Widespread Data Issues in Manufacturing
The Data Dilemma: Unpacking the Challenges in Manufacturing
Manufacturers stand at the crossroads of innovation and inertia. At the heart of this conundrum lies a familiar yet vexing issue: data. Both the Hexagon Advanced Manufacturing Report and the ESG State of DataOps reveal a unified story—data is simultaneously the industry's greatest asset and most insidious challenge. To thrive, manufacturers must address the widespread issues surrounding data quality, accessibility, and integration.
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The Scope of the Problem
Manufacturers today are drowning in data but starving for insight.
And before you blame your dashboards, analytics vendors, or data scientists, take a step back. Because the issue often isn’t with your visibility layers…
It’s with your data foundation.
Here’s how the dysfunction shows up:
Your machine data is accurate—but trapped in a siloed historian.
Your quality data is complete—but buried in PDFs or Excel sheets.
Your inventory data is real-time—but misaligned with what production sees.
Your maintenance data is detailed—but isn’t used to improve uptime or reliability.
Your operator data is digital—but missing context and interconnection.
This isn’t just inconvenient—it’s operationally dangerous. Disconnected data leads to poor decisions, failed initiatives, and wasted investments in automation and AI.
You can’t automate inconsistency.
You can’t optimize chaos.
You can’t AI your way out of a mess you haven’t mapped.
The statistics are startling: 98% of manufacturers report data-related challenges. This near-universal figure isn’t merely a reflection of operational inefficiencies—it signals a structural issue at the heart of modern manufacturing. Data inaccuracies, incompleteness, and obsolescence not only hinder decision-making but also jeopardize the effectiveness of advanced technologies such as automation and AI.
Similarly, ESG’s findings highlight the pervasiveness of data-related struggles across industries. With 33% of organizations identifying data quality management as their most resource-intensive activity and 42% citing difficulties in integrating disparate sources, the challenge spans far beyond the manufacturing floor. It speaks to a broader failure to harmonize systems and processes in an increasingly digital and interconnected world.
A System Out of Sync
The Hexagon report paints a picture of an industry caught between aspiration and reality. On the one hand, manufacturers are eager to harness technologies like automated quality control and predictive analytics. On the other, they are encumbered by silos, outdated data, and integration challenges. This dissonance illustrates a system out of sync—a dynamic where ambition continually outpaces infrastructure.
ESG’s emphasis on multi-cloud environments further illuminates the issue. Half of all organizations surveyed use three or more cloud providers. While this setup promises flexibility and scalability, it introduces new layers of complexity, making data consistency and synchronization Herculean tasks. For manufacturers, this fractured landscape raises the stakes: poor data management isn’t just a technical hiccup—it can ripple across supply chains, delaying products, and undermining innovation.
The Implications for Technology
Advanced technologies depend on reliable data. Hexagon’s findings reveal that 52% of manufacturers anticipate significant improvements through automated design optimization and generative AI. However, these tools require seamless access to high-quality data. ESG underscores this dependency, highlighting that 54% of organizations cite improved data quality and accuracy as the primary benefit of DataOps strategies.
This dependency underscores a paradox: while manufacturers are eager to adopt cutting-edge technologies, they are often unprepared to meet the foundational requirements these systems demand. Inaccurate or incomplete data becomes a bottleneck, turning potentially transformative tools into expensive yet underperforming assets.
The Human Dimension
Amid the technological narrative, there’s a human story unfolding. Hexagon and ESG both highlight the role of collaboration—or lack thereof—in exacerbating data challenges. Hexagon notes that 42% of manufacturers face difficulties sharing data between teams, while ESG’s findings reveal that poor integration often stems from cultural and organizational misalignment.
These barriers extend beyond workflow inefficiencies. They represent a deeper cultural resistance to change, where teams struggle to adapt to a data-driven paradigm. The promise of innovation remains tethered to the willingness—and ability—of individuals to embrace new tools and approaches.
