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Getting insight from your data

The Saiku workbench is a tool, not an answer. The other pages cover mechanics — drag here, click there. This page covers what to actually do with the mechanics: how to take a business question and turn it into a Saiku analysis that answers it.

Start with the question, not the data

The biggest mistake analysts make in OLAP tools is starting from “what data do we have?” and clicking around until something interesting shows up. That’s exploration, not analysis. Useful sometimes — usually not.

A better start: write the question down before opening Saiku.

  • “Which regions grew revenue more than 20% year-over-year in Q4?”
  • “What’s our gross margin trend by product line over the last 12 months?”
  • “Which customers haven’t bought anything in the last 60 days but bought regularly before that?”

With the question explicit, you know what you need:

  • A measure (revenue, margin, customer count).
  • A dimension to slice by (region, product line, customer).
  • A time filter (Q4, last 12 months, last 60 days).
  • A comparison (vs prior year, vs trend, vs population).

Now the workbench moves are obvious.

The four-step pattern

Most analyses follow the same shape:

  1. Pick the measure. What number answers the question?
  2. Pick the slicing dimension. What do you want it broken down by?
  3. Pick the time window. What period?
  4. Pick the comparison. Vs what?

Step 1 + 2 + 3 give you a number. Step 4 is what makes it an insight rather than just a value.

Worked example: regional revenue growth

The question: Which regions grew revenue more than 20% year-over-year in Q4?

Decompose:

  • Measure: Revenue (sum).
  • Dimension: Region.
  • Time window: Q4 2024 vs Q4 2023.
  • Comparison: % change between the two.

In the workbench:

  1. Drag Region into Rows.
  2. Drag Revenue into Columns.
  3. Drag Time › Year into Filters; pick 2023 and 2024.
  4. Drag Time › Quarter into Columns above Revenue, filter to Q4.
  5. Now you have a table with one column for Q4 2023 revenue, one for Q4 2024, by region.
  6. Add a calculated member: ([Q4 2024 Revenue] - [Q4 2023 Revenue]) / [Q4 2023 Revenue]. Format as percentage.
  7. Sort by the calculated column descending.

The top of the table now answers the question directly. Click any region to drill into its products and see what drove the growth.

Patterns

A few moves come up over and over.

Period-over-period. Two date filters + a calculated-member ratio. The shape above — works for “this year vs last year”, “this month vs same month last year”, whatever.

Top-N + the rest. “Top 10 customers by revenue, with everyone else collapsed into Other.” Filter the dimension to top 10; add a calculated member for total - sum(top 10) labelled “All others”.

Drill-then-pivot. Drill into a parent that looks interesting, then swap the axes (toolbar button) to see the children side-by-side instead of stacked. Quick way to reorient when the top-level shape leads you to a specific question one level down.

Compare via small multiples. When you want to see the same chart across many sub-groups, switch to a multi-chart variant (multi-bar, multiple sunburst). Same shape repeated, different slice each time.

Anti-patterns

A few moves to avoid.

Pie charts with 12+ slices. Unreadable. Switch to treemap or sort + bar instead.

Drilling without filtering. Drilling Year → Quarter → Month → Day on a 5-year cube gives you ~1825 rows. Filter to a window first.

Trusting the default sort. Saiku’s default is whatever order the database returned. Always click a measure column header to sort by something meaningful before reading conclusions.

Auto-run on a slow query. If a query takes 30 seconds to run and you’re still composing the layout, every drag fires a query that runs to completion. Toggle auto-execution off, compose, then fire it manually.

When to use a chart vs the table

  • Table when you want exact numbers. “What was Q4 revenue in the South-West?” wants $2,381,492, not a bar of approximate height.
  • Chart when you want pattern. “Which regions grew, and which shrank?” wants the visual gestalt of a bar chart, not the row of numbers.

A common workflow: build in the table to confirm the data is right, switch to a chart for the final visual, then switch back to verify specific numbers if questioned.

Sharing your finding

Once you’ve found the answer, save the workbook (File → Save as) so you can come back. Pick a folder visible to the people who’ll want it.

Two options for sharing the result:

  • Saved workbook link. Anyone with access to your workspace can open it and see the same query. They’ll see live numbers — the workbook re-runs against fresh data each time.
  • Export. Excel, CSV, or PDF. A snapshot of the data as you see it. Best for emailing to people outside the platform or attaching to a report.

Both have their place: the workbook for collaborators who want to keep exploring; the export for the final answer.

When Saiku isn’t the right tool

A few cases where Saiku’s strengths don’t help:

  • Row-level operational queries. “Show me orders 47281 through 47290.” That’s a SQL query, not an OLAP analysis. Use the drillthrough feature (right-click → Drill through) instead.
  • Real-time dashboards. Saiku runs queries on demand. If you need a number that updates every 30 seconds with no human in the loop, a different tool fits better.
  • Free-text search across data. Saiku doesn’t search rows; it aggregates them. For “find every order mentioning ‘urgent’ in the comment field”, look elsewhere.

For everything else — slicing aggregations along dimensions, with or without a chart — Saiku is what you want.

Where to go next