Manufacturing’s Data Confidence Crisis

The manufacturing sector stands at a crossroads. The promise of digital transformation, AI-driven efficiency, and hyper-resilient supply chains is colliding with a fundamental lack of confidence in its own data.

The latest Dun & Bradstreet Manufacturing Pulse Survey 2025 reveals a startling data confidence gap.

This widespread data distrust is no longer an IT issue like it may have been previously, in today’s age it is instead a critical vulnerability that is actively eroding operational agility, stalling innovation, and stifling growth across the industry.

 

What Happens When You Can’t Trust Your Data?

When data is unreliable, leaders revert to intuition and experience, a necessary fallback that cannot keep pace with the complexities of modern manufacturing, such as volatile supply chains and technological change. The practical costs of this lack of confidence are significant, impacting both top-line growth and bottom-line resilience:

• Growth is Stalled: Our report found that poor data creates critical blind spots that hamper manufacturers going for growth. 73% of firms feel their data won’t help them find new customers, 70% can’t track return on investment on projects, and 68% lack the data needed to identify the best markets to sell into.  
• Resilience is Compromised: 70% of respondents feel unable to find alternative suppliers with their current data should issues arise, and 65% cannot use it to identify supply chain efficiencies. This creates vulnerability at a time when resilience is the industry’s most urgent goal

The Root Causes of the Confidence Gap

Björn Gerster, European Lead Centre of Excellence, Manufacturing at Dun & Bradstreet argues that the data confidence crisis is a solvable problem rooted in three fundamental issues:

1. Poor Data Quality and Manual Processes: A heavy reliance on manual data collection introduces errors, delays, and outdated information, eroding trust from the start. The Pulse Report confirms this reliance on analog processes, showing that around a third of firms say their key decision-making processes are mostly, or completely, manual. This is compounded by data quality issues, with 41% of manufacturers saying they actively distrust the data they use for supply chain operations. 
2. Absence of a Clear Data Strategy (Silos and Duplicates): The core infrastructure is often fragmented. Data is trapped in isolated silos across different systems and formats, making a unified, real-time view impossible. In fact, 51% of manufacturers report their data is siloed in disparate tools and systems, while 54% struggle with duplicate data residing in several systems.  
3. Organisational and Cultural Resistance: Even with the right tools, a data-driven culture cannot thrive without the right people and mindset. Employees accustomed to traditional methods are often skeptical of new, data-driven approaches due to a lack of data literacy and training.

Regional Variations in the Data Confidence Crisis

While the challenge of data distrust is prevalent across all markets, the manifestation of the problem varies significantly depending on local nuances:

• US and Switzerland Struggle with Fragmentation: The US is most likely to have duplicate data across several systems (58%). Meanwhile, Switzerland comes out on top for poor data management, with 61% reporting siloed information. 
• Confidence Levels Split: Manufacturers in the UK, US, and Germany generally report higher trust, with at least 59% reporting they trust their data. Conversely, manufacturers surveyed in Sweden and Switzerland show a significant lack of confidence, with 55% reporting data distrust. 
• A Glimmer of Hope: Despite high levels of siloed information, Swiss manufacturing firms also report high adoption of a holistic view, with 63% claiming to have a 360-degree view of third parties in place, shared across the business. This suggests that while fragmentation is a challenge, leaders in some regions are prioritising the investment needed to overcome it.

The AI Innovation Barrier

The consequences of this foundation of distrust impact the future of the manufacturing sector. Digital transformation and AI promise efficiency and agility, but only if the data foundation is solid. 

Unfortunately, the data deficiencies are already stalling progress. A striking 44% of manufacturers have seen AI projects fail due to poor data quality. Yet, investment in this critical foundation lags. Only 50% of manufacturers are enhancing their data with insights from third-party data providers, and just 33% are investing in unified/cloud data platforms that allow for integrations and strong analytical capabilities. While a promising 56% of firms have implemented a 360-degree view on business partners, a significant 46% still struggle to share and access this foundational information across their organisation.

Master Data Management and the 360-Degree View

The key to this is a robust Master Data Management (MDM) strategy focused on achieving a 360-degree view of all business partners.

MDM involves consolidating business partner records into a central data warehouse, integrating internal ERP and CRM data with external reference data. This process is often underpinned by a universal identifier, such as the Dun & Bradstreet D-U-N-S number, which acts as a unique Company ID to unify scattered data across systems and departments. 

A unified view, successfully implemented by 56% of firms, provides the foundation for:

• Compliance Automation: Allowing for automatic screening against sanctions, watch lists, and UBO registers during supplier onboarding. 
• Predictive Analytics: Providing clean, structured data for Al-powered use cases, such as demand forecasting and predictive analytics. 
• By implementing MDM and creating a single source of truth, manufacturers can move from siloed, manual data to informed decision-making. 

The Path to Data Confidence

To close the data confidence gap, manufacturers must move beyond fragmented legacy systems toward unified, scalable data architectures. This transformation shifts an organisation from a reactive state to a proactive and predictive one. 

Practical steps to achieve this include: 

• Implement a Unified Data Architecture: Break down existing data silos, merge disparate data sets, and eliminate duplicates. This involves shifting to unified, scalable data architectures, including investing in cloud platforms with real-time analytics. 
• Establish Data Governance: Roles and responsibilities must be clarified, and Master Data Management (MDM) must be rooted in the corporate strategy. This involves establishing data governance frameworks to ensure trust and accessibility. 
• Automate Core Processes: Automate analog and manual processes, such as supplier onboarding and risk assessments, to increase efficiency and ensure the data underlying the processes is correct, complete, and up-to-date. 
• Embrace Third-Party Enrichment: To ensure data accuracy and completeness, manufacturers should match their own data against a reliable ‘Reference Data Universe’ to standardise, complete, and enrich the data with critical external insights. 
• Invest in Data Literacy: Offer targeted training to close the data literacy gap, helping employees interpret and use data effectively. 

The manufacturing industry is in flux, and data is critical to separating those who will fall behind from those who will continue to grow. For manufacturers, the first decisive step is to build the foundation of trust in their own data.