Why don’t we trust our data? By Björn Gerster
It’s no secret that the manufacturing industry is in the midst of a significant transformation, grappling with technological shifts, increased customer expectations, and supply chain vulnerabilities. The effects are starting to show. Our latest Global Business Optimism Insight Report reveals that confidence in supply chain resilience is still slipping. After a sharp 9.7 percent drop in Q3, the index dipped again in Q4, pointing to ongoing uncertainty.
In a landscape this complex, data is a manufacturer’s critical ally, allowing teams to stay agile, anticipate disruption, and make smarter decisions. Yet, in my daily conversations with manufacturing leaders, one thing has become increasingly clear: despite having more data than ever at their fingertips, many still aren’t using it to inform critical business decisions. There’s a clear confidence gap when it comes to actually turning data into action. So why has this happened and how do we fix it?
The root causes of the data confidence gap
The root causes of the data confidence gap- Poor data quality and manual processes
Clean, structured, compliant, and up-to-date data is the foundation of effective decision-making, but many manufacturers still struggle to achieve it. The reliance on manual data collection, a practice still widespread, introduces errors and delays that erode trust in the data from the start. When data is collected manually, it’s susceptible to human error and outdated information, making it unreliable for analysis. These issues often cause leaders to rely on intuition and experience rather than a data-driven approach. While intuition will always have its place, it can’t keep pace with the complexities of modern manufacturing, such as volatile supply chains and technological change. Inaccurate supplier data can lead to sourcing from high-risk partners, or failing to comply with crucial regulations, directly impacting the entire supply chain and the manufacturer’s bottom line.
- Absence of a clear data strategy
The sheer volume of data at the modern manufacturer’s fingertips is often viewed as a challenge, but the real problem isn’t a lack of information, it’s the absence of a clear, coherent plan for how to use it. Many companies struggle with fragmented data, where information is trapped in isolated silos across different systems and formats. This makes it nearly impossible to get a unified, real-time view of operations. Leaders can’t trust what they can’t see, and they become reluctant to make decisions based on an incomplete or disconnected picture.
Without a clear data strategy, this fragmented data is simply noise. It’s a resource that isn’t being leveraged, leaving companies unable to effectively identify trends, optimize processes, or predict future outcomes. This is critical for the supplier lifecycle, where a lack of a trusted data layer prevents a holistic view of potential risks. Without this, manufacturers can’t effectively vet new suppliers, find competitive costs, or manage logistics efficiently. The same challenges appear across sales and marketing. Poor or missing data creates gaps in evaluating market potential, qualifying leads, and analyzing cross-sell and up-sell opportunities. The result is a cycle of hesitation and reliance on intuition, as the data, though plentiful, is not presented in a way that inspires confidence or enables action.
- Organizational and cultural resistance
Even with the right data and technology, a data-driven culture cannot thrive without the right people and mindset. Organizational resistance is a major barrier, with employees and managers who are accustomed to traditional methods being skeptical of new data-driven approaches. This cultural gap often stems from a lack of data literacy and training. Many employees do not have the skills needed to interpret and use data effectively, which leads to a lack of confidence in their abilities and a return to outdated, intuition-based decision-making. This extends to supply chain or sales teams who, without the confidence to use data, may rely on established, but potentially outdated, relationships instead of leveraging insights to build more resilient networks.
Practical steps to overcome the challenges 
So, while the issues I outline above aren’t the only ones, they are the three most common reasons for the data disconnect we see across the manufacturing sector. How can we, as an industry, overcome these challenges?
- Build a data-driven culture
Leadership must champion the use of data in all decision-making processes. It’s about fostering a culture where data is a shared asset and a common language. Encourage collaboration between IT, operations, and executive teams to break down data silos and ensure everyone understands the value of the information being collected. That data must also be shared across the relevant processes and with the right users to ensure it drives meaningful action within the organization.
- Invest in targeted training
To close the data literacy gap, manufacturers need to offer targeted training. This empowers employees to understand and interpret data relevant to their specific roles, enabling them to make smarter decisions and solve problems more effectively.
- Implement a comprehensive data strategy
A comprehensive data strategy is key to transforming raw data into a powerful asset. It begins with improving data quality through robust master data management, ensuring all information, from supplier to customer records, is accurate at the source. This trusted data then enables enhanced visibility, breaking down departmental silos to create a unified platform. This holistic view is crucial for proactively identifying supply chain risks and empowering data-driven sales efforts throughout the customer lifecycle. The final step is leveraging data for strategic insights, moving beyond simple reporting to advanced analytics that support critical decisions on everything from ESG compliance to market positioning.
Ultimately, a strong data strategy shifts an organization from a reactive to a proactive and predictive state, helping it to not just react to change, but to actively thrive in it.
Björn Gerster
Björn Gerster is European Lead Centre of Excellence, Manufacturing at Dun & Bradstreet. Dun & Bradstreet, a leading global provider of business decisioning data and analytics, enables companies around the world to improve their business performance. Dun & Bradstreet’s Data Cloud fuels solutions and delivers insights that empower customers to accelerate revenue, lower cost, mitigate risk, and transform their businesses. Since 1841, companies of every size have relied on Dun & Bradstreet to help them manage risk and reveal opportunity.