Unlocking Industrial Data with HighByte: Connecting PAS-X MES and AVEVA PI for Scalable Data Architectures

As industrial organisations continue to invest in digital transformation, one of the most common challenges remains the same: how to structure, contextualise and deliver data across multiple systems in a consistent and scalable way.

In life sciences and pharmaceutical manufacturing, this challenge is even more complex. Data must move between systems such as MES, historians and analytics platforms while maintaining integrity, traceability and compliance.

This is where HighByte plays a critical role.

What is HighByte?

HighByte provides an Industrial DataOps platform designed to model, structure and deliver industrial data in a way that makes it usable across modern digital systems.

Rather than simply moving data from one system to another, HighByte focuses on contextualisation. It allows organisations to take raw operational data and organise it into meaningful, structured models aligned with assets, processes and ISA-95 hierarchies.

This structured approach enables data to be consumed more effectively by downstream systems such as analytics platforms, dashboards, cloud environments and AI applications.

Why HighByte Matters for Industrial Data

Many organisations operate with fragmented data landscapes. MES systems, historians, PLCs and enterprise platforms often operate in silos, making it difficult to extract consistent, trusted insights.

HighByte addresses this by acting as a central layer that:

• Connects to multiple industrial data sources
• Structures and models data into a consistent format
• Publishes data to various destinations, including MQTT, APIs and cloud platforms
• Reduces the need for complex point-to-point integrations

The result is a more scalable, flexible and maintainable data architecture.

Réalta Technologies and HighByte

Réalta Technologies is proud to partner with HighByte, bringing together our expertise in automation, data historians, analytics and digital infrastructure with HighByte’s Industrial DataOps capabilities.

For our clients, this partnership means access to a more structured and scalable approach to industrial data, supporting initiatives such as Unified Namespace, advanced analytics and AI readiness.

Connecting HighByte to PAS-X MES

One of the most valuable applications of HighByte in life sciences is its ability to integrate with MES platforms such as Werum PAS-X, not just for data extraction, but for real-time, bidirectional data exchange.

PAS-X is a critical system in pharmaceutical manufacturing, managing electronic batch records, workflows and production execution. While it contains valuable operational data, integrating PAS-X with other systems in a scalable and consistent way can be challenging, particularly where native connectivity is limited.

Réalta Technologies addresses this through the combination of the PAS-X MSI interface and HighByte’s capabilities.

Using the PAS-X MSI interface alongside the REST server functionality within HighByte, we enable a structured and flexible method of information interchange. This approach allows organisations not only to read data from PAS-X, but also to write data back into the MES in real time.

In practice, HighByte acts as a central integration layer between PAS-X and other systems such as AVEVA PI, SAP, Maximo and other enterprise or analytics platforms.

HighByte models and contextualises this data before delivering it to PAS-X in a consistent and structured format. This ensures that MES data is enriched with relevant operational and business context, improving visibility and enabling more informed decision-making at the point of execution.

This bidirectional capability is particularly valuable where systems need to interact with MES in real time, without relying on complex custom integrations. It allows PAS-X to operate as part of a connected digital ecosystem rather than as a standalone system.

Exposing AVEVA PI Data via MQTT

In addition to MES integration, HighByte plays a key role in enabling modern data architectures through its ability to expose data via MQTT.

AVEVA PI systems are widely used as the backbone of industrial data collection, providing high-resolution, time-series data from across manufacturing operations. However, traditional architectures often rely on point-to-point integrations or batch data transfers.

By connecting to AVEVA PI, HighByte can ingest time-series data, contextualise it and publish it in real time via MQTT. This enables the implementation of a Unified Namespace, where data is structured and made available across the organisation in a consistent and scalable way.

This approach allows multiple systems, including analytics platforms, cloud environments and AI tools, to consume the same structured data without additional integration complexity.

Enabling a Unified, Scalable Data Architecture

When combined, HighByte, PAS-X and AVEVA PI form a strong, modern data architecture.

PAS-X provides structured production and batch data. AVEVA PI provides time-series operational data. HighByte contextualises and distributes this data across systems.

Together, they enable:

• Unified Namespace implementation
• Real-time data sharing across systems
• Advanced analytics and reporting
• AI and machine learning applications
• Reduced integration complexity

This architecture replaces fragmented integrations with a more standardised and scalable approach.

The Benefits for Life Sciences and Manufacturing

For organisations operating in regulated environments, this approach delivers several key benefits:

• Improved data accessibility across systems
• Reduced integration complexity
• Stronger data consistency and governance
• Faster time to insight
• A scalable foundation for future digital initiatives

Most importantly, it enables organisations to move from fragmented data to a connected, usable and valuable data environment.

Conclusion: Turning Data into a Strategic Asset

Industrial data has the potential to drive significant improvements in efficiency, quality and operational performance. However, unlocking that value requires more than just collecting data. It requires structure, context and the ability to move data seamlessly between systems.

HighByte provides a powerful way to achieve this, particularly when integrated with PAS-X and AVEVA PI.

At Réalta Technologies, we help organisations design and implement these architectures in a way that is practical, scalable and aligned with regulatory expectations.

If you are looking to integrate your MES and historian systems, enable real-time data exchange or build a scalable foundation for analytics and AI, speak to our team today.

📧 sales@realtatechnologies.com

💻 https://realtatechnologies.com

📞 IRL: +353 21 243 9113 | US: +1 302 509 4401

Artificial Intelligence is no longer a future ambition. It is already embedded in analytics platforms, automation tools and reporting systems across industry. Yet while AI adoption is accelerating, the organisations that are seeing real value are not simply deploying AI tools. They are preparing their data foundations first.

