How to build a unified data layer for modern manufacturing

Modern manufacturing facilities generate vast amounts of data across every stage of production, yet much of this information remains trapped in isolated systems and proprietary formats. The challenge extends beyond simple data collection; manufacturers must transform fragmented operational data into a cohesive, accessible foundation that drives real-time decisions and advanced analytics. Organizations spend approximately $10 per record to clean up corrupted data, while failure to address data quality issues costs $100 per record in downstream consequences.

This transformation requires more than technological upgrades—it demands a strategic approach to data integration that addresses legacy equipment constraints, protocol incompatibilities, and organizational silos. Up to 87 percent of artificial intelligence projects never reach production, with poor data quality emerging as the primary culprit. The path forward involves creating a standardized data foundation that serves as the backbone for smart factory operations and AI-driven optimization.

This transformation requires more than technological upgrades—it demands a strategic approach to data integration that addresses legacy equipment constraints, protocol incompatibilities, and organizational silos. The path forward involves creating a standardized data foundation that serves as the backbone for smart factory operations and AI-driven optimization.

What is a unified data layer?

A unified data layer represents a fundamental shift in how manufacturing organizations manage and leverage their operational data. Rather than maintaining separate, disconnected systems for production equipment, quality control, inventory management, and business planning, this architecture creates a single, coherent framework where all data flows seamlessly from sensors on the factory floor to executive dashboards in the cloud.

Organizations implementing unified data architectures report transformative results: Georgia-Pacific reduced knowledge transfer time by automating expert documentation, while Rehrig Pacific compressed equipment repair cycles from four days to two hours through enhanced visibility. More broadly, enterprise organizations achieve an average return on investment of 299 percent over three years from data integration investments, with manufacturing specifically reporting 354 percent ROI. These improvements stem from three fundamental capabilities that unified data layers provide: comprehensive data access through standardized naming conventions, architectural flexibility that accommodates system evolution without disruption, and dramatically simplified integration complexity compared to traditional point-to-point connections.

Organizations implementing unified data architectures report transformative results: Georgia-Pacific reduced knowledge transfer time by automating expert documentation, while Rehrig Pacific compressed equipment repair cycles from four days to two hours by gaining comprehensive visibility into production equipment status through its unified data layer. These improvements stem from three fundamental capabilities that unified data layers provide: comprehensive data access through standardized naming conventions, architectural flexibility that accommodates system evolution without disruption, and dramatically simplified integration complexity compared to traditional point-to-point connections.

• Production systems: PLCs, SCADA systems, and industrial IoT devices that generate real-time operational data
• Execution layer: Manufacturing Execution Systems (MES) that orchestrate production workflows and track performance
• Enterprise applications: ERP systems, supply chain platforms, and business intelligence tools that drive strategic decisions
• Advanced analytics: AI and machine learning systems that optimize processes and predict maintenance needs

Creating a unified data layer involves a strategic approach to integrating diverse systems and technologies. This process begins with a comprehensive assessment of the current infrastructure, identifying key components such as ERP systems, MES platforms, and IoT devices. The digital twin market is projected to expand from $24.48 billion in 2025 to approximately $259.32 billion by 2032, representing a compound annual growth rate of 40.1 percent. Evaluating these systems reveals existing data fragmentation and integration challenges, providing insight into the barriers that hinder seamless operations.

Organizations implementing unified data architectures report transformative results: Georgia-Pacific reduced knowledge transfer time by automating expert documentation, while Rehrig Pacific compressed equipment repair cycles from four days to two hours through enhanced visibility. These improvements stem from three fundamental capabilities that unified data layers provide: comprehensive data access through standardized naming conventions, architectural flexibility that accommodates system evolution without disruption, and dramatically simplified integration complexity compared to traditional point-to-point connections.

The unified data layer also addresses critical challenges in modern manufacturing environments. Legacy equipment running decades-old protocols can share the same data infrastructure as cutting-edge IoT sensors. Quality data from vision inspection systems flows alongside production metrics from PLCs. Maintenance logs integrate with real-time equipment telemetry. This convergence creates the foundation for advanced use cases: predictive maintenance algorithms that prevent costly downtime, quality control systems that adapt in real-time to process variations, and supply chain optimization that responds dynamically to production capabilities. A single hour of unplanned downtime in an automotive manufacturing facility costs $2.3 million, representing more than $600 per second of lost value.

How to build a unified data layer for modern manufacturing

Creating a unified data layer involves a strategic approach to integrating diverse systems and technologies. This process begins with a comprehensive assessment of the current infrastructure, identifying key components such as ERP systems, MES platforms, and IoT devices. Evaluating these systems reveals existing data fragmentation and integration challenges, providing insight into the barriers that hinder seamless operations.

Step 1: assess current infrastructure

Understanding the scope of your existing systems is essential. Identify how data flows between various components, like ERP and MES platforms. Evaluate any integration challenges that may create barriers. Recognizing these challenges enables the development of a roadmap for streamlined operations and highlights areas for improvement.

