Enhancing AI Reliability in OT Environments Through DataOps
TL;DR;
In today’s industrial landscape, the volume of data generated is immense and growing exponentially.
While industries have systems like Plant Information Management Systems (PIMS) that handle data ingestion, provisioning, and cataloging, there’s still a significant gap when it comes to preparing this data for Artificial Intelligence (AI) and Machine Learning (ML) applications.
This is where DataOps comes into play, amplifying existing capabilities and evolving beyond traditional systems to ensure data reliability and security in Operational Technology (OT) environments.
What Is DataOps?
DataOps is a methodology that combines agile development practices, continuous integration, and automation to enhance data analytics quality and reliability .
Originating from the best practices of DevOps, DataOps can offer autonomy to data scientists or AI engineers, allowing them to integrate and deploy models more easily with less dependency on data engineers (also enabling them to handle more complex tasks).
Just as DevOps revolutionized collaboration between development and operations teams to deliver software faster and more reliably, DataOps applies the same principles to the data realm. This empowers teams to be more autonomous and efficient in data management and utilization. The picture below shows the main steps of a DataOps approach.
Description of each step:
1. Data Ingestion (Extract & Load): This step gathers data from multiple sources, loading it into a central repository like a data lake or data warehouse, setting it up for further processing.
2. Data Preparation (Cleaning & Transformation): Prepares data for analysis by cleaning inconsistencies and transforming it into usable formats, ensuring high-quality data.
3. Data Validation & Quality Assurance (Lineage): Track and verifies data for accuracy and consistency, applying automated tests to meet quality standards before use in analytics or machine learning models. Tracking data lineage for transparency and trust, allowing users to understand data origins and transformations
4. Data Provisioning: Makes validated data accessible to users and systems via APIs, dashboards, or direct database access, ready for decision-making processes.
5. Data Cataloging: Organizes and documents datasets, providing metadata that aids in data discovery, accessibility, and collaboration among teams.
On top of each step, we can add the Monitoring & Observability step, aimed at continuously monitoring data pipelines for quality, integrity, and real-time anomaly or issue detection to ensure reliability.
Now that we understand what DataOps is, let’s explore its relevance to the OT space.
The Relevance of DataOps in OT
In industries like manufacturing, mining, and others, the primary focus is producing physical goods such as ore and steel. Unlike tech companies with robust data teams, these industries typically have small data teams and limited resources. Here, DataOps becomes essential to maximize the efficiency and productivity of these lean teams.
By implementing DataOps practices, professionals such as process engineers, automation engineers, and data scientists gain tools to develop analytical solutions more autonomously. This is crucial in industry, where speed and precision in decision-making can significantly impact productivity and operational safety.
Not only is DataOps important to overcome the lack of professionals on industrial data teams, but it also bridges the gap between what big tech companies already do and the needs of industrial engineers who want to apply AI in their factories. DataOps can make the lives of these engineers easier by providing the tools and processes that facilitate AI implementation in industrial settings.
In that sens, it is crucial to highlight that there is a significant difference between industrial data and digital data. In sectors like e-commerce, data is predominantly digital and controlled, originating from clicks, page visits, and user interactions. This data is collected in stable and predictable environments.
However, in factories, data is generated by physical sensors and equipment located in hostile environments, subject to dust, vibration, and dirt. This