Data, CRM and the Reality Behind “Single Source of Truth”

Introduction

“Single source of truth” is one of the most widely used—and misunderstood—concepts in MarTech.

In theory, it sounds simple: one unified view of the customer, accessible across the organisation.
In practice, it is far more complex.

Many organisations invest heavily in building centralised data structures, only to find that the result is difficult to maintain, slow to evolve, and hard to use.

The problem is not the ambition. The assumption is that perfection is required to create value.

The Real Problem

Why “single source of truth” rarely works as expected

The idea of a fully unified data source is appealing—but often unrealistic.

Common challenges include:

• Data is spread across multiple systems and platforms
• Inconsistent data quality and definitions
• Complex integrations that are difficult to maintain
• Long implementation cycles that delay value

As a result, organisations end up with:

• Centralised data that is hard to activate
• Systems that require constant maintenance
• Teams that lack access to the data they actually need

The pursuit of a perfect data model often slows progress rather than enabling it.

From Strategy to Execution

What actually needs to change

Instead of aiming for perfection, organisations need to focus on usability.

This means:

• Accepting that not all data needs to be centralised
• Prioritising data that supports real use cases
• Creating flexible structures that evolve over time
• Enabling teams to access and use data in their workflows

The goal is not a perfect system.
The goal is a system that works in practice.

Common Mistakes to Avoid

Where companies go wrong

Typical pitfalls include:

• Trying to centralise all data at once
• Overengineering data models
• Prioritising structure over usability
• Delaying activation while waiting for “complete” data

These mistakes reduce speed and impact.

Key Components That Make It Work

What to focus on

To build a data and CRM setup that actually delivers value:

• Use-case driven data strategy
  Start with how the data will be used
• Pragmatic integration
  Connect what is needed—not everything
• Accessible data
  Ensure teams can actually use it
• Continuous improvement
  Evolve the data model over time

These principles create a balance between structure and flexibility.

How I Would Approach This in Practice

A simple, proven approach

1. Define key use cases for data and CRM
   What decisions or actions should the data support?
2. Prioritise and integrate only what is needed
   Focus on high-value data first
3. Enable quick activation and iterate
   Use the data early, improve continuously

This approach creates value faster and reduces complexity.

Conclusion

From perfect data to usable data

The concept of a “single source of truth” is not wrong—but it is often misunderstood.

Organisations that succeed do not aim for perfection.

They focus on building data and CRM capabilities that work in practice.

That is how data creates real business value.

Article series: Making MarTech Work in Practice

• What MarTech Really Is and Why It Matters for Your Business
• Why MarTech Fails in Most Organisations
• Why MarTech Strategy Alone Is Not Enough
• How to Turn MarTech Strategy into Execution
• How to Build a MarTech Roadmap That Works
• Data, CRM and the Reality Behind “Single Source of Truth”
• Omnichannel Marketing That Actually Works
• Marketing Automation That Drives Business Value
• Organisation, Roles and Ways of Working That Drive MarTech Results
• Measuring What Actually Drives Business Value
• How I Would Make MarTech Work in Your Organisation