Closing the data confidence gap: How manufacturers can build Trust and drive growth

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: only 36% of manufacturers feel they can make informed business decisions with their existing data. This distrust is no longer just an IT issue; it’s a critical vulnerability.

What happens when you can’t trust your data?

When data is unreliable, leaders revert to intuition and experience. However, whilst this knowledge is important, modern manufacturing and today’s increasingly volatile and complex supply chains, demand a data-driven approach. The practical costs can be significant:

• Growth Stalled: The report finds 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 Compromised: 97% have experienced negative impacts from complex supply chains, 70% cannot find alternative suppliers with current data, and 65% can’t identify supply chain efficiencies, at a time when resilience is critical.

Consider the example of a mid-sized automotive parts manufacturer navigating fluctuating raw material costs, tariffs and potentially sanctioned parties in its supply chain. Without accurate data, procurement teams struggle to identify cost-effective alternatives, may compromise regulatory compliance, leading to missed savings opportunities, potential fines and production delays. Their marketing may be inefficient due to poor segmentation, targeting and lack of personalisation.

Root causes of the confidence gap

Björn Gerster, European Lead Centre of Excellence, Manufacturing at Dun & Bradstreet, identifies three key issues:

1. Poor Data Quality & Manual Processes: Heavy reliance on manual data collection introduces errors and delays, and information becomes outdated quickly. Yet, around a third of firms say key decision-making processes are mostly manual. This is compounded by data quality issues, with 41% distrusting their current supply chain data.

2. Fragmented Infrastructure: Data silos and duplicates make a unified view impossible, and over half of manufacturers struggle with both of these issues.

3. Cultural Resistance: Even with the right tools, a data-driven culture cannot thrive without the right people and mindset. Lack of data literacy fuels scepticism.

The innovation barrier

The consequences of this foundation of distrust impact on 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 AI projects in manufacturing fail due to poor data quality. Yet, investment in this critical foundation lags.
• Only 50% are enhancing their data with insights from third-party data providers
• Just 33% are investing in cloud data platforms that allow for integration 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.

The key to trust? Master data management

A robust Master Data Management (MDM) or foundational data strategy is essential. By consolidating and centralising records, and integrating ERP/CRM data with external reference data (often using universal identifiers like the D-U-N-S number), manufacturers can achieve:

• A single source of truth: Make the move from siloed data to informed decision making with the same information shared across the company.
• Compliance Automation: Automatic screening against sanctions, watch list, tariffs and more, not only during onboarding but with monitoring and change alerts on an ongoing basis.
• Scaling AI and tech initiatives: Clean, structured data for AI-powered use cases and to flow through the technology stack.

Manufacturers that prioritise data integrity are already seeing tangible benefits. Companies with robust data foundations, including strong data management, report faster onboarding of suppliers, reduced compliance risks, and improved customer acquisition strategies.

For example, a large global exporter cut compliance screening time in half using Dun & Bradstreet data and gained a deeper understanding of who they were really doing business with, safeguarding their reputation. An electronics manufacturer mapped global supplier relationships, assessed cluster risks and improved financial reporting accuracy.

Rethinking Data: From Risk to Strategic Advantage in Manufacturing. Gregor Hähner from Dun & Bradstreet highlights the impact of data quality and AI on driving digital transformation in the manufacturing industry.

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.

Looking ahead

The next frontier for manufacturing is predictive and prescriptive analytics powered by AI. But these innovations hinge on a single prerequisite: trusted, high-quality data. Firms that act now to close the confidence gap will not only safeguard resilience but also unlock new growth opportunities in emerging markets.