has doubled.
Read together, the two panels contradict each other. If new customers were being served in under three days on average, the backlog of open requests should not be growing. The explanation lies in how the wait panel is calculated. It averages the waits of requests completed during the week. A request is only counted once it is served. The 148 requests still waiting are not in the average at all, and among them are the customers who have waited longest. The more customers who are stuck in the queue, the more the average leaves out, so the average can stay green even as the backlog grows.
What the Hidden Problem Looks Like
When I pulled the open requests from the job management system on Monday, 41 of the 148 had already waited more than four business days, and the oldest had waited eleven. None of those waits appears anywhere on the dashboard.
This is a known problem in measuring durations. Kaplan and Meier (1958) addressed it for survival times: when some subjects have not yet reached the event being measured, leaving them out biases the estimate, and methods are needed that count them as incomplete observations. A dashboard average that only includes completed visits makes the same mistake in a simpler setting.
The Other Panels
Jobs completed, at 141, shows the technicians are busy but not whether new customers are waiting, the mismatch discussed at the start of the course. Weekly revenue, at $31,400, reflects mostly existing customers' scheduled treatments and says little about new callers. Callbacks, at 6, concern treatment quality, not waiting. The customer rating, 4.7, comes from customers who were served and reviewed, so customers who gave up waiting are not in it either. Each of these is useful for other questions, and none answers this one.
What the Owner Should Take From the Screen
The honest reading of this week's dashboard is that new customers are not being served as quickly as the green tile suggests, and that the backlog is growing as the spring season begins. Taken with the 2027 figures, where about one new customer in five waited more than four business days in the spring quarter, the dashboard is consistent with a capacity shortfall in peak months.
Why the Green Tile Is Persuasive
The wait tile is the only colored panel on the screen, and green signals that nothing needs attention. An owner with ten minutes on a Monday will reasonably trust it. That is why the flaw matters more than an error in a panel nobody looks at.
Suggested Changes to the Dashboard
Two changes would let the dashboard answer the Saturday route question on its own. First, add a panel showing the age of the oldest open request and the number of open requests older than four business days. Second, replace the average wait with the share of all new requests, completed or still open, that have waited more than four business days, which is the measure defined earlier in this course. Few (2006) argued that dashboards should be designed so the most important information can be grasped at a glance and that every display should serve the decisions it supports. A green tile that hides the customers waiting longest does the opposite. The view of data-driven decisions set out by Provost and Fawcett (2013) starts from the decision being made; the current wait panel was built to report on completed work, not on the customers still waiting.
References
Few, S. (2006). Information dashboard design: The effective visual communication of data. O'Reilly Media.
Kaplan, E. L., & Meier, P. (1958). Nonparametric estimation from incomplete observations. Journal of the American Statistical Association, 53(282), 457-481. https://doi.org/10.1080/01621459.1958.10501452
Provost, F., & Fawcett, T. (2013). Data science and its relationship to big data and data-driven decision making. Big Data, 1(1), 51-59. https://doi.org/10.1089/big.2013.1508
The Walden BUSI 3014 Week 9 instructions
The ninth week supplies a dashboard and asks what it means for one named decision. State the decision first, so the reader knows which panels matter before seeing the screen. Identify the panels that bear on the decision and read them together, since the most important finding often lies in the relationship between two displays rather than in either one. Look for a hidden problem, such as a panel calculated in a way that leaves out the cases that matter most. Acknowledge the unrelated panels briefly, with a sentence each on what they do show. End with what the decision maker should take from the screen and how the dashboard could be changed to answer the decision directly. Two to three pages, reproducing the dashboard as a table or figure, is typical.
Inside the BUSI 3014 Week 9 example
The read opens with the owner's Monday routine and the Saturday route question. A table reproduces the six panels for the week ending April 21, 2028. The core section reads the green 2.6-day average alongside 148 open requests, compares the backlog with winter levels of 55 to 75 and explains that the average includes only completed visits. A section reports that 41 open requests had already waited more than four business days and cites Kaplan and Meier on unfinished durations. Another explains why the green tile is persuasive. The other panels, jobs completed, revenue, callbacks and rating, get a sentence each. The conclusion links the screen to the 2027 findings, and two proposed panels replace the average, citing Few and Provost and Fawcett.
Where the marks sit in the BUSI 3014 Week 9 rubric
Dashboard reads are graded on focus and insight. A tour of every panel in screen order misses the assignment; the read should be organized around the decision. Graders look for the relevant panels to be read together and for the writer to find what the display hides, such as an average that excludes unfinished cases. The hidden problem should be explained clearly enough that a manager would understand why the green figure is misleading. Unrelated panels deserve brief acknowledgment, not analysis. Proposed changes should let the dashboard answer the decision on its own. Readings on dashboard design or measurement should support the critique. A read that ends with a plain statement of what the decision maker should conclude meets the week's main test.
BUSI 3014 Week 9 help from the desk
Write the decision at the top of your page, then mark each panel as relevant or not. For the relevant ones, ask how each number is calculated and what it leaves out. Read them side by side: do they tell the same story? If not, find out why. Check whether any average or rate includes only completed cases. Then say in one sentence what the screen really means for the decision. Weak reads describe every panel equally, accept a green tile without asking how it was calculated or recommend a complete redesign unrelated to the decision. Others ignore what the unrelated panels are good for. If you need a model dashboard read for a screen your workplace uses, share the panels and the decision it supports.
Get a BUSI 3014 Week 9 example written to your instructions
Include the dashboard image or file, the Week 9 prompt and its rubric; an interpretation written to that display arrives within 24 to 48 hours, the first at no charge. Should the prompt name the decision the read must serve, quote it, and the panels are ranked against that decision instead of one the sample chooses.
More BUSI 3014 papers and BS in Business Administration samples
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- BUSI 3014 Week 2: Business Question Framing
- BUSI 3014 Week 3: Operational Measure Definition
- BUSI 3014 Week 4: Data Quality Audit
- BUSI 3014 Week 5: Measure Fit Peer Replies
- BUSI 3014 Week 6: Descriptive Statistics Summary
- BUSI 3014 Week 7: Misleading Chart Critique
- BUSI 3014 Week 8: Seasonal Trend Read
- BUSI 3014 Week 10: One-Page Decision Memo
- BUSI 3014 Week 11: Final Data Analysis Report
- BUSI 3010 Week 9: Shop Floor Layout Proposal
BUSI 3014 Week 9 questions, answered
Where can I find a free BUSI 3014 Week 9 sample paper?
Read the full dashboard analysis, panel table and proposed changes included, on this page at no cost.
What decision does the BUSI 3014 Week 9 read focus on?
Whether a pest control company should add a Saturday route because new customers are waiting too long.
Which two panels matter most?
Average first-visit wait, shown green at 2.6 days, and open requests, which had doubled to 148.
What does the average hide?
It counts only customers already served, so the 41 open requests that had waited more than four business days are left out.
What changes to the dashboard are proposed?
A panel for the oldest open request and long-waiting open requests, and a long-wait share covering served and unserved requests together.