What It All Means
The widespread data issues plaguing manufacturing are more than operational headaches; they are a reflection of the growing pains of an industry undergoing rapid transformation. The Hexagon report likens this moment to a fork in the road, where manufacturers must navigate between technological aspiration and the weight of systemic inefficiencies. ESG reinforces this view, showing how these challenges mirror those faced across industries, particularly in complex, data-intensive environments.
This isn’t merely a story about technology or infrastructure—it’s about alignment. The disconnect between ambition and execution speaks to the need for a reevaluation of how data is perceived and utilized. For manufacturers, the stakes couldn’t be higher: in an era where speed, agility, and precision define competitiveness, the ability to manage data effectively will increasingly determine who leads and who lags.
In the end, the lesson from both reports is clear. Data is no longer just a supporting asset—it is the backbone of modern manufacturing. The industry's ability to evolve will depend not on the adoption of advanced technologies alone but on its capacity to address the systemic data challenges that continue to hold it back.
Three Pieces of Advice
Inventory Your Data Pain—Not Just Your Data Sources
Action: Stop obsessing over where your data lives and start identifying where it hurts. Data issues don’t show up as empty databases—they show up as slow decisions, rework, missed KPIs, and firefighting. Instead of mapping systems, map frustration. That’s where the real story lies.
How to Start: Bring together leaders from operations, planning, maintenance, quality, and IT. Ask each to describe a moment where the data was wrong, missing, or just didn’t line up with reality. Don’t settle for technical breakdowns—dig into business consequences. This exercise is less about creating a data map and more about surfacing organizational pain points. Once those pain points are known, they become the anchors for your data improvement strategy.
Best Practices: Don’t just log every data source; capture every instance of data distrust. Highlight when employees go offline—like printing data out, rekeying it, or using sticky notes to "fix" reality. These moments reveal breakdowns in data usability and confidence. Use anonymized stories to build buy-in across leadership. Keep revisiting this inventory—your systems may not change weekly, but your pain points often will.
Shift from Collecting Data to Connecting It
Action: Move away from data accumulation and focus on contextual integration. Your goal is not to build a bigger lake—it’s to build a smarter stream.
How to Start: Select one high-impact use case—like downtime reduction or first-pass yield improvement. Then trace every system, spreadsheet, and manual step involved in informing that outcome. Map how data flows from one to the next. Note where translations happen, where latency creeps in, or where the same value is labeled five different ways. This “data journey map” will expose integration gaps and insight bottlenecks.
Best Practices: Invest in a unified data architecture—something like a unified namespace or common data model that gives systems a shared vocabulary. Don’t let different teams call the same thing by different names. Adopt a modern MES or industrial data platform that can contextualize—not just capture—events across your factory. Standardize how data is structured and timestamped to reduce reconciliation later. If your AI team is spending 70% of their time cleaning up data, you're not scaling—you’re spinning your wheels.
Treat Data Like a Product—Not an Exhaust
Action: Elevate data from an invisible byproduct to a managed, governed, and value-generating asset. Think of data like a product: it needs ownership, lifecycle management, and service quality.
How to Start: Identify 2–3 critical data sets that directly influence business performance. For each, assign a “data product owner” responsible for quality, availability, and usability. Define service levels: How fresh does the data need to be? Who relies on it? What breaks if it’s wrong? This is not just about assigning blame—it’s about empowering stewardship. Data without accountability is just noise.
Best Practices: Manage your data like you manage customer-facing products. Establish versioning, quality gates, and feedback loops. Hold teams accountable for improving—not just accessing—the data. Monitor adoption: If people are exporting and adjusting numbers before meetings, something’s broken. Build a culture where clean, contextualized, and trusted data is seen as a shared responsibility, not a backend IT task. And most importantly, make data part of your operating model—not just your tech stack.
References:
Hexagon - 2024 Advanced Manufacturing Report: https://hexagon.com/advanced-manufacturing-report
Enterprise Strategy Group - 2023 State of DataOps, Unleashing the Power of Data: https://boomi.com/content/ebook/esg-state-of-data-ops/