In the life sciences, pharmaceutical and manufacturing sectors, embracing AI is less about algorithms and more about readiness. The question is not whether AI will impact your organisation. The real question is whether your data infrastructure, governance and contextualisation are strong enough to support it.

AI in the Data Industry: From Hype to Operational Reality

AI in the data analytics industry has evolved significantly over the past five years. Early adoption focused on predictive modelling and statistical forecasting. Today, we are seeing machine learning embedded directly into analytics platforms, generative AI assisting with exploratory analysis, and AI-driven tools that support root cause investigations and anomaly detection in near real time.

Modern AI platforms rely on structured, high-quality datasets. They depend on clean data pipelines, reliable time-series data, and consistent metadata. In regulated industries, they also depend on auditability and traceability.

The industry is moving beyond dashboards and reports toward intelligent systems that continuously learn from operational data. AI is increasingly used to:

• Detect deviations before they become failures
• Optimise process performance in manufacturing
• Reduce downtime through predictive maintenance
• Accelerate batch review and quality investigations
• Improve decision-making with contextual recommendations

However, none of this is possible without robust data architecture.

How AI Is Evolving in Industrial Environments

AI is transitioning from experimental pilot projects to embedded operational tools. In industrial environments, this means AI is being applied directly to production systems, laboratory environments and enterprise data platforms.

The next phase of AI adoption is defined by three key shifts:

1. AI is becoming integrated into data platforms rather than existing as standalone tools.
2. AI outputs are expected to be explainable and auditable, particularly in pharmaceutical and life sciences environments.
3. AI is moving closer to real-time decision support rather than retrospective analysis.

As AI becomes more operational, the cost of poor data increases. Fragmented systems, inconsistent tag naming, incomplete historian coverage and unstructured datasets will limit the effectiveness of any AI initiative.

Why Data Foundations Determine AI Success

From Réalta Technologies experience working with global manufacturers and life sciences organisations, the most common misconception is that AI readiness begins with selecting the right platform. In reality, AI readiness begins with data maturity.

For AI to generate reliable outputs, your organisation needs:

Structured and Contextualised Data

Raw data alone has limited value. AI systems require contextualised data that aligns with ISA-95 models, asset hierarchies and process frameworks. Without context, a machine learning model cannot distinguish between noise and meaningful operational variation.

Contextualisation allows data to be linked to equipment, processes, batches and quality events. This structured approach is essential for trustworthy analytics.

Strong Data Historian Architecture

Data historians play a foundational role in AI readiness. High-resolution time-series data from manufacturing systems forms the backbone of predictive analytics and performance optimisation.

A properly configured historian ensures:

• Accurate and complete data capture
• Reliable timestamping
• Data integrity and traceability
• Scalable integration into analytics platforms

Without strong historian infrastructure, AI models will struggle with incomplete or inconsistent datasets.

The Role of a Unified Namespace in AI Readiness

A Unified Namespace (UNS) is becoming a key architectural approach for organisations preparing for AI adoption. Built typically using MQTT and aligned with ISA-95 principles, a UNS creates a single, structured, real-time data layer across the enterprise. It does not store data itself, but organises and standardises how data is published and accessed.

For AI initiatives, this structure is critical. Machine learning models perform best when data is consistent, contextualised and aligned to clear asset hierarchies. A UNS reduces fragmentation, eliminates point-to-point integrations and ensures that operational data is accessible in a governed and scalable format.

Importantly, a Unified Namespace complements rather than replaces a data historian. The historian provides secure time-series storage and traceability, while the UNS ensures data is structured and discoverable across systems. Together, they create a strong foundation for AI-driven analytics in regulated manufacturing environments.

Clean Integration Between OT and IT Systems

AI thrives when operational technology and enterprise systems communicate effectively. Unified data pipelines, stable connectivity and consistent governance between plant systems and enterprise analytics platforms are critical.

Disconnected systems introduce latency, duplication and quality risks that undermine AI outcomes.

High-Quality Visualisation and Reporting Layers

AI does not replace dashboards. It enhances them. When AI identifies anomalies or predicts trends, teams still need intuitive visualisation tools to validate findings and understand operational context.

Effective data visualisation ensures that AI insights are actionable and trusted.

Practical AI Use Cases in Manufacturing and Life Sciences

When organisations prepare properly, AI can deliver measurable benefits across operations.

In pharmaceutical manufacturing, AI can accelerate deviation investigations by identifying patterns in process parameters that would take weeks to uncover manually.

In discrete manufacturing, predictive maintenance models can reduce unplanned downtime by analysing historian data to detect early warning signals in equipment performance.

In quality operations, AI can assist in identifying correlations between environmental data and product variability, enabling earlier intervention.

In all of these cases, the common requirement is trusted, contextualised and structured data.

Common Barriers to AI Adoption

Organisations that struggle with AI adoption typically face one or more of the following challenges:

• Fragmented data across multiple systems
• Inconsistent data governance practices
• Limited historian coverage
• Lack of contextualised asset frameworks
• Poor integration between OT and analytics platforms
• Unclear ownership of data quality

These barriers are not technology problems alone. They are data architecture and governance issues.

A Roadmap to Becoming AI Ready

Preparing for AI does not require a complete system overhaul. It requires a structured, phased approach.

The first step is conducting a data maturity assessment to understand gaps in historian architecture, contextualisation and governance.

The second step is strengthening your data infrastructure, including historian optimisation, asset modelling and integration pipelines.

