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Industrial DataOps: Capture value hidden in your data and scale industrial AI

03.29.2024 | Srinivas Kuppa
 

As industries continue to embrace digital transformation, the role of Industrial DataOps becomes increasingly crucial. Creating a strong synergy between human expertise and advanced technology to turn data into a strategic asset is the primary objective. By harnessing the power of DataOps, industrial and manufacturing sectors can optimize their current operations while also laying the groundwork for productizing AI industrial applications. The journey of industrial DataOps is a testament to the endless possibilities that arise when data is treated as a valuable asset, providing manufacturers with a scalable foundation to increase their process efficiency, reduce unscheduled asset downtime, and enhance connected worker capabilities.

Creating a reliable industrial data foundation can be simplified into three key steps. First, data silos must be broken down, making IT, OT, and engineering data easily accessible. Then, data must be organized and combined into a unified namespace that’s easy to understand and navigate. Lastly, we unlock the value of this data by using AI to uncover insights. This involves using ready-made AI tools and personalized assistance to simplify decision-making for users, helping them make better choices based on data.

 

Step 1: Breaking Down Data Silos

A diagram of P&ID Ingestion using computer vision to identify equipment and model processes
P&ID Ingestion using computer vision to identify equipment and model processes

 

Ensuring Accessibility of Industrial Data for Optimized Operations

In the industrial and manufacturing realms, effectively harnessing data is key to leveraging the benefits of digital transformation. The end state involves presenting the right users with properly contextualized and timely data to tackle specific challenges. Yet, the origins of accessibility of this data pose a significant hurdle. Often confined within disparate systems, it becomes a laborious task for data scientists and application builders to find, compile, and prepare it for impactful analysis and maximum usefulness.

For these organizations, the reliability and trust of data is critical. The steep costs associated with operational missteps demand unwavering trust in data utilization. This trust encompasses not only the data’s precision but also its traceability in the event of discrepancies. Industrial data extends beyond structured formats, including process and instrumentation data (P&ID), images, time series, and events that demand a deep domain understanding for accurate interpretation.

Moreover, industrial and manufacturing facilities typically generate time-series data, notorious for its challenges in maintaining quality and consistency. This necessitates ongoing surveillance, review, and quality assessments, thereby introducing an added layer of intricacy in managing data in these sectors.

 

Integrating IT, OT, and engineering Data for Comprehensive Insights

Integrating IT/OT/ET/X data is emerging as a strategic response to these challenges. This approach seeks to dismantle traditional data silos and merge data across different cycles and of different types – Information Technology (IT), Operational Technology (OT), Engineering Technology (ET), and other diverse, unstructured, data forms (X). Such integration fosters more robust decision-making and lays the groundwork for tailored applications and adoptions of predictive and generative AI solutions. 

The objective is not to blur professional boundaries but to cultivate a cohesive strategy that aligns experts, platforms, and data from OT /IT, and engineering domains. This approach is gaining traction through innovative data and digital platforms that merge various data types, including audio / visual and other ‘X’ data, enhancing its accessibility and utility for a wider range of users within and outside the organization.

 

Step 2: Contextualizing and Unifying Industrial Data

A diagram of Industrial Knowledge Graphs provide a visualization and navigation layer for contextualized data
Industrial Knowledge Graphs provide a visualization and navigation layer for contextualized data
Transforming Data into a Powerful Tool for Industrial Applications

The true utility of industrial data is unlocked when it undergoes a transformation into a resource that is integrated, contextualized, and made securely accessible and actionable for all users, both human and machine, within the industrial enterprise. This transformation must embrace the diverse range of data sources and formats prevalent in industrial settings, such as sensor data, process diagrams, audio & visual media, event histories, asset models, and various unstructured documents.

 

Contextualizing Data for Enhanced Understanding

At the heart of an Industrial DataOps strategy is the process of data contextualization. This involves establishing meaningful connections between various data sources and types, enabling users to easily locate and utilize relevant data across operations.

For instance, a production engineer may easily interpret sensor data from an electric submersible pump at an oil well, but a data scientist might not be familiar with the naming convention at a specific facility. Through contextualization, the pump’s data is linked with its asset hierarchy, related work orders, and relational components and processes, making it comprehensible for diverse users of all levels. 

