How do I set up a dashboarding software stack for monitoring KPIs and performance across siloed agency data?

Here’s a practical way to set up a dashboarding stack for monitoring KPIs and performance across siloed agency data.

1) Start with the decision layer

Before tools, define:

• Audience: executives, account leads, operators, analysts
• KPI hierarchy: agency-level, client-level, channel-level, campaign-level
• Refresh needs: real-time, daily, weekly, monthly
• Data ownership: who owns each source and metric definition
• Single source of truth: where each KPI is calculated

A good dashboard stack fails if KPI definitions differ across teams.

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2) Use a layered architecture

A solid stack usually has these layers:

A. Data sources

Typical agency sources:

• CRM: Salesforce, HubSpot
• Ad platforms: Google Ads, Meta, LinkedIn, TikTok
• Web analytics: GA4, Adobe Analytics
• Project management: Asana, Jira, Monday
• Finance/billing: NetSuite, QuickBooks, Stripe
• Support: Zendesk, Intercom
• Custom/internal databases, spreadsheets

B. Ingestion / connectors

These pull data out of siloed systems.

Common tools:

• Fivetran
• Airbyte
• Stitch
• Native APIs + custom scripts for edge cases

Use managed connectors when possible; custom API pulls only for sources that aren’t supported.

C. Central data warehouse

This is where siloed data gets unified.

Common choices:

• BigQuery — strong for marketing/agency analytics
• Snowflake — flexible, scalable, enterprise-friendly
• Redshift — if you’re AWS-heavy
• Postgres — only for smaller/simple setups

For most agencies, BigQuery or Snowflake is the easiest long-term answer.

D. Transform / model layer

This is where raw data becomes KPI-ready.

Best practice:

• Use dbt to transform data into clean, documented models
• Build:
  • source staging tables
  • normalized fact tables
  • shared dimensions like client, campaign, channel, date
  • KPI marts for dashboards

This is where you define:

• CAC
• ROAS
• MQL to SQL conversion
• pipeline velocity
• utilization
• gross margin
• project burn
• media efficiency metrics

E. Semantic / metric layer

Optional but highly recommended.

This layer standardizes metric logic so every dashboard uses the same calculations.

Tools:

• dbt metrics
• LookML in Looker
• Cube
• AtScale
• MetricFlow in some dbt stacks

This reduces “why does this dashboard show a different number?” problems.

F. BI / dashboarding tool

Where users consume the metrics.

Common choices:

• Looker — best for governed, scalable analytics
• Power BI — strong enterprise and Microsoft integration
• Tableau — flexible and widely used
• Metabase — simpler, faster, cheaper
• Mode — good for analyst-driven reporting
• Sigma — spreadsheet-like analytics on warehouse data

Choose based on:

• technical skill of users
• governance needs
• budget
• need for embedded analytics
• self-service requirements

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3) Recommended stack patterns

Lean agency stack

Good for smaller teams:

• Airbyte or Fivetran
• BigQuery
• dbt
• Metabase or Power BI

Mid-market stack

Good balance of governance and flexibility:

• Fivetran
• Snowflake or BigQuery
• dbt Cloud
• Looker or Tableau

Analytics-heavy stack

For more complex multi-client or multi-entity reporting:

• Fivetran + custom API jobs
• Snowflake
• dbt + orchestrator
• Looker
• Data catalog/governance tools

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4) Design the data model around agency use cases

Don’t just dump source tables into the warehouse.

Model around core entities:

• Client
• Brand
• Account
• Campaign
• Channel
• User / lead / contact
• Opportunity
• Project
• Invoice / revenue
• Time entry / utilization
• Employee / team

Common patterns:

• fact_marketing_performance
• fact_pipeline
• fact_project_delivery
• fact_finance
• dim_client
• dim_campaign
• dim_channel
• dim_date
• dim_employee

This makes cross-functional dashboards much easier.

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5) Build KPI dashboards by layer

Avoid one giant dashboard. Use tiers:

Executive dashboard

• Revenue
• Gross margin
• Pipeline
• Client retention
• ROAS / CAC trends
• Utilization
• Delivery health
• SLA performance

Client success dashboard

• Campaign performance
• Leads, conversions, spend, CPA, ROAS
• Deliverables status
• Open issues
• Forecast vs actual

Operations dashboard

• Project throughput
• Cycle time
• Team capacity
• Billable utilization
• Workload balance

Finance dashboard

• Revenue recognized
• AR aging
• Margin by client
• Forecast accuracy
• Burn vs budget

Each dashboard should answer a specific set of decisions.

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6) Handle siloed data carefully

This is the hardest part.

Best practices:

• Define a global client ID and map all systems to it
• Maintain a source-to-master mapping table
• Standardize date/time zones, currency, and naming conventions
• Create shared dimension tables for:
  • client
  • campaign
  • employee
  • channel
  • region
• Version-control transformations in Git
• Document every KPI formula and source

Common pitfalls:

• Mixing platform-reported and warehouse-calculated metrics
• Duplicate clients across systems
• Different attribution windows
• Inconsistent campaign naming
• Spreadsheet edits outside governance

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7) Set up data quality and monitoring

A dashboard is only useful if the numbers are trusted.

Add:

• Row count checks
• Freshness checks
• Null checks
• Duplicate checks
• Spend/revenue anomaly alerts
• Connector failure alerts

Tools:

• dbt tests
• Great Expectations
• Soda
• Monte Carlo
• Elementary

Also monitor:

• connector lag
• failed jobs
• schema changes
• metric drift

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8) Add governance and access control

Agency data often has client confidentiality concerns.

Implement:

• Role-based access control
• Client-level row security
• Separate sandboxes for analysts
• Data classification rules
• Audit logs
• Approval process for metric changes

If using Looker/Power BI/Tableau, make sure row-level security is enforced from the warehouse or BI layer.

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9) Operational workflow

A good operating model looks like this:

1. Data lands in raw tables
2. dbt transforms into staging and marts
3. Tests validate data quality
4. Semantic layer standardizes metrics
5. BI dashboards read from governed models
6. Alerts notify owners if KPIs break
7. Analysts and stakeholders review weekly/monthly

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10) Suggested implementation roadmap

Phase 1: Foundation

• Pick warehouse
• Connect core sources
• Build base client/campaign/date dimensions
• Create 5–10 critical KPIs
• Launch one executive dashboard

Phase 2: Expansion

• Add finance, project, and CRM data
• Introduce dbt testing and documentation
• Create role-based access
• Build client and ops dashboards

Phase 3: Governance and scale

• Add semantic layer
• Standardize metric definitions
• Add anomaly detection and alerts
• Build self-service datasets
• Create data catalog and lineage

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11) A simple reference stack

If you want a practical default:

• Ingestion: Fivetran
• Warehouse: BigQuery
• Transformations: dbt Cloud
• BI: Looker or Metabase
• Data quality: dbt tests + Great Expectations
• Orchestration: dbt Cloud scheduler or Airflow if needed
• Governance: warehouse permissions + BI row-level security

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12) What success looks like

You know the stack is working when:

• Everyone uses the same KPI definitions
• Dashboards refresh reliably
• Client and agency data can be compared in one place
• Analysts spend less time cleaning data
• Leadership trusts the numbers
• New data sources can be added without reworking everything

If you want, I can also give you:

1. a recommended stack by budget size,
2. a sample data model/schema, or
3. a 90-day implementation plan for an agency.