The third step is implementing scalable analytics frameworks that allow AI tools to access clean, reliable datasets.

Only then should advanced AI initiatives be layered on top.

AI is not a shortcut. It is a multiplier. It amplifies the quality of your data environment, whether good or bad.

The Competitive Advantage of Getting It Right

Organisations that invest in strong data foundations before deploying AI see faster adoption, greater trust from operational teams and more sustainable results. They avoid the common pitfalls of AI pilots that fail to scale.

In regulated industries, this approach also ensures compliance with data integrity expectations, audit readiness and explainability requirements.

The future of AI in industry will belong to organisations that treat data as a strategic asset rather than a byproduct of operations.

Partnering with Experts to Accelerate Your AI Journey

At Réalta Technologies, we work with life sciences, pharmaceutical and manufacturing organisations to prepare their systems for AI adoption in a practical and compliant way.

From data historian optimisation and contextualisation to advanced analytics and AI integration, our approach is grounded in real operational environments. We understand that AI must deliver measurable value, not just technical capability.

If your organisation is considering AI initiatives in 2026 and beyond, now is the time to ensure your data foundations are strong enough to support them.

Speak to our team about how to assess your AI readiness and build a scalable, future-proof data architecture.

Contact Réalta Technologies today:
📧 sales@realtatechnologies.com
💻 https://realtatechnologies.com
📞 IRL: +353 21 243 9113 | US: +1 302 509 4401

As 2025 comes to a close, the data analytics landscape continues to evolve at a pace few industries can match. What was once centred on historical reporting and isolated datasets has matured into a connected, intelligent ecosystem that influences decision-making in real time. For organisations across life sciences, pharmaceuticals, manufacturing, energy and utilities, data is no longer a by-product of operations. It is a strategic asset.

Looking ahead to 2026 and beyond, several clear trends are emerging that will shape how organisations collect, manage, analyse and act on data. These developments are not about adopting the latest technology for its own sake. They are about building resilience, maintaining compliance, improving efficiency and enabling smarter decisions across increasingly complex operations.

From Data Collection to Data Intelligence

One of the most significant shifts underway is the move from basic data collection towards true data intelligence. Many organisations have already invested heavily in historians, automation systems and reporting platforms. The challenge now is not access to data, but the ability to contextualise it, trust it and extract meaningful insight from it.

By 2026, successful organisations will be those that have moved beyond disconnected data sources and created well-structured, governed data foundations. This includes consistent naming standards, clear ownership, strong data integrity practices and alignment with operational models such as ISA-95. Without this groundwork, advanced analytics and AI initiatives struggle to deliver value.

Artificial Intelligence Becomes Operational, Not Experimental

Artificial Intelligence has dominated recent industry conversations, but its role is now shifting from experimentation to practical, operational use. In regulated industries especially, AI adoption has been cautious, and rightly so. However, we are now seeing a clear move towards AI solutions that are explainable, auditable and aligned with regulatory expectations.

In the years ahead, AI will increasingly be embedded into everyday operational workflows. This includes predictive maintenance, anomaly detection, quality monitoring, demand forecasting and decision support. Rather than replacing human expertise, AI will augment it, enabling engineers, operators and analysts to focus on higher-value tasks while routine analysis runs continuously in the background.

Importantly, organisations will place greater emphasis on trustworthy AI. This means models built on high-quality data, transparent logic and robust validation, particularly in life sciences and pharmaceutical manufacturing where patient safety and compliance are paramount.

Real-Time Insight Becomes the Standard

The expectation of real-time or near-real-time insight is becoming the norm rather than the exception. Operational teams increasingly expect to understand what is happening now, not what happened last week. Advances in data infrastructure, streaming technologies and modern visualisation platforms are making this possible at scale.

By 2026, real-time dashboards, alerts and analytics will be embedded across operations, from shop floor monitoring to executive decision-making. This shift supports faster response times, improved operational agility and reduced downtime. It also places greater responsibility on organisations to ensure that real-time data is accurate, contextualised and governed correctly.

Greater Focus on Data Architecture and Interoperability

As technology ecosystems become more complex, the importance of strong data architecture continues to grow. Organisations are increasingly recognising that long-term success depends on systems that can evolve without repeated large-scale rework.

Future-ready data strategies will prioritise interoperability between systems, vendors and platforms. This includes automation systems, data historians, analytics tools and enterprise applications working together seamlessly. Open standards, scalable architectures and flexible integration approaches will be key enablers of this trend.

Analytics Moves Closer to the Business

Another notable trend is the continued democratisation of data analytics. While deep technical expertise remains essential behind the scenes, analytics tools are becoming more accessible to a wider range of users. Engineers, quality teams and operations managers increasingly expect self-service access to insights without needing to rely on specialist teams for every request.

This does not reduce the need for expert data professionals. On the contrary, it increases the importance of well-designed solutions that balance usability with governance, ensuring that insights are reliable, secure and compliant.

Compliance and Data Integrity Remain Non-Negotiable

In regulated industries, compliance and data integrity will continue to underpin every data initiative. As analytics and AI capabilities expand, regulators will expect the same level of control, traceability and validation as traditional systems.

Looking ahead, organisations that successfully integrate compliance into their digital strategies from the outset will be best positioned to innovate with confidence. This includes validation-aware system design, strong change management processes and continuous monitoring of data quality.

Preparing for the Future

The future of data analytics is not defined by a single technology or trend. It is shaped by how organisations bring together people, processes and platforms to create sustainable, value-driven solutions. The most successful organisations will be those that invest in strong foundations, adopt emerging technologies pragmatically and partner with experts who understand both the technical and regulatory landscapes.