• Identify fragmentation: Determine where data is isolated and how these gaps impact operations.
• Analyze integration challenges: Identify communication gaps between systems to understand their effect on efficiency.

Step 2: implement a unified namespace

A cohesive data framework standardizes data models and facilitates smooth exchange. This approach integrates both older and newer systems, ensuring compatibility and enhancing data flow across the organization.

• Create consistent frameworks: Develop uniform data representation across all systems.
• Facilitate seamless exchange: Use protocols like OPC UA and MQTT to support interoperability.

Step 3: leverage cloud integration

Utilizing cloud services enhances data accessibility and scalability, enabling efficient processing and analytics. By integrating cloud solutions, manufacturers can expand their data capabilities beyond physical constraints.

• Improve data accessibility: Implement cloud-native solutions for streamlined processing and analysis.
• Enable comprehensive monitoring: Use cloud-based systems to manage operations from various locations.

Step 4: establish an IoT framework

Deploying an IoT framework is crucial for continuous data collection and monitoring. This framework supports secure connectivity and robust data management, allowing for ongoing insights into manufacturing processes.

• Deploy sensors and devices: Equip the factory floor with tools for capturing data continuously.
• Ensure robust connectivity: Implement protocols to maintain data integrity as it moves across networks.

By systematically addressing each of these components, manufacturers can develop a unified data layer that enhances their operations. This architecture not only supports real-time insights but also prepares the groundwork for future advancements in smart manufacturing.

Step 1: assess current infrastructure

Initiating a comprehensive review of your manufacturing systems is essential for creating a seamless data layer. Begin by examining the various platforms in use, such as ERP, MES, and connected IoT devices, to gain insights into existing operational dynamics. This process helps reveal how these systems interact and where potential inefficiencies may lie.

Identifying areas where data flow is restricted by silos is crucial. These silos often arise from outdated technologies that lack compatibility with modern solutions. By pinpointing these obstacles, you can develop strategies to enable more fluid data exchange and improve overall system integration.

• Analyze system interactions: Evaluate the roles of different systems and their data exchange mechanisms.
• Detect integration barriers: Focus on areas where data gets bottlenecked, reducing visibility and operational agility.

Employing advanced data analysis techniques will further illuminate inefficiencies within your current setup. This insight allows for targeted improvements, ensuring that data flows more freely across the entire manufacturing process. By gaining a thorough understanding of your infrastructure, you set the stage for a more integrated and responsive manufacturing environment.

Step 2: implement a unified namespace

Creating a unified namespace is vital for achieving a streamlined data environment in modern manufacturing. This system aligns data structures across platforms, promoting efficient data flow throughout the enterprise. By establishing a unified namespace, manufacturers can dismantle data silos and improve connectivity between diverse technologies.

A unified namespace serves as an organizational hub, where data points are uniformly defined and categorized. By employing protocols designed for industrial communication, such as OPC UA and MQTT, this framework ensures seamless integration across various systems. These protocols enable efficient data interchange, allowing legacy systems and new technologies to communicate effectively.

• Harmonize data structures: Develop a universal framework that adheres to industry norms.
• Facilitate interoperability: Utilize industrial protocols to maintain smooth system interactions.

Integrating older systems with current technologies is crucial for the unified namespace to function optimally. This involves retrofitting existing equipment to operate within the new data architecture, ensuring consistent access and usage of information. By merging different technological generations, the unified namespace supports enhanced analytics and informed decision-making.

• Bridge technological gaps: Adapt existing infrastructure to fit within the new data model.
• Support cutting-edge solutions: Align contemporary technologies with unified standards.

Step 3: leverage cloud integration

Harnessing cloud technology transforms data management by providing seamless access and robust scalability. By integrating cloud-native applications, manufacturers can achieve real-time insights, enhancing decision-making processes. This technology ensures data flows efficiently across platforms, empowering organizations to respond swiftly to operational demands.

Cloud solutions offer dynamic resource allocation, adapting to the ever-changing needs of manufacturing environments. This adaptability supports the handling of fluctuating data volumes, ensuring that capacity aligns with production requirements. Without the limitations of traditional infrastructure, cloud services provide the flexibility to scale operations effectively.

• Boost resource efficiency: Cloud platforms dynamically adjust resources to match data demands.
• Optimize data handling: Employ cloud systems for agile data management and analysis.

Remote capabilities become a reality with cloud integration, allowing for comprehensive oversight of operations from any location. This connectivity ensures continuous management and optimization of processes, even across distributed facilities. By enabling remote access to critical data, manufacturers can maintain high performance and address issues proactively.

• Enable comprehensive oversight: Monitor operations seamlessly across various sites.
• Sustain operational excellence: Ensure uninterrupted performance with real-time connectivity.

Integrating cloud solutions fosters collaboration and innovation within the manufacturing sector. Teams can leverage shared data resources to drive improvements and foster a culture of agility, essential for thriving in the competitive industrial landscape.

