DAT 565 Week 3 Visual Representations of Data Example

Seeing the Late Afternoon Problem: Designing Charts for a Fulfillment Warehouse's Leaders and Clients

[Student Name]

University of Phoenix

DAT/565: Data Analysis and Business Analytics

Week 3 Assignment

[Instructor Name]

[Date]

Peachtree Fulfillment Partners, its clients, data and figures are composites written for a model paper.

Weeks 1 and 2 prepared and summarized a year of order data for Peachtree, the invented Fayetteville, Georgia, warehouse with 34 client brands. The statistics showed that accuracy problems cluster among clients with complex, look-alike orders and that orders released after 2 p.m. often miss carrier cutoffs. Statistics alone did not persuade managers; most had not read past the first page of the old report. This paper designs visuals that make the findings clear.

What Research Says About Reading Charts

Heer and Bostock (2010) replicated classic graphical perception experiments with hundreds of online participants and confirmed that readers estimate values best when they compare points against a shared axis, followed by length, with angles, areas and color shading judged less accurately. They also found that chart details such as gridline spacing and aspect ratio affected accuracy. The practical lesson is to encode the most important comparisons as positions or lengths on a shared axis.

Borkin et al. (2013) studied what makes visualizations memorable and found that visuals with clear titles, recognizable objects and distinctive features were remembered better, and that readers often recalled the title's message. For business charts, a title that states the finding helps busy managers remember it.

Knaflic (2015) applies these ideas to business presentation, urging analysts to identify the audience and the action they want, choose an appropriate visual, remove clutter and focus attention with color and text.

Critiquing the Current Charts

Order volume by client. The current report shows a 3-D pie chart with 34 slices. No one can compare slices smaller than 3 percent, and the 3-D tilt distorts the front slices to look larger. Replacement: a horizontal bar chart sorted by volume, with the top ten clients labeled and the rest grouped.

On-time shipping by day. A dual-axis chart plots on-time rate on a left axis starting at 90 percent and order volume on a right axis. The truncated left axis makes a one-point dip look like a collapse, and the overlay suggests a relationship the data do not support. Replacement: a single line chart of the on-time rate with a target line, axis starting at 85 percent and clearly labeled, and a separate small chart of volume aligned below.

Accuracy by client. A table of 34 percentages to two decimals. Replacement: a dot plot with each client's accuracy as a point, sorted, with the contract target as a vertical line, so clients below target stand out immediately.

Errors by type. A stacked bar chart with nine colors for error types across 34 clients. Replacement: a small set of bar charts showing the top three error types for the five clients with the most errors.

New Visuals for Operations Managers

Time to ship. A histogram of hours from release to ship shows a large peak under four hours and a long tail with a second bump around 16 to 20 hours, orders that waited overnight. The title states: "Most orders ship within four hours; one in eight waits overnight."

Release timing. A heat map with release hour on one axis and weekday on the other, colored by on-time rate, shows a band of poor performance on weekday afternoons after 2 p.m., strongest on Mondays. Title: "Orders released after 2 p.m. drive most missed cutoffs."

Order complexity. A scatter plot of each client's average lines per order against its error rate per order shows a clear upward pattern, with the three low-accuracy clients in the upper right. Title: "Clients with more lines per order have more errors per order."

The heat map did in ten seconds what the fourteen-page report had not done in a year.

A Client Scorecard

Clients need different visuals. Each client's quarterly review will include a one-page scorecard: a line chart of its accuracy and on-time rates over 13 weeks against its contract targets, a bar showing its order release timing compared with the warehouse average and two sentences on actions under way. For clients with late releases, the scorecard shows how much their on-time rate would likely rise if orders arrived by noon, a factual way to start a conversation about release timing.

Using Color With Purpose

The old reports used a different color for every client, which meant color carried no meaning. The new standard uses gray for context and a single accent color for whatever the reader should notice: clients below target, the afternoon band on the heat map or the current week on a trend line. Red and green are avoided as the only signal because about one in twelve men has difficulty telling them apart; below-target items are also marked with a symbol. Limiting color this way draws the eye to the message instead of spreading attention across the page.

Annotation

A chart without words leaves readers to guess what matters. Each new chart carries one or two short annotations placed next to the data they explain: on the on-time trend, a note marks the carrier outage in March; on the scatter plot, a note identifies the three clients in the upper right. Annotations replace legends where possible, so readers do not shift their eyes back and forth to decode the chart.

Honest Design

Visuals can mislead even when the data are correct. The standards adopted for Peachtree's reports require axes that start at zero for bar charts, clearly labeled truncated axes for line charts where small changes matter, no dual axes, no 3-D effects, consistent colors across reports and time ranges that match the question rather than favorable periods.

Tools

The visuals were built in the company's business intelligence tool, connected to the prepared tables from Week 1 so they refresh weekly. Templates enforce the standards, so new charts start from good defaults.

Testing the Visuals

Before rollout, the new charts were first shown to five managers and two account managers, each asked to state the main message of each chart within 30 seconds. Six of the seven readers correctly stated the release timing finding from the heat map, against two of seven from the old report's table. The scatter plot needed a clearer label for "lines per order," which was revised to "average items per order."

Standards for Future Reporting

Start with the question and the audience.

Use position and length for key comparisons.

Title each chart with its finding.

Remove decoration and limit colors to emphasis.

Show targets and comparisons on the chart.

Conclusion

Peachtree's findings were already in the data; the charts hid them. Research on graphical perception, memorability and business presentation guided a redesign that replaces pies, dual axes and dense tables with sorted bars, dot plots, a histogram, a heat map and a scatter plot, each titled with its message. Managers now see the late-afternoon problem and the cost of complex orders at a glance, which sets up the process improvement work in Week 4.

References

Borkin, M. A., Vo, A. A., Bylinskii, Z., Isola, P., Sunkavalli, S., Oliva, A., & Pfister, H. (2013). What makes a visualization memorable? IEEE Transactions on Visualization and Computer Graphics, 19(12), 2306-2315. https://doi.org/10.1109/TVCG.2013.234

Heer, J., & Bostock, M. (2010). Crowdsourcing graphical perception: Using Mechanical Turk to assess visualization design. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 203-212). ACM. https://doi.org/10.1145/1753326.1753357

Knaflic, C. N. (2015). Storytelling with data: A data visualization guide for business professionals. Wiley.