You can build charts. Now learn what to do with them — how to frame the analysis, design dashboards people actually use, and write findings that drive decisions.
Last week you learned to create visuals in Power BI. That is a mechanical skill — like learning to type. This week is about what you type. Analysis is the reasoning that connects data to a decision. A chart without reasoning is just decoration.
| Tier | What you ask | What you do in Power BI | What you say in the report |
|---|---|---|---|
| 1 — Descriptive | "What is the profit in Central?" | Build a bar chart: Sum of profit by region. Central shows $39,706. | "The Central region generated $39,706 in profit — the second lowest of the four regions." |
| 2 — Diagnostic | "Why is Central's profit low?" | Scatter plot: discount vs profit, filter to Central. Add state filter. Texas and Illinois show heavy discounting driving losses. | "The low profit is driven by Texas (−$25,729) where 42% of orders carry discounts above 50%, eroding all margin." |
| 3 — Predictive | "Will Central improve?" | Line chart of Central profit by quarter. Add Tableau forecast. Wide confidence band signals uncertainty. | "If current discounting continues, Central is forecast to remain loss-making in Texas through 2024 (95% CI: −$28K to −$22K)." |
| 4 — Prescriptive | "What should we do?" | Calculate: removing discounts >30% in TX. Model the profit impact using a DAX measure. | "Recommendation: Cap Texas discounts at 30%. Modelling shows this recovers ~$18K of the annual loss with minimal volume impact." |
Read each statement and decide: is it Descriptive, Diagnostic, Predictive, or Prescriptive? Be ready to justify your answer to the class.
UX (User Experience) in data visualisation means designing for your audience's cognitive limitations — not your own preferences. Five principles determine whether a dashboard enables decisions or creates confusion.
Place your most important KPI in the top-left. It should be the first thing the eye lands on. Use size, contrast, and white space — not colour alone — to establish dominance.
profit → Fields → set to Sum. Shows $286,397 in large font.sales (Sum = $2,297,201) and profit_margin (Average = 12%)Cognitive load is the mental effort required to interpret a visual. Every additional chart, label, colour, and gridline increases it. The brain has a fixed processing budget per screen.
If blue = Consumer in your first chart, blue must mean Consumer in every chart on the dashboard. Breaking this forces the audience to re-read the legend on every visual — destroying comprehension.
| Role | Usage | Superstore example |
|---|---|---|
| Categorical | Different colours for different categories. Max 6–8 categories. | Blue=Consumer, Orange=Corporate, Red=Home Office — consistent across every visual |
| Sequential | One hue, varying lightness. For quantities on a scale. | Light blue = low profit → Dark blue = high profit on the choropleth map |
| Diverging | Two hues from a neutral midpoint. For loss vs gain. | Red = loss-making states, Grey = zero, Blue = profitable states |
Interactivity is powerful because it allows one dashboard to serve many analytical questions. But every filter you add creates a decision the audience must make before they can read anything. Keep controls minimal and purposeful.
| Slicer field | Good reason to add? | Verdict |
|---|---|---|
Year | Audience needs to compare annual performance | Add it |
Region | Regional managers will each use this for their area | Add it |
Category | Product managers want category-specific views | Add it |
Customer | 793 customers — audience can't meaningfully choose | Skip it |
Order ID | No business question requires filtering to one order | Skip it |
Ship Mode | Only useful for a specific logistics dashboard | Maybe |
| Audience | What they need from the dashboard | Right chart types | What to avoid |
|---|---|---|---|
| Executive / CEO 5–10 seconds per view |
High-level summary. Is the business on track? Where is the biggest risk? | KPI cards · single bar chart · one map. Max 4 visuals per page. | Scatter plots · matrices · raw tables · anything requiring technical literacy |
| Regional Manager 10–20 minutes per session |
Performance for their area. How is their team tracking against target? | Filtered bar charts · time series with target line · table with drill-down | National-level aggregates that don't reflect their decisions |
| Data Analyst Deep exploration sessions |
Full data access. Ability to explore hypotheses. Raw figures visible. | Scatter plots · matrices · distribution histograms · all filtering options | Over-simplification that hides the data they need to investigate |
| Marketing Team Campaign-specific use |
Customer segments, product performance, geographic opportunity | Donut charts for segment share · map · treemap for product prominence | Financial loss/profit metrics that aren't in their remit |
Apply the five UX principles to improve the dashboard you built last week. Work in pairs — one person applies changes, the other reads as the intended audience.
The most common mistake in student data reports is restating what the charts show in prose. That adds no value — the audience can see the chart. A report's job is to explain what the data means and what should be done about it.
Any finding can be expressed in three sentences:
Sentence 1: The key finding (observation)
Sentence 2: What it means (insight)
Sentence 3: What to do (recommendation)
If you can't write Sentence 3, your analysis hasn't reached the level required for a business report.
This analysis examines four years of Superstore transactional data (2019–2022, 9,994 orders) to identify drivers of profitability across products, regions, and customer segments.
The Central region's discount strategy is destroying margin: while it generates $723K in gross sales, aggressive discounting — particularly in Texas and Illinois — produces a net profit of only $39,706, the lowest of all four regions.
Specifically, Texas alone accounts for a $25,729 annual loss, driven by 42% of its orders carrying discounts ≥50%. In contrast, the Technology category generates a 20% profit margin even at moderate volumes, confirming that product mix — not sales volume — is the primary profit lever.
Note: this analysis cannot determine whether discounts are being applied correctly per contract or at-will by sales staff — a process audit would clarify this.
Recommendation: Cap all discretionary discounts in the Central region at 30%, and require manager approval for any discount above this threshold. Modelled impact: recovery of approximately $18K annually in Texas alone.
Using the five-part structure, write an executive summary for the dashboard you redesigned in the first activity. This will form the basis of your Week 3 submission.