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18.06.26
Single Source of Truth in product development
SSOT: Why data quality determines the quality of decision-making
In today’s product development, data-driven decision-making is essential – yet the reality is often quite different: decisions are based on incomplete, outdated or contradictory information. The real problem is rarely a lack of data, but rather the unreliability of the data itself.
In today’s product development, data-driven decision-making is essential – yet the reality is often quite different: decisions are based on incomplete, outdated or contradictory information. The real problem is rarely a lack of data, but rather the unreliability of the data itself.
Organically developed system landscapes rather than a unified database
Medium-sized industrial companies in particular tend to have IT landscapes that have evolved over time: CAD, PDM, ERP and Excel all coexist. Data is maintained in multiple places, versions are not uniquely identified, and responsibilities are unclear.
In day-to-day operations, this may ‘somehow’ work. However, as soon as decisions need to be made more quickly, are more complex or span multiple departments, the limitations become clear: without a consistent data foundation, every decision becomes a guess.
What does ‘single source of truth’ really mean in engineering?
The term is often used, but its meaning is frequently unclear. In the context of product data management, it does not mean that all data must be stored in a single system. Rather, it refers to a federated data architecture with clear responsibilities:
1
Unambiguous database
Consistent, non-contradictory information across all systems.
2
Clear responsibility for data
Every data point has a designated owner – there are no grey areas.
3
Traceable versioning
Changes are fully documented and reproducible.
4
Seamless integration
Information from development, manufacturing and service is integrated.
Data quality = quality of decision-making
The more complex products and processes become, the more the quality of decisions depends on the quality of the underlying data. The figures speak for themselves:
- According to Gartner, poor data quality costs companies an average of $12.9 million per year.
- According to McKinsey, 20 per cent of working time in engineering teams is lost searching for data.
Typical consequences of poor data quality
- Incorrect design decisions and costly rework
- Unnecessary iterations during development – wasted time and higher costs
- Manufacturing errors leading to quality defects and customer complaints
- Delayed market launch and competitive disadvantages
What a clean database enables
- Faster decision-making thanks to immediately available, reliable data
- Significant reduction in coordination effort
- Early identification of risks in the development process
- More stable processes in development, manufacturing and service
A single source of truth is not merely an IT issue – it is a crucial factor for the success of the entire organisation.
Typical challenges in developing a consistent PLM strategy
- Existing systems are often not properly integrated or use different data models.
- There is no standardised product data management system.
The role of PLM and the digital thread
From a technological perspective, two concepts play a key role in the journey towards a single source of truth: PLM and the digital thread.
PLM – Product Lifecycle Management
Modern PLM goes far beyond traditional document management. It integrates development, manufacturing and service within a single, end-to-end data model:
- Bill of Materials (BOM) Management – consistent bills of materials across all departments
- Change & Configuration Management – comprehensive change tracking
- System integration (CAD, ERP, MES) – an interconnected system landscape
- ALM for software-intensive products
- SLM – Service Lifecycle Management for the aftermarket
Digital Thread
The Digital Thread links data across systems and processes – from development and simulation through to manufacturing and service. It reveals interrelationships and ensures end-to-end traceability.
Together, PLM and the Digital Thread enable end-to-end data availability, consistent information across the organisation and better traceability of decisions – the technological foundation for a functioning single source of truth.
From data chaos to a unified database: 5 steps
A practical approach – iterative rather than a ‘big bang’ project:
- Analysis of the existing data landscape: Which systems are in place? Where is data generated? Where are there gaps and redundancies?
- Defining a target vision: What should the data structure look like in future? Which systems will play which roles in product data management?
- Clarification of responsibilities and data governance: Who is responsible for which data? How is quality measured and ensured?
- Technical integration and implementation: Systems are connected, data flows harmonised and interfaces standardised.
- Enablement and continuous development: Staff are involved, and processes are continuously improved and adapted.
Without clean data, there can be no sound decisions
The debate surrounding the ‘single source of truth’ is often framed in technical terms. In practice, however, it becomes clear that the real key lies in a company’s decision-making capacity.
Those who do not have their product data management under control will inevitably make decisions more slowly, bear greater risks and face more coordination work. A consistent, reliable database, on the other hand, creates the conditions for:
- Faster development cycles
- Better cross-departmental collaboration
- Robust, well-founded decisions
Data quality is not a secondary consideration – it is the foundation for effective engineering.
Ready for the next step?
Would you like to bring transparency to your product data and create a robust basis for decision-making? The d.u.h.Group will work with you to analyse your existing system and data landscape and develop a realistic roadmap for your single source of truth.
Get in touch – we’ll support you on your journey towards a consistent data foundation.
Topics
Technologies
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