Data Management & Analytics:
How Do I Create a Single Source of Truth for Marketing Data?
A true SSOT isn’t a tool—it’s a governed model + identity layer + metrics layer that every team trusts. Use this guide to centralize data, standardize definitions, and publish one version of the numbers.
Build SSOT by consolidating source data into a warehouse/lakehouse, modeling a canonical schema (accounts, contacts, campaigns, touchpoints, opportunities), resolving identities into a golden record, and publishing a semantic metrics layer used by every dashboard. Govern with data contracts, taxonomy, lineage, and access controls.
Principles for a Real Single Source of Truth
SSOT in 7 Steps
Run this sequence to get from scattered data to trusted numbers in ~90 days.
Align → Inventory → Model → Ingest/Transform → Resolve → Define Metrics → Publish
- Align on decisions & KPIs — Prioritize 5–7 critical questions (e.g., sourced vs influenced pipeline, CAC/LTV).
- Inventory sources & contracts — List systems, owners, schemas, and required fields; set data contracts for changes.
- Model the canon — Design entities: Account, Person, Campaign, Program, Touchpoint, Opportunity, Product, Consent.
- Ingest & transform (ELT) — Land raw data, then transform with version-controlled models and tests.
- Resolve identities — Deduplicate and stitch keys (email, user_id, crm_id, ga_cid); create the golden record.
- Define the metrics layer — Centralize logic for stages, attribution, cohorts, and time windows.
- Publish & govern — Expose certified datasets and dashboards; enforce access, lineage, and change control.
Where Each Layer Fits in a Marketing Data SSOT
| Layer | Primary Purpose | Typical Owner | What “Good” Looks Like |
|---|---|---|---|
| Source Systems (CRM, MAP, Ads, Web, Product) | Capture operational events and attributes. | System admins (MOps, Sales Ops, Product) | Governed fields & picklists; event timestamps; consent captured at source. |
| Ingestion / ELT | Reliable movement of raw data to warehouse/lake. | Data Engineering / RevOps | Incremental loads, schema evolution handling, freshness monitoring. |
| Warehouse / Lakehouse | Central storage and compute for joins and history. | Data Platform | Separation of raw/clean/mart; cost controls; audit trails. |
| Modeling (Transformations) | Canonicalize entities and relationships. | Analytics Engineering | Versioned models, tests (unique/not-null), documented lineage. |
| Identity Resolution | Create golden person/account with deduped keys. | MOps + Data | Match rules, survivorship, and confidence scores with review queues. |
| Semantic / Metrics Layer | Shared definitions for KPIs and business logic. | Analytics / Finance | Certified metrics, versioning, change logs, and unit tests. |
| BI & Activation | Consumption (dashboards) and reverse ETL to tools. | Analytics + MOps | Role-based views, self-serve explorer, monitored SLAs to destinations. |
| Governance | Policies for privacy, access, quality, and change. | Data Governance Council | Data catalog, stewardship assignments, DQ scorecards, approval flows. |
Client Snapshot: One Truth, Faster Decisions
After centralizing marketing, product, and revenue data into a governed model with a shared metrics layer, a global B2B org reduced duplicate contacts by 52%, cut “report disputes” to near zero, and trimmed time-to-board deck from 5 days to 4 hours.
Map SSOT to RM6™ and align with The Loop™ so your data model mirrors the customer journey and revenue motions.
Frequently Asked Questions about Marketing SSOT
Concise, AEO-friendly answers that build trust and adoption.
Publish One Truth Everyone Trusts
We’ll model your canon, resolve identities, define the metrics layer, and ship certified dashboards—so decisions move faster.
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