In the steel industry, a data scientist may find it challenging to understand the intricacies of predictive quality and steel-grade monitoring without a deep knowledge of the chemicals and physics involved. Providing additional context through tools like P&ID models and industrial knowledge graphs can facilitate a better understanding, aiding in the development of models and data applications for anomaly detection, among other uses.

 

Addressing Challenges in Industrial Data Utilization

Industries face unique challenges due to their scale and complexity, such as varied analytics workflows, conflicting business interests, and diverse incentives. The growing influence of AI/ML and the scarcity of data scientists further complicate this scenario, imposing new requirements on data modeling, source availability, integrity, and contextual metadata.

Data engineers in industrial digitalization projects often struggle to access key source system data, reminiscent of challenges faced by other sectors a decade ago. Industrial sectors not only share these challenges but also face additional complexities due to the convergence of IT and Operational Technology (OT), dealing with unconventional data velocity, variety, and volume.

 

The Role of Contextualization in Industrial DataOps

Contextualization is essential to address these business challenges and achieve scalable operational outcomes in industry. Contextualization forms the foundation for automating the journey of data to actionable solutions. Without this, data lacks significance and requires manual point-to-point connections that are unable to scale and resource-intensive to maintain.

Modern Industrial DataOps platforms blend data-driven statistical methods with physics-driven process modeling and simulation. While each method has its advantages and limitations, a hybrid ML model combining both often yields the best results. These platforms equip developers with workflows that are compatible with third-party AI tools, enabling them to develop, train, and manage hybrid ML models effectively. This facilitates the operationalization of use-case-specific data subsets at the required scale, transforming data into a vital tool for industrial and manufacturing sectors.

 

Step 3: Scaling AI-powered Insights

A diagram of Plant performance insights with AI copilots provide a simple interface to gain actionable insights and recommendations
Plant performance insights with AI copilots provide a simple interface to gain actionable insights and recommendations

 

Enhancing Value from Industrial Data through DataOps

Maximizing the value of data in the industrial and manufacturing sectors hinges on applying sophisticated models to generate deep insights. Role-based AI copilots provide the simplicity needed to interact with these models and gain actionable insights. This combination drives optimal decision-making, enabling operators to act with assurance. Operationalizing data and Industrial AI applications in production settings is a requirement to unlock data’s true value.

The use of advanced models, which merge data science and physics principles, creates synthetic data and offers profound insights. The scalability of these insights is achieved through machine learning and deep learning. Constant observation, analysis, and optimization are essential for producing dependable forecasts and actionable intelligence.

 

Empowering Users with Industrial DataOps Platforms

Industrial DataOps platforms are revolutionizing data management by offering low-code or no-code solutions for application development and model lifecycle management. This democratization of DataOps allows non-expert data users to independently handle data management tasks and develop sophisticated analytics, all within established governance frameworks. This approach not only preserves process knowledge but also ensures technical continuity for new engineers engaging with and enhancing existing models.

 

Overcoming Challenges in Digital Operations for Real-World Applications

Digital operation initiatives often face hurdles, such as delays or high costs in scaling pilots, living in a state of “POC purgatory”. This challenge stems from divides between IT/OT, OT/data science, OT/engineering, and the struggle to produce and access contextualized, high-quality data at scale.

Industrial DataOps platforms play a pivotal role in connecting data users to diverse operational data sources, thereby bridging critical divides and facilitating the operationalization of use cases. These platforms offer ML libraries tailored to standard industrial scenarios, aiding developers in data collection, model development, and training. Data scientists can utilize these libraries with component-level data, and once a use case is proven effective for one plant component, it can be scaled up to encompass the entire plant or fleet.

 

Conclusion

In the evolving landscape of industrial and manufacturing sectors, the implementation of Industrial DataOps principles marks a significant stride toward operational excellence. The journey from ensuring data accessibility to transforming it into a powerful tool, and ultimately enhancing its value through advanced operationalization, highlights the transformative potential of Industrial DataOps. This journey is not just about technology and data; it’s about reshaping organizational culture to become more data-centric and collaborative. By breaking down data silos, facilitating data usability, and enabling its contextualization and operationalization, industries are paving the way for a more informed, efficient, and innovative future.

 

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