Multi-dimensional analysis using cubes

business analytics

slug: recipe-business-analytics-multi-dimensional-analysis-using-cubes

Recipe: Multi-dimensional analysis using cubes

category: business analytics

Problem

You need to analyze business metrics across multiple dimensions:

• summarize sales by product, region, and time period
• identify trends, top performers, or underperforming segments
• prepare data for dashboards or reports

Solution

Follow these steps to build and analyze cubes:

• load the relevant datasets
• define the dimensions (e.g., product, region, date) and measures (e.g., sales, quantity)
• apply cube step to generate aggregated multi-dimensional data
• optionally calculate additional derived metrics
• visualize or report the results

Step Sequence

load step -> cube step -> calculate step -> chart step

Input Datasets

• transactions_clean — cleaned transactional data
• products_standardized — standardized product dataset
• regions — reference table for regions or locations
• Notes: datasets must include dimension keys and numeric measures

Output Dataset

• sales_cube — multi-dimensional aggregated dataset
• Notes: can be used for reporting, dashboards, or further analytics

Step-By-Step Explanation

Step	Purpose	Notes
load step	Load datasets	Supports local file, database, or API sources
cube step	Aggregate metrics across dimensions	Example: total sales by product × region × month
calculate step	Compute derived measures	Example: % of total sales, growth rates
chart step	Visualize cube results	Optional pivot table, bar chart, heatmap, or cube view

Variations & Extensions

• Add multiple measures (e.g., revenue, quantity, profit) to the cube
• Slice and dice using filter step for subsets of interest
• Combine with dashboard step or [step-report] for interactive analysis
• Integrate with [step-rank] to highlight top products or regions

Concepts Demonstrated

• Multi-dimensional aggregation
• Cube slicing and dicing
• Derived metrics and ratios
• Sequencing analytics for business reporting

Related Recipes

• Customer segmentation using RFM analysis
• Product contribution analysis
• Ranking observations or variables

Notes & Best Practices

• Ensure all dimension keys are standardized and consistent
• Check for missing data or mismatched joins to avoid incorrect aggregations
• Document measures and dimensions for reproducibility

Metadata

title: "Multi-dimensional analysis using cubes"
category: "business analytics"
difficulty: "Intermediate"
tags: [cube, multidimensional, aggregation, reporting]
inputs: [transactions_clean, products_standardized, regions]
outputs: [sales_cube]
steps: [step-load, step-cube, step-calculate, step-chart]
author: "Tom Argiro"
last_updated: "2025-10-25"
doc_type: "recipe"