I'm building a BI layer for operations, sales, and support data

Great use case. A BI layer for operations, sales, and support usually works best when you design it around a few shared business entities and a small number of consistent metrics.

Start with the core questions

Before modeling, define the decisions the BI layer should support:

• Operations: throughput, SLA adherence, backlog, cycle time, capacity
• Sales: pipeline, conversion, win rate, revenue, quota attainment, forecast accuracy
• Support: case volume, first response time, resolution time, CSAT, reopen rate

Use a dimensional model

A good starting point is a star schema or a set of conformed dimensions across domains.

Common fact tables

• fact_orders or fact_revenue
• fact_opportunities
• fact_cases
• fact_tasks / fact_work_items
• fact_activity if you need event-level operational tracking

Shared dimensions

• dim_date
• dim_customer
• dim_account
• dim_product
• dim_region
• dim_user / agent / rep
• dim_channel
• dim_status
• dim_team / org_unit

Using shared dimensions helps you compare metrics across functions, like revenue vs. support load by customer, region, or product.

Recommended layers

A clean BI stack often has these layers:

1. Raw ingestion
   • Pull from CRM, support system, ERP, operational databases
2. Standardized staging
   • Clean types, timestamps, IDs, dedupe, normalize enums
3. Business transformation layer
   • Define canonical metrics, conformed dimensions, SCD handling
4. BI semantic layer
   • Expose trusted measures and dimensions to dashboards/tools

Key modeling decisions

1. Grain first

For every fact table, define one row = one:

• order
• opportunity
• support ticket
• support ticket status change
• operational event

2. Canonical IDs

Create mapping logic for:

• customer/account IDs across systems
• employee IDs for reps/agents
• product/service catalog IDs

3. Time handling

Support and ops often need:

• created_at
• closed_at
• updated_at
• first_response_at
• SLA deadline timestamps
• timezone normalization

4. SCD strategy

For dimensions like customer, rep, or product:

• use Type 2 if historical reporting matters
• use Type 1 for simple overwrite fields

Metric examples

A few cross-functional metrics worth standardizing:

• New revenue
• Open pipeline
• Case backlog
• SLA hit rate
• Average resolution time
• Conversion rate
• Gross margin
• Orders per rep
• Cases per account
• Revenue per support contact

Dashboard structure

I’d usually split into:

• Executive summary
• Sales performance
• Operations health
• Support health
• Customer 360
• Cohort/trend analysis
• Drill-down detail pages

Governance

Make sure you have:

• metric definitions documented
• ownership for each dataset
• freshness SLAs
• data quality checks
• row-level security if needed
• a single source of truth for KPIs

Practical next step

If you want, I can help you design one of these next:

1. a data model / star schema
2. a dbt transformation structure
3. a BI metric layer definition
4. a dashboard KPI list
5. a source-to-target mapping template

If you tell me what systems you’re using (e.g. Salesforce, Zendesk, NetSuite, Snowflake, Postgres), I can tailor the architecture.