Step 4: establish an IoT framework

Integrating an IoT framework is crucial for capturing real-time insights across the manufacturing floor. By deploying a network of smart sensors and connected devices, manufacturers gain visibility into every aspect of their operations, from machine efficiency to environmental factors. These devices provide continuous data streams that inform more nuanced decision-making and enable proactive maintenance.

Ensuring robust connectivity and secure data transmission is essential for the success of an IoT ecosystem. Implementing advanced security measures, such as end-to-end encryption and authentication protocols, protects data integrity and privacy. This approach guarantees that information flows smoothly and securely between devices and central systems, minimizing risks of data breaches.

• Advanced security protocols: Implement measures like encryption and authentication for data protection.
• Seamless data flow: Establish reliable communication channels to ensure consistent information transfer.

A centralized IoT platform acts as the nerve center, coordinating data flows and device interactions across the enterprise. This platform facilitates seamless integration with existing IT infrastructure, enabling manufacturers to optimize operations and drive innovation. By harnessing the power of IoT, manufacturers can transform data into actionable insights that enhance process efficiency and product quality.

• Integrated management: Utilize a platform to synchronize data and device operations.
• Actionable insights: Leverage data to improve decision-making and enhance operational efficiency.

Through a well-structured IoT framework, manufacturers can unlock new levels of operational excellence and remain competitive in an ever-evolving industrial landscape.

Step 5: enhance data analytics capabilities

Advancing data analytics capabilities transforms manufacturing operations by converting raw data into actionable insights. Deploying predictive maintenance tools allows organizations to foresee equipment issues and maintain peak performance. This foresight reduces unexpected downtime and ensures smooth, efficient operations.

Utilizing sophisticated analytics refines production processes by uncovering hidden patterns and trends. By leveraging AI-driven insights, manufacturers can optimize operations, minimize waste, and enhance throughput. These analytics provide a comprehensive view of production dynamics, enabling strategic decision-making.

• Equipment health monitoring: Implement systems that proactively identify potential failures, extending equipment longevity.
• Real-time quality assurance: Use monitoring systems to ensure products consistently meet quality standards.

Embracing advanced analytics facilitates continuous improvement and innovation. These capabilities nurture a data-driven culture, allowing organizations to swiftly adapt to market changes. Through the strategic application of analytics, companies unlock new avenues for growth and competitiveness in the manufacturing sector.

Step 6: ensure security and compliance

Embedding security and compliance into manufacturing operations is paramount. Implementing advanced security measures ensures the integrity of data, protecting it from unauthorized access. Utilizing techniques like multi-factor authentication and data encryption fortifies defenses against cyber threats.

Compliance with industry standards serves as a foundation for trust and operational excellence. Aligning with frameworks such as ISO 27001 ensures that practices meet rigorous global security benchmarks. This alignment builds confidence among stakeholders and reinforces an organization's reputation.

• Global standards alignment: Ensure practices adhere to international security norms.
• Enhanced credibility: Strengthen stakeholder confidence through rigorous compliance.

Continual adaptation of security protocols is essential in a dynamic threat landscape. As threats evolve, regular updates and monitoring of security measures are necessary to maintain resilience. This proactive stance enables manufacturers to mitigate vulnerabilities and maintain robust protection.

• Proactive threat management: Implement real-time systems to identify and neutralize threats.
• Dynamic security strategies: Continuously refine approaches to counter emerging risks.

By integrating comprehensive security and compliance strategies, manufacturers can confidently advance their technological capabilities while safeguarding critical operations. This strategic focus ensures both innovation and protection in the manufacturing environment.

Final thoughts

Establishing a cohesive data architecture revolutionizes manufacturing by ensuring smooth integration and robust analytics. This framework lays the foundation for intelligent factory operations, facilitating fluid information exchange across systems and enhancing both productivity and insight-driven processes. By embracing innovative technologies, manufacturers can fully exploit AI's capabilities, fostering creativity and refining performance.

Strategic alignment and thoughtful deployment are crucial. The process involves bridging traditional systems with cutting-edge innovations, ensuring seamless interaction and system harmony. Vigilant management of security protocols is essential to safeguard data and adhere to industry regulations, maintaining confidence and reliability.

With a well-planned strategy, manufacturers can achieve remarkable progress. Strengthened data integration enables immediate decision-making, while advanced analytics uncover fresh avenues for development and efficiency. This evolution equips manufacturers to lead in industry advancements, prepared to navigate future complexities with dexterity and accuracy.

The journey to building a unified data layer in manufacturing requires the right combination of strategy, technology, and expertise to unlock the full potential of your operational data. As manufacturers continue to embrace digital transformation, having an AI-powered platform that seamlessly connects and analyzes information across your entire organization becomes essential for staying competitive. Ready to see how we can help you transform your manufacturing operations? Request a demo to explore how Glean and AI can transform your workplace.