As we move into 2026 and beyond, data analytics will continue to play a central role in operational excellence, innovation and competitive advantage. The opportunity is significant, but so is the responsibility to implement these capabilities thoughtfully and effectively.

To learn more about how Réalta Technologies can help you excel in 2026, contact us on;

sales@realtatechnologies.com
https://realtatechnologies.com
IRL: +353 21 243 9113 | US: +1 302 509 4401

As 2025 comes to a close, the data analytics landscape continues to evolve at a pace few industries can match. What was once centred on historical reporting and isolated datasets has matured into a connected, intelligent ecosystem that influences decision-making in real time. For organisations across life sciences, pharmaceuticals, manufacturing, energy and utilities, data is no longer a by-product of operations. It is a strategic asset.

Looking ahead to 2026 and beyond, several clear trends are emerging that will shape how organisations collect, manage, analyse and act on data. These developments are not about adopting the latest technology for its own sake. They are about building resilience, maintaining compliance, improving efficiency and enabling smarter decisions across increasingly complex operations.

From Data Collection to Data Intelligence

One of the most significant shifts underway is the move from basic data collection towards true data intelligence. Many organisations have already invested heavily in historians, automation systems and reporting platforms. The challenge now is not access to data, but the ability to contextualise it, trust it and extract meaningful insight from it.

By 2026, successful organisations will be those that have moved beyond disconnected data sources and created well-structured, governed data foundations. This includes consistent naming standards, clear ownership, strong data integrity practices and alignment with operational models such as ISA-95. Without this groundwork, advanced analytics and AI initiatives struggle to deliver value.

Artificial Intelligence Becomes Operational, Not Experimental

Artificial Intelligence has dominated recent industry conversations, but its role is now shifting from experimentation to practical, operational use. In regulated industries especially, AI adoption has been cautious, and rightly so. However, we are now seeing a clear move towards AI solutions that are explainable, auditable and aligned with regulatory expectations.

In the years ahead, AI will increasingly be embedded into everyday operational workflows. This includes predictive maintenance, anomaly detection, quality monitoring, demand forecasting and decision support. Rather than replacing human expertise, AI will augment it, enabling engineers, operators and analysts to focus on higher-value tasks while routine analysis runs continuously in the background.

Importantly, organisations will place greater emphasis on trustworthy AI. This means models built on high-quality data, transparent logic and robust validation, particularly in life sciences and pharmaceutical manufacturing where patient safety and compliance are paramount.

Real-Time Insight Becomes the Standard

The expectation of real-time or near-real-time insight is becoming the norm rather than the exception. Operational teams increasingly expect to understand what is happening now, not what happened last week. Advances in data infrastructure, streaming technologies and modern visualisation platforms are making this possible at scale.

By 2026, real-time dashboards, alerts and analytics will be embedded across operations, from shop floor monitoring to executive decision-making. This shift supports faster response times, improved operational agility and reduced downtime. It also places greater responsibility on organisations to ensure that real-time data is accurate, contextualised and governed correctly.

Greater Focus on Data Architecture and Interoperability

As technology ecosystems become more complex, the importance of strong data architecture continues to grow. Organisations are increasingly recognising that long-term success depends on systems that can evolve without repeated large-scale rework.

Future-ready data strategies will prioritise interoperability between systems, vendors and platforms. This includes automation systems, data historians, analytics tools and enterprise applications working together seamlessly. Open standards, scalable architectures and flexible integration approaches will be key enablers of this trend.

Analytics Moves Closer to the Business

Another notable trend is the continued democratisation of data analytics. While deep technical expertise remains essential behind the scenes, analytics tools are becoming more accessible to a wider range of users. Engineers, quality teams and operations managers increasingly expect self-service access to insights without needing to rely on specialist teams for every request.

This does not reduce the need for expert data professionals. On the contrary, it increases the importance of well-designed solutions that balance usability with governance, ensuring that insights are reliable, secure and compliant.

Compliance and Data Integrity Remain Non-Negotiable

In regulated industries, compliance and data integrity will continue to underpin every data initiative. As analytics and AI capabilities expand, regulators will expect the same level of control, traceability and validation as traditional systems.

Looking ahead, organisations that successfully integrate compliance into their digital strategies from the outset will be best positioned to innovate with confidence. This includes validation-aware system design, strong change management processes and continuous monitoring of data quality.

Preparing for the Future

The future of data analytics is not defined by a single technology or trend. It is shaped by how organisations bring together people, processes and platforms to create sustainable, value-driven solutions. The most successful organisations will be those that invest in strong foundations, adopt emerging technologies pragmatically and partner with experts who understand both the technical and regulatory landscapes.

As we move into 2026 and beyond, data analytics will continue to play a central role in operational excellence, innovation and competitive advantage. The opportunity is significant, but so is the responsibility to implement these capabilities thoughtfully and effectively.

To learn more about how Réalta Technologies can help you excel in 2026, contact us on;

sales@realtatechnologies.com
https://realtatechnologies.com
IRL: +353 21 243 9113 | US: +1 302 509 4401

TDengine today announced that Réalta Technologies, an Ireland-based global automation and digital systems integrator, has joined the TDengine Reseller Program as a value-added reseller (VAR). The partnership expands TDengine’s worldwide ecosystem and strengthens its ability to deliver high-performance time-series and industrial data solutions to manufacturing and life sciences customers.

