Unstructured data accounts for 80% of all global data generated, totaling as much as 175–180 zettabytes (or the same amount of data that is held in 1.4 trillion phones). For manufacturers, the challenge is even greater. Nearly 90% of all data manufacturers create is unstructured, ranging from machine logs and sensor readings to customer feedback and supply chain records. Yet, despite this abundance of information, manufacturers are only ever analyzing or using about 1%.
This gap between data generated and data leveraged is more than a missed opportunity, it’s a growing risk. Manufacturers that fail to modernize their data strategies face tangible consequences: millions lost to inefficiencies, delayed insights that slow innovation, bottlenecks in production, and missed chances to respond to market or supply chain shifts with agility. In an industry where margins are tight and competition is fierce, the inability to harness data effectively doesn’t just hinder growth, it threatens long-term viability.
Why manufacturers must modernize fata
Digital transformation depends on data. Every initiative, from predictive maintenance leveraging AI to supply chain optimization, runs on the ability to collect, process, and analyze information quickly and at scale.
However, the majority of manufacturers still rely on outdated data systems. A 2025 Intoware study found as much as 74% of manufacturing and engineering companies rely on legacy systems. This presents a major challenge as traditional architectures handle structured records well, but modern operations generate enormous volumes of unstructured data. Sensors, equipment logs, video feeds, supplier emails, and customer interactions all fall into this category. Without modernization, these streams remain siloed and unused, resulting in lost insights, slower decision-making, and missed opportunities for efficiency and innovation.
Modernized data platforms can make all the difference. Cloud-native systems, data lakes, and real-time analytics tools give manufacturers the capacity to store, organize, and act on data across operations. With these tools, leaders gain sharper visibility into production, accelerate decisions, and adapt more quickly when disruptions arise.
Additionally, manufacturers must adhere to strict regulations and protect intellectual property (such as NIST Cybersecurity Framework for protecting operational and supply chain data and ISO/IEC 27001, which is the global standard for managing information security), and fragmented storage environments can make compliance and data security difficult. Modernized data platforms now offer centralized governance, encryption, and access controls, ensuring compliance while safeguarding sensitive design files and confidential supplier agreements.
Scalability as a core requirement
With modern systems in place, manufacturers are better prepared to scale seamlessly across regions. As sensors are added, assets connected, and supply chains integrated, these systems provide the flexibility and resilience needed to handle growing data volumes while supporting expansion into new markets.
A platform that supports a single pilot project can crumble once scaled across a global enterprise. Scalable design ensures that the system grows without creating bottlenecks or runaway costs.
Consider predictive maintenance. A single facility may produce thousands of readings per second. Multiply that across dozens of plants worldwide, and the data load becomes staggering. Scalable infrastructure allows organizations to extend this use case without sacrificing accuracy, speed, or security.
Risks of poor data management
Manufacturers who fail to modernize and scale data management expose themselves to major risks:
- Operational inefficiency: Fragmented and delayed data slows production cycles, reduces asset utilization, and increases downtime. Leaders fall back on reactive rather than proactive decisions.
- Financial loss: Poor forecasting and misaligned supply chains drive overproduction, shortages, and wasted resources. These inefficiencies erode margins and competitiveness.
- Security gaps: Legacy systems often lack adequate protection, making breaches and compliance failures more likely. A weak governance framework puts intellectual property and customer trust at risk.
- Stalled innovation: Advanced analytics, automation, and AI depend on clean and connected data. Companies without modern systems cannot harness these tools, leaving them behind more digitally mature competitors.
- Fragile supply chains: Without real-time, scalable insights, manufacturers cannot anticipate disruptions or optimize logistics. The result is greater vulnerability in an unpredictable global market.
Steps to implementing a successful data strategy
Manufacturers can overcome these risks by adopting a strategy grounded in four pillars:
- Integration across systems. Success does not require discarding existing infrastructure. Instead, leaders must link legacy systems with modern platforms to create a unified data environment.
- Cloud-first architecture. Cloud solutions provide the flexibility and capacity needed to handle large, fluctuating workloads. Hybrid and multi-cloud models give manufacturers redundancy and control where they need it most.
- Real-time analytics. Moving from batch to continuous processing lets manufacturers respond instantly to quality issues, machine failures, or logistics delays. Proactive action replaces costly reaction.
- Governance and security. Clear governance frameworks ensure accuracy, transparency, and accessibility. Advanced security measures protect sensitive operational and customer data from evolving cyber threats.
Competitive advantages of modern data management
Manufacturers today sit on a mountain of unstructured data that, if left untapped, is a liability. But with the right data strategy— inclusive of modern platforms, scalable infrastructure, real-time analytics, and strong governance— this same data can unlock new avenues for innovation, resilience, and growth. Those who embrace modernization and scalability cut downtime by applying AI to predict maintenance needs, improve quality control by analyzing production data in real time, reduce environmental impact by optimizing resource use, and strengthen resilience by modeling supply chain risks before they become crises.
Equally important, these companies can accelerate innovation. Teams across functions can access trusted datasets, collaborate more effectively, and bring new products to market faster. These organizations move beyond efficiency gains to create new opportunities for growth and differentiation.
