Relying on untrusted data in a mid-market manufacturing environment is like trying to bake a complex recipe with an uncalibrated scale. Even if you have all the right ingredients (the data), if you don’t trust the scale’s measurements, you are likely to ignore the numbers and “guess” based on how the dough feels. This approach risks ruining your batch because the margin for error in baking is very slim.
According to various sources, there is a significant “data confidence gap” in the manufacturing sector; only 36% of manufacturers believe they can make informed business decisions based on their existing data. For mid-market manufacturers—defined as those with $50 million to $1 billion in annual revenue—this lack of trust is particularly critical, as they have no margin for error compared to global enterprises.
Several root causes contribute to this distrust in their data:
- Poor Data Quality
A primary reason for this distrust is the industry’s continued reliance on manual data collection, which remains widespread. Manual processes introduce human errors, delays, and quickly outdated information. Consequently, 41% of firms specifically distrust their supply chain data, leading many leaders to revert to intuition and experience rather than data-driven approaches because they find the numbers unreliable.
- Data Silos
Mid-market manufacturers often struggle with data trapped in isolated silos across disparate systems such as ERP, MES, CRM, and IoT platforms. This fragmentation makes it nearly impossible to achieve a unified, real-time view of operations. When different teams pull conflicting numbers or KPIs, it further undermines overall trust in the data’s accuracy.
- No Data Strategy
Many organizations suffer not from a lack of information but from the absence of a coherent plan for utilizing it. Experts note that a “good AI strategy is a good data strategy,” yet many mid-market firms have not yet “put their data house in order” through consistent methodologies for recording transactions or tracking inventory. Without proper data hygiene and governance, the massive amounts of data collected daily remain “noise” rather than a strategic asset.
- Literacy Gaps
A data-driven culture cannot thrive if employees are skeptical of new approaches or lack the skills to interpret the information. This lack of data literacy often leads staff to revert to outdated, relationship-based decision-making instead of leveraging insights. Furthermore, over 60% of AI projects fail in this sector, often due to poor data integration and governance rather than the algorithms themselves, which reinforces internal skepticism.
- Technical Complexity
Organizations frequently find themselves “stuck in pilots” that fail to progress toward tangible outcomes. Navigating the technical complexity of modern platforms while facing “information overload” makes it difficult for leaders to find the “trusted insights” needed to address mission-critical priorities.