Through this collaboration, Réalta will offer TDengine’s industry-leading time-series database and AI-native industrial data management platform to enterprises across pharmaceutical, biopharmaceutical, medical device, FMCG, and general manufacturing sectors. As a value-added reseller, Réalta will provide deep expertise in automation, data infrastructure, analytics, and integration, helping clients modernize operations and accelerate Industry 4.0 adoption.

“TDengine is revolutionizing how industrial data is collected, stored, and analyzed,” said Jim Fan, VP of Product at TDengine. “Réalta Technologies’ broad experience in digital systems integration and process optimization makes them an exceptional partner to extend TDengine’s reach and help customers achieve faster, more data-driven operations.”

Réalta Technologies is a global automation and digital systems integrator headquartered in Cork, Ireland, with offices in Cork, the United States, and India. The company provides automation, digitalization, and data analytics solutions for the life sciences, pharmaceutical, manufacturing, and other industries. With deep expertise across engineering, industrial automation, IT systems, and data infrastructure, Réalta helps clients maximize the value of their data and achieve true Industry 4.0 transformation.

“We’re proud to partner with TDengine to bring their innovative time-series data technology to our clients,” said Dan Moore, CEO of Réalta Technologies. “Our mission is to help organizations harness the full power of their data and drive manufacturing excellence from automation to analytics. With TDengine’s platform for operational data storage and management, we can deliver even greater value through performance, scalability, and real-time intelligence.”

By combining Réalta’s global presence and industry expertise with TDengine’s high-performance, AI-native industrial data platform, customers will benefit from streamlined deployments, simplified operations, and a lower total cost of ownership across on-premises, cloud, and hybrid environments.

• For more information about the TDengine Reseller Program, see the TDengine website.

Introduction: 

In the world of pharmaceutical and life sciences manufacturing, the ability to capture, contextualise, and analyse data in real time is essential. Modern operations depend on reliable, validated data systems that not only meet strict compliance standards but also empower continuous improvement, efficiency, and innovation. Among the leading data historian technologies driving this transformation is Canary Historian, a powerful, scalable solution trusted by manufacturers worldwide.

As specialists in data infrastructure and data analytics, Réalta Technologies works closely with clients to implement historian systems like Canary, helping them achieve visibility, reliability, and data integrity across their operations.

What Is Canary Historian?

Canary Historian, developed by Canary Labs, is a high-performance, enterprise-grade data historian designed to store, manage, and analyse time-series data. Built for speed, reliability, and scalability, it provides life sciences organisations with a secure and compliant platform for collecting data from sensors, control systems, and industrial devices.

Unlike traditional historians that can be complex to maintain or scale, Canary’s architecture is lightweight and efficient, allowing for fast data ingestion and retrieval without compromising integrity or performance. It seamlessly integrates with control systems like Siemens, Rockwell, Emerson, and Ignition front end screens, as well as analytics and reporting platforms such as Power BI and SEEQ.

Key Capabilities of Canary Historian

Canary Historian is built with a clear focus on data integrity, speed, and accessibility, all essential criteria in regulated environments such as pharmaceutical manufacturing.

• Lossless Data Compression:
  Canary’s patented compression algorithms allow massive volumes of process data to be stored efficiently while preserving accuracy. This ensures traceability and compliance with regulatory frameworks like FDA 21 CFR Part 11 and EU Annex 11.
• High-Performance Data Retrieval:
  Canary is optimised for fast query performance, enabling engineers, data scientists, and quality teams to access and visualise data instantly, even across years of historical information.
• Scalability and Flexibility:
  Designed to scale from a single production line to global enterprise deployments, Canary can handle millions of data points per second, supporting digital transformation initiatives across multiple sites.
• Integration with Analytics Platforms:
  Through seamless integration with SEEQ, Power BI, and other modern analytics tools, Canary allows users to move from raw process data to actionable insights. Additionally, Canary can integrate its UNS architecture seamlessly by collecting data in MQTT & SPBv1.0 specifications from MQTT data sources.
  This empowers smarter decision-making and accelerates continuous improvement. 
• Security and Compliance:
  Canary supports role-based access control, data encryption, and full audit trails , critical for compliance in GxP environments.

Use Cases in Life Sciences and Pharmaceutical Manufacturing

For pharmaceutical and biotech manufacturers, data integrity and process optimisation are non-negotiable. Canary Historian plays a crucial role across a range of applications:

• Batch Process Monitoring:
  Ensuring each batch follows the defined recipe and identifying deviations or anomalies early in production.
• Equipment Performance Tracking:
  Continuous monitoring of critical systems such as bioreactors, cleanrooms, and HVAC to ensure operational reliability and product quality.
• Regulatory Compliance and Audit Readiness:
  Providing detailed data trails that demonstrate compliance with GMP standards and regulatory requirements.
• Energy and Utility Management:
  Capturing and analysing utility data, from compressed air to chilled water, to optimise energy consumption and sustainability initiatives.
• Predictive Maintenance and Quality Analytics:
  When combined with advanced analytics platforms, Canary enables manufacturers to predict equipment failures before they occur and improve product consistency through process insights.

Data Collection, Visualisation, and Analytics

At its core, Canary is more than a historian, it is a data foundation for innovation. By centralising time-series data and contextualising it within the manufacturing ecosystem, it enables seamless visualisation and analysis.

Through integrations with platforms like SEEQ, engineers can build advanced analytics models, track key performance indicators (KPIs), and uncover correlations between process parameters and product quality. This real-time visibility leads to more efficient operations, reduced downtime, and data-driven decision-making across departments.

