Every marketing team wants a single source of truth. It promises accuracy and efficiency.
It’s a catch-all for clarity, alignment and trust. The CFO and the social media exec working from the same numbers, derived the same way, from the same raw data. Better questions. Faster decisions. No more “my spreadsheet says something different.”
However, marketing is become more complex – more channels, more platforms, more data. Pulling it all together into an accurate, robust and meaningful reporting framework is harder than it’s ever been. As more budgets move into digital channels, the stakes are higher. Then there’s the promise of AI.
Tools like ChatGPT, Claude and Gemini are tempting marketing teams with conversational analytics: simple, intuitive and powerful ways to interact with their data. However, AI can only be leveraged if you can feed it robust, well structured and accurate data.
If you want to take advantage of what AI can offer, you need a single source of truth.
What is a Single Source of Truth?
You’ve heard the phrase. Maybe you’ve said it yourself: “We need a single source of truth.”
In theory, brilliant. In practice, elusive – because a true SSOT isn’t something you can buy. It’s something you build, carefully and collaboratively, combining technical infrastructure with organisational agreement and the determination to change how people work.
The scale of the challenge varies with the size of your organisation and the complexity of your data. However, the places where SSOT delivers the most value tend to be larger organisations handling bigger budgets with more disconnected teams – exactly where it’s hardest to achieve.
This guide breaks down what a single source of truth actually requires, technically and organisationally, so you can assess where you are and what it will take to get there.
Why This Matters More Than Ever
There’s a new urgency to getting your marketing data house in order: AI.
However, AI can only be leveraged if you can feed it robust, well structured and accurate data. Feed it inconsistent definitions, fragmented sources and undocumented metrics, and you’ll get confident-sounding nonsense.
A well-built single source of truth isn’t just about better dashboards anymore. It’s the foundation for AI-ready analytics. A proper semantic layer – with clean definitions, consistent dimensions and reliable raw data – means you can point AI tools at your marketing data and actually trust the answers.
This is where the investment in a SSOT will pay dividends you couldn’t have anticipated even two years ago.
What a Single Source of Truth Actually Means
Think of SSOT as having two aspects:
- A technical layer – the infrastructure where clean, modelled data is made available to users
- An organisational agreement – the ownership, governance and ways of working that ensure it succeeds
A more precise definition of the technical requirement:
“A reporting framework underpinned by automated, consistent data acquisition and a centrally-defined, shared data model that guarantees all users have their data requests satisfied by the same raw data, processed in the same way, regardless of context.“
Breaking this into components helps identify stakeholders, technology requirements and the expertise needed – not just to build it, but to maintain and extend it over time.
The Technical Requirements
1. Reliable Raw Data
Everything starts here. If teams are pulling data in different ways, at different times, with different granularity, nothing else matters.
You need automated ETL that imports and stores your data reliably, accessibly and accurately. No manual exports. No “I pulled this yesterday, let me refresh it.” The data arrives consistently, on schedule, at the granularity you need.
2. Transparent, Shared Metric Definitions
This is what most people think of when they hear “single source of truth” – essential, but not sufficient on its own.
If Finance includes VAT in “Spend” and your agency doesn’t, but both call it “Spend,” you have a problem no dashboard can solve.
You need a tool to centrally define and publish a shared data model. Everyone accessing data must go through this model – not around it. And you need the business context to define metrics correctly, plus stakeholders who will communicate and enforce alignment.
You cannot have everyone working from the same raw data but applying their own analytical approach. The system must force data through the same lens for all users.
3. Consistent, Well-Defined Dimensions
If I’m grouping by one definition of “Campaign Type” and you’re using another, our results will differ even with identical metrics.
Dimensions must be unique, serve a single purpose and have clear definitions. If they’re driven by rules, that logic must be visible in a UI – not buried in SQL that nobody has touched for years.
This is where taxonomy becomes critical. Multiple versions of an Excel doc with no validation and free-form inputs everywhere does not count.
4. Automated Monitoring and Data QA
A solid taxonomy is worthless if people bypass it. You need automated monitoring and exception reporting to catch non-compliant campaigns before they pollute your data. Being able to trace who created a rogue campaign means you can have a polite conversation about why the taxonomy tool exists.
5. Filtering Linked to Dimensions
Often overlooked. If our filtering differs, we can have identical metrics and dimensions but still get different outputs.
You must only be able to filter on values produced by your common dimensions. This requires centrally-defined dashboards built and maintained by a core team who understand what numbers each team needs.
The Organisational Requirements
Technology alone won’t get you there.
Determination
Getting teams to abandon familiar processes requires influence. You need stakeholders who can get people to listen, demonstrate the benefits and catch backsliding before it becomes habit again.
Without senior sponsorship, your technical work will gradually come to nothing as people revert to old ways.
Flexibility
This sounds contradictory given the emphasis on consistency, but it’s essential. Different teams need different views of the data, and if small changes require a two-week round trip through an overstretched data team, adoption will stall.
A single source of truth does NOT mean a single view of the data. It means everyone views data through the same lens – but the social team still needs ad-level performance for specific campaigns, while the CFO needs total spend by channel.
If you can’t flex and evolve what you report on without compromising the SSOT, users will revert to source platforms and workarounds.
Guardrails
You need structure and boundaries to enforce rigour. Control who can extend and amend the shared data model. Define access roles for report creation and editing. Without guardrails, you end up back where you started.
Start with lockdown for all but a handful of core users, then gradually unlock flexibility where appropriate.
The Right Balance on Report Authoring
Balancing flexibility in who can create reports versus maintaining consistent views is tricky. The best approach: build rich, detailed dashboards with enough depth that users don’t need to go elsewhere. Most people don’t want to build reports – they want answers.
The Hardest Part: Keeping It Alive
Achieving a single source of truth is only half the battle. Maintaining it – and occasionally policing it – is the real work.
There are no shortcuts. Like planting a tree, you can’t just dig a hole and forget it. You have to keep checking usage data, ensuring people are engaged and catching old habits before they spread.
What Success Looks Like
Once it’s in place:
- You trust your data. No more “let me check my spreadsheet” in meetings.
- You move faster. Questions get answered in minutes, not days.
- You make better decisions. Because you’re arguing about strategy, not methodology.
- Your AI tools actually work. Because they’re built on clean, well-modelled data.
Where to Start
If you’re serious about building a single source of truth, audit where you are:
- How is your raw data being collected? Is it automated and consistent?
- Do you have documented, agreed metric definitions?
- Are your dimensions clearly defined and enforced through taxonomy?
- Who has access to create and modify reports?
- What happens when someone bypasses the system?
The answers will tell you where to focus first.
Building a single source of truth is a journey. If you’d like to talk through where you are and what’s realistic for your organisation, get in touch – no hard sell, just a practical conversation about your data challenges.
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