I started with a fact table around support tickets and built out dimensions for customers, agents, and issue categories.
Start by clarifying the business goals and key metrics for the customer support platform, then propose a dimensional model (star schema) with a central fact table for support interactions and dimension tables for customers, agents, time, and products. Walk through the table structures, relationships, and how this design supports common reporting needs like ticket volume, resolution time, and agent performance, while discussing trade-offs between normalization and query performance.
Pro tip: Emphasize how your schema supports slowly changing dimensions (SCDs) for customer and agent attributes, as this shows you understand real-world data evolution and historical reporting accuracy.
Ask questions to understand the key business questions, reporting needs, and data sources. Identify metrics like ticket volume, first response time, resolution time, and customer satisfaction.
Propose a central fact table (e.g., fact_support_interaction) at the grain of one row per ticket or interaction, containing foreign keys to dimensions and numeric measures like duration and satisfaction score.
Create dimension tables for customer, agent, time, product, and channel, with descriptive attributes. Discuss how to handle slowly changing dimensions (Type 2) for customer and agent details.
Explain the star schema with foreign key relationships from the fact table to dimensions. Mention potential snowflaking for hierarchical dimensions like product categories, and trade-offs.
Describe how the schema supports common queries (e.g., aggregations by time, agent, product) and discuss indexing, partitioning, and materialized views for performance.
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