How Réalta Technologies Adds Value with Canary Historian

As a trusted data partner to global life sciences organisations, Réalta Technologies has extensive experience implementing and optimising Canary Historian systems. Our engineers understand both the technical and regulatory dimensions of data management, ensuring that each deployment is compliant, scalable, and future-ready.

Our expertise spans:

• Designing and deploying data historian architectures across multi-site facilities.
• Integrating Canary with control systems and enterprise applications.
• Enabling connectivity to SEEQ, Power BI, and advanced analytics frameworks.
• Supporting validation, testing, and documentation to meet regulatory expectations.

Whether you are upgrading from legacy historians or implementing a new data infrastructure, Réalta Technologies provides a complete solution , from design and deployment to ongoing managed services.

Conclusion

In today’s data-driven manufacturing landscape, the ability to collect, contextualise, and analyse process data efficiently is a competitive advantage. Canary Historian provides life sciences companies with the flexibility, speed, and compliance they need to turn data into real value.

Partnering with Réalta Technologies ensures that this technology is implemented with precision and aligned with your business goals. With proven expertise in data historians, automation, and analytics, we empower organisations to achieve operational excellence through data.

To learn more about how Réalta Technologies can help you implement or optimise Canary Historian, contact our team today.

📧 sales@realtatechnologies.com
💻 https://realtatechnologies.com
📞 IRL: +353 21 243 9113 | US: +1 302 509 4401

Introduction: 

Réalta Technologies recently hosted an exclusive event at Thomond Park, home of Munster Rugby, exploring how data is driving performance and decision-making across both industry and sport. The event, Turning Data into Action: Unlocking Real-Time Insights for Operational Excellence in Industry and Sport, brought together clients, partners, and industry leaders for an engaging day of learning, collaboration, and discussion.

Guests enjoyed a behind-the-scenes tour of Thomond Park and a series of presentations and panel discussions featuring thought leaders from Réalta Technologies, AVEVA, SolutionsPT, and Munster Rugby.

Shaping the Future with Digital Twins – Andy Davidson, AVEVA

Andy Davidson, Product Manager at AVEVA, opened the day with an insightful session on Building and Evolving a Digital Twin. He explained how digital twins bring together connected data and intelligent insight to improve decision-making, efficiency, and performance. Andy highlighted how these technologies are already transforming industries and how AVEVA’s scalable digital solutions empower businesses of all sizes to operate more intelligently.

Digitalisation of the 3D Printing Process – Declan Hickey, Réalta Technologies

Declan Hickey, Principal Engineer at Réalta Technologies, delivered an engaging session on Digitalisation of the 3D Printing Process. Declan explored how automation, data integration, and digital workflows can transform additive manufacturing, improving traceability, quality, and efficiency at every stage. 

He outlined how connecting equipment, materials, and production data within a unified digital framework enables manufacturers to achieve greater consistency, scalability, and regulatory compliance. Drawing on Réalta’s extensive experience in life sciences and advanced manufacturing, Declan demonstrated how a data-driven approach can unlock the full potential of 3D printing in regulated industries.

AI in Life Sciences – Thomas McCarthy, AVEVA

Next, Thomas McCarthy, Industry Principal at AVEVA, delivered a compelling talk on Artificial Intelligence in Life Sciences. Thomas outlined how AI and data integration are revolutionising the pharmaceutical sector, from pre-clinical research and development through to manufacturing and patient outcomes. His talk showcased how AI-driven data ecosystems can accelerate innovation, enhance quality, and optimise operations across the life sciences value chain.

From Equipment to Enterprise: Achieving Data Integration at a Global Scale – Réalta Technologies & Pharma Client

Nikhil Ramisetty and Andreas Scannell from Réalta Technologies were joined by a Value Stream Leader from a leading pharmaceutical client to deliver an insightful joint presentation on their collaborative work. Titled “From Equipment to Enterprise: Achieving Data Integration at a Global Scale:, the session detailed their shared journey in implementing a full-scale, GxP-compliant AVEVA PI System and advanced data analytics solution across the client sites. 

Together, they outlined how a unified data infrastructure can transform visibility, efficiency, and decision-making across complex operations. The speakers discussed the project’s key challenges, the importance of collaboration, and how the integration of data from equipment to enterprise level enables greater standardisation, compliance, and operational excellence in regulated environments.

Generative AI and Large Language Models – Ken Molloy, SolutionsPT

Ken Molloy, Customer Success Manager at SolutionsPT, delivered an engaging session on Generative AI and Large Language Models, examining the rapid advancements reshaping today’s industrial landscape. Ken provided an in-depth look at how generative AI is already driving innovation, efficiency, and smarter automation across industries. He also highlighted how SolutionsPT supports organisations through comprehensive education, training, audits, consulting, and customer success management, empowering teams to adapt, evolve, and succeed with confidence.

Data in Sport – George Murray & Munster Rugby

George Murray, Lead Performance Analyst for Munster Rugby, shared fascinating insights into how data analysis supports high performance within elite sport. He was joined by Damien Falvey of Réalta Technologies to discuss Munster’s data-driven approach and how Réalta Technologies are helping coaches and players optimise performance, manage workloads, and gain competitive advantage, illustrating clear parallels between how data delivers value both on the pitch and in industry.

The event concluded with a panel discussion hosted by Barry Murphy, former Munster Rugby player, who was joined by Munster Rugby coaches and players to gain an insight into how they use data to improve their performance and find that extra 1% in competitive edge. 

A Collaborative Success

Réalta Technologies would like to extend sincere thanks to AVEVA and SolutionsPT for their sponsorship and support in making the day possible, to Munster Rugby for their hospitality and partnership, and to all who attended and contributed to such a successful event.

Finally, a special thanks to the Réalta Technologies team whose effort and expertise made the event a resounding success. A special thanks to Damien Falvey, Nikhil Ramisetty, Andreas Scannell, and Declan Hickey for representing Réalta on stage and sharing their expertise with the audience.

Réalta looks forward to hosting more events that bring together leaders across industry and sport to explore how data can unlock the next era of innovation and performance.

Introduction: 

Réalta Technologies recently completed a critical PI System upgrade for a leading pharmaceutical manufacturer. The project involved replacing ageing hardware and unsupported operating systems, ensuring the client’s data infrastructure met modern performance, security, and compliance standards. 

As a trusted partner and AVEVA Endorsed System Integrator, Réalta Technologies delivered a seamless transition to a fully upgraded system without data loss, enabling the client to continue operations without disruption and minimal downtime. 

The Challenge

The client’s PI System servers were operating on outdated hardware and operating systems. An upgrade-in-place was not possible due to:

• Incompatibility with newer OS versions and software.
• Requirement for new hardware to meet performance demands.
• The need to keep existing servers intact for rollback in case of an issue.
• The critical need to avoid any data loss during the transition.

These constraints required a carefully designed migration strategy that balanced operational continuity, data integrity, and validation requirements.

Possible Solutions

Drawing on extensive experience in industrial data infrastructure projects, Réalta Technologies identified three potential upgrade strategies:

Option 1: Adding New Servers to the PI Collective

• Build new servers on new hardware and OS.
• Add them to the existing PI System Collective.
• Promote one of the new servers to Primary Data Archive.
• Remove old servers once complete.

Pros: Minimal downtime, data available to users throughout.
Cons: Requires reconfiguring and restarting all interfaces, which can be time-consuming with large systems.

Option 2: Swapping Names and IP Addresses

• Build new servers with temporary names.
• Stop the PI Data Archives.
• Swap the names/IP addresses with the old servers.
• Restart PI Data Archives on the new hardware.

Pros: No interface reconfiguration required.
Cons: Brief downtime while archives are offline, though buffered data is restored.

Option 3: Building a Complete Parallel System

• Create a fully independent system (Data Archive, AF, interface servers).
• Restore a backup from the old system.
• Run both systems in parallel until testing and validation are complete.

Pros: Safest approach, full validation before go-live, allows migration from Batch to Event Frames database in a controlled environment.
Cons: Requires more time, resources, and hardware.

Chosen Solution

After carefully reviewing all available upgrade paths, Réalta Technologies determined that Option 2: Swapping Names and IP Addresses was the most effective and efficient solution for this specific project.

This decision was based on our team’s in-depth understanding of both the technical requirements and the operational constraints within regulated pharmaceutical manufacturing. By applying our expertise in designing bespoke solutions, we were able to match the upgrade approach precisely to the client’s unique needs.

Several factors influenced our decision:

• Minimising Disruption: The client’s PI System had multiple critical interfaces feeding data from across the site. Reconfiguring and restarting these, as required in other options, would have posed significant risk and extended downtime. Retaining the same names and IP addresses allowed us to transition to the new infrastructure seamlessly without altering the existing interface configuration.
• Maintaining Operational Continuity: Pharmaceutical manufacturing operates under strict production and compliance demands. Swapping Names and IP Addresses enabled the upgrade to be completed within a tightly controlled maintenance window, avoiding unnecessary interruption to manufacturing activities.
• Safeguarding Data Integrity: Our approach ensured that all data generated during the brief downtime was buffered and automatically restored to the upgraded system, protecting the accuracy and completeness of production records.
• Bespoke Problem-Solving: Rather than applying a one-size-fits-all upgrade method, we evaluated the project holistically, balancing efficiency, safety, and compliance. Option 2 offered the best combination of speed, reliability, and risk mitigation for this particular environment.

This tailored solution is a clear example of how Réalta Technologies leverages its deep technical expertise and industry knowledge to deliver results that are both strategically sound and operationally safe, ensuring clients can modernise their systems without compromising productivity or compliance.

Benefits of Updating the PI System

• Modern Infrastructure: New hardware and OS increased system performance and stability.
• Data Integrity: All historical data retained with no loss during migration.
• Reduced Operational Risk: Fully tested migration plan ensured predictable results.
• Regulatory Compliance: Updated system aligned with industry best practices and supported ongoing GMP compliance.
• Future-Ready Platform: Prepared for integration with advanced analytics, data visualisation tools, and machine learning applications.

Conclusion

This PI System upgrade demonstrated Réalta Technologies’ expertise in designing and executing complex data infrastructure projects for regulated industries. By selecting the optimal migration strategy and executing it flawlessly, Réalta ensured the client could continue delivering high-quality pharmaceutical products with minimal disruption.

As an AVEVA Endorsed System Integrator, Réalta Technologies provides tailored solutions that modernise industrial data systems while safeguarding compliance, security, and operational excellence.

Need help with updating your systems? Get in touch with our team.

Phone: +353 21 243 9113

Email: sales@Realtatechnologies.com 

Introduction: 

In the age of Industry 4.0, the volume of data generated in manufacturing environments continues to grow exponentially. But data alone doesn’t drive smarter decisions. It’s how you visualise and act on that data that creates real value. For companies in life sciences, pharmaceuticals, and high-volume manufacturing, choosing the right data visualisation tool is critical.

