7. Lighthouse

Level 4 · Strategic, operational or analytical?

From Jason Reyes, Operations Manager

“We're splitting the big operations report into three pages: one for the leadership meeting, one my team checks every morning, and one for digging into problems. I've got these three charts and I'm not sure which page each belongs on.”

Your goal

Look at the three charts drawn from the store data and pick the right page for each.

Chart A Sales from completed orders, by year
Sales from completed orders, by year$0$500k$1M$1.5M$2M$2.5M2023: $1,600,511$1,600,51120232024: $1,993,605$1,993,60520242025: $2,300,171$2,300,1712025
Sales from completed orders, by year$0$500k$1M$1.5M$2M$2.5M2023: $1,600,511$1.6M20232024: $1,993,605$2M20242025: $2,300,171$2.3M2025

$1.60M in 2023, $1.99M in 2024 (+24.6%), $2.30M in 2025 (+15.4%).

Show the numbers
sales
2023$1,600,511
2024$1,993,605
2025$2,300,171
Chart B Orders still pending, by order date (as of Dec 31, 2025)
Orders still pending, by order date (as of Dec 31, 2025)051015Dec 22: 10Dec 22Dec 23: 13Dec 24: 4Dec 24Dec 25: 6Dec 26: 4Dec 26Dec 27: 12Dec 28: 11Dec 28Dec 29: 8Dec 30: 10Dec 30Dec 31: 14Pending orders
Orders still pending, by order date (as of Dec 31, 2025)051015Dec 22: 10Dec 22Dec 23: 13Dec 24: 4Dec 25: 6Dec 25Dec 26: 4Dec 27: 12Dec 28: 11Dec 28Dec 29: 8Dec 30: 10Dec 31: 14Dec 31Pending orders
Show the numbers
Pending orders
Dec 2210
Dec 2313
Dec 244
Dec 256
Dec 264
Dec 2712
Dec 2811
Dec 298
Dec 3010
Dec 3114
Chart C Returned order lines by category and reason, 2023 to 2025
Returned order lines by category and reason, 2023 to 2025changed_minddamagedlatewrong_itemDecorDecor × changed_mind: 176176.00Decor × damaged: 143143.00Decor × late: 6363.00Decor × wrong_item: 7070.00FurnitureFurniture × changed_mind: 2929.00Furniture × damaged: 4747.00Furniture × late: 1919.00Furniture × wrong_item: 88.00KitchenKitchen × changed_mind: 111111.00Kitchen × damaged: 9393.00Kitchen × late: 4949.00Kitchen × wrong_item: 4444.00LightingLighting × changed_mind: 8080.00Lighting × damaged: 107107.00Lighting × late: 3535.00Lighting × wrong_item: 3333.00OutdoorOutdoor × changed_mind: 7373.00Outdoor × damaged: 4343.00Outdoor × late: 2424.00Outdoor × wrong_item: 2424.00TextilesTextiles × changed_mind: 229229.00Textiles × damaged: 4444.00Textiles × late: 5353.00Textiles × wrong_item: 7676.00
Returned order lines by category and reason, 2023 to 2025changed_minddamagedlatewrong_itemDecorDecor × changed_mind: 176176.00Decor × damaged: 143143.00Decor × late: 6363.00Decor × wrong_item: 7070.00FurnitureFurniture × changed_mind: 2929.00Furniture × damaged: 4747.00Furniture × late: 1919.00Furniture × wrong_item: 88.00KitchenKitchen × changed_mind: 111111.00Kitchen × damaged: 9393.00Kitchen × late: 4949.00Kitchen × wrong_item: 4444.00LightingLighting × changed_mind: 8080.00Lighting × damaged: 107107.00Lighting × late: 3535.00Lighting × wrong_item: 3333.00OutdoorOutdoor × changed_mind: 7373.00Outdoor × damaged: 4343.00Outdoor × late: 2424.00Outdoor × wrong_item: 2424.00TextilesTextiles × changed_mind: 229229.00Textiles × damaged: 4444.00Textiles × late: 5353.00Textiles × wrong_item: 7676.00
Show the numbers
changed_minddamagedlatewrong_item
Decor176.00143.0063.0070.00
Furniture29.0047.0019.008.00
Kitchen111.0093.0049.0044.00
Lighting80.00107.0035.0033.00
Outdoor73.0043.0024.0024.00
Textiles229.0044.0053.0076.00
Which page does each chart belong on?

Pick one answer, then check it.

The lesson

Dashboards come in three types, and most bad ones are two types mixed together. Knowing which one you’re building tells you how many KPIs it needs, how fresh the data must be, and how much filtering to allow.

What you are working in

This quiz runs in a practice page, not Power BI (Power BI Desktop runs only on Windows). The charts above are drawn from Harbor & Pine’s data when the site is built. In Power BI Desktop each would be a visual from the Visualizations pane: a Clustered column chart for A and B, a Matrix with conditional formatting (Format visual → Cell elements → Background color) for C.

Strategic (executive)

  • Question: are we doing well or badly?
  • Content: roughly 6 to 12 KPIs at most, trends, comparisons with last year (YoY) or last month (MoM), targets.
  • Pace: read weekly, monthly or quarterly.
  • Example: Chart A. Sales grew from $1.60M to $1.99M to $2.30M; growth slowed from 24.6% to 15.4%. That slowdown is a leadership conversation.

Operational

  • Question: what needs fixing today?
  • Content: current values, thresholds, exceptions, lists to work through. Often refreshed several times a day, with alerts.
  • Pace: checked every morning, acted on immediately.
  • Example: Chart B. Ninety-two orders placed between December 22 and 31 are still pending, between 4 and 14 a day. The team needs that list, oldest first.

Analytical (exploratory)

  • Question: why is this happening?
  • Content: many filters, drill-down, segments, cross-tabulations like a heatmap.
  • Pace: used when a strategic or operational number raises a question.
  • Example: Chart C. Of 402 Textiles returns, 229 are “changed mind”; Lighting and Furniture returns are led by damage. Two different problems with two different fixes.

How they connect

The three pages work as a chain: the strategic page shows that returns are rising, the analytical page shows where and why, and the operational page tracks the fix day by day. Link them with drill through or page navigation buttons, so a reader can follow a number from one level to the next.

Common mistakes

  • Operational detail on the executive page. It hides the trend leadership came for.
  • Strategic pages that refresh every hour. Constant movement invites over-reaction.
  • Analytical pages with no filters. If readers can’t slice it, they can’t find the “why”.

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