In this blog, we compare three leading tools in the space: Microsoft Power BI, Tableau, and SEEQ, examining their features, benefits, and use cases from the perspective of industrial data analytics.

Why Data Visualisation Matters in Manufacturing?

Before diving into the tools, it’s worth revisiting why data visualisation plays such a key role in manufacturing.

Manufacturers face constant pressure to increase yield, reduce downtime, improve compliance, and optimise performance. Data visualisation tools allow plant teams, analysts, and decision-makers to transform raw operational data into actionable insights. Whether tracking equipment efficiency or identifying production bottlenecks, the right dashboard can be the difference between reactive and proactive decision-making.

Power BI: Scalable, Accessible, and Microsoft-Native

Microsoft Power BI is one of the most widely used business intelligence platforms in the world. It offers deep integration with Microsoft products, scalability, and user-friendly interfaces, making it a powerful choice for companies already embedded in the Microsoft ecosystem.

Key Features:

• Native integration with Excel, Azure, and SharePoint
• Drag-and-drop dashboard creation
• Custom DAX formulas for advanced metrics
• Scheduled data refresh and real-time dashboards
• Strong data modelling capabilities

Strengths:

• Easy to adopt for teams already using Microsoft 365
• Strong community support and regular updates
• Affordable pricing tiers at enterprise level compared to other  visualization tools 
• Suitable for both SME and enterprise scale

Manufacturing Use Cases:

• OEE Dashboards: Track overall equipment effectiveness across multiple plants
• Quality Monitoring: Monitor defect rates and identify trends
• Supply Chain Analysis: Visualise logistics and inventory data

Limitations:

• Can be less flexible for time-series industrial data
• Requires additional configuration for integration with industrial historians like AVEVA PI or OSIsoft

Tableau: Powerful Visualisation and Data Exploration

Tableau is known for its visually rich dashboards and ability to handle large datasets from varied sources. It empowers users to explore data intuitively and supports custom, interactive reporting.

Key Features:

• Rich data visualisation capabilities
• Native support for many data connectors
• Real-time data exploration and drill-downs
• Customisable dashboards with dynamic filters

Strengths:

• Intuitive UI for data analysts and non-technical users
• Excellent at data storytelling and presenting complex trends
• Highly flexible for different data sources and schemas

Manufacturing Use Cases:

• Batch Performance Analysis: Track trends in batch processes over time
• Energy Consumption Reporting: Visualise and compare energy usage across facilities
• KPI Reporting Dashboards: Executive-level visual reporting across departments

Limitations:

• Higher licensing costs than some alternatives
• Not purpose-built for time-series industrial data
• More suitable for data analysts than plant-floor users

SEEQ: Purpose-Built for Time-Series Industrial Data

SEEQ is designed specifically for advanced analytics in process manufacturing industries. Built to work with time-series data from historians like AVEVA PI or Canary, SEEQ enables engineers and analysts to gain insights from complex datasets quickly.

Key Features:

• Native connectivity with AVEVA PI System, OSIsoft, and Canary
• Purpose-built for time-series and event-based data
• Predictive analytics and statistical modelling
• Collaboration features for teams across functions
• Strong integration with Jupyter for advanced data science

Strengths:

• Ideal for engineers and process analysts
• Handles large volumes of industrial data efficiently
• Designed around manufacturing and life sciences workflows
• Short time to value with minimal IT setup

Manufacturing Use Cases:

• Process Optimisation: Identify trends and anomalies in production runs
• Deviation Analysis: Investigate root causes of failures and off-spec product
• Batch Comparisons: Compare equipment and material performance across runs

Limitations:

• Not designed for traditional business metrics (e.g. finance or HR data)
• Requires familiarity with process data structures and tag naming conventions

Choosing the Right Tool for Your Manufacturing Business

The best data visualisation tool depends on your organisation’s needs, data environment, and user base. Here’s a quick comparison:

At Réalta Technologies, we work with clients to implement the right data visualisation solution based on their unique needs. This might be AVEVA PI paired with SEEQ for deep process insights, Tableau connected to AVEVA PI for advanced visual storytelling, or Power BI dashboards for plant-wide KPIs and reporting.

How Réalta Technologies Adds Value

As experts in industrial data architecture, data science, and automation, Réalta Technologies supports clients through every stage of their data journey. This includes infrastructure and historian setup, advanced analytics, and dashboard delivery.

We’ve successfully delivered SEEQ and AVEVA PI solutions across global manufacturing and life sciences clients. Our partnerships with leading technology providers and our in-house data engineering team ensure solutions that are tailored, validated, and built for real-world impact.

Conclusion

Data visualisation is not just about attractive dashboards. It’s about empowering teams with insights. Whether you need plant-level performance metrics, quality trends, or predictive insights, selecting the right visualisation tool is essential.

Power BI, Tableau, and SEEQ each offer distinct advantages. Understanding how they align with your infrastructure, team skillsets, and business goals helps ensure long-term value.

Need help selecting or implementing your data visualisation tools? Get in touch with our team.

Phone: +353 21 243 9113

Email: sales@Realtatechnologies.com 

Introduction

Databricks is one of the most powerful and versatile platforms available for handling large-scale data analytics, machine learning, and AI workflows. Built on top of Apache Spark, it enables organisations to unify their data and AI strategies with scalable solutions tailored for speed, collaboration, and security.

As industries like life sciences, pharmaceutical manufacturing, and advanced engineering become increasingly data-rich, the need for a platform like Databricks becomes essential. At Réalta Technologies, we use Databricks to help clients unlock real-time insights, streamline operations, and make smarter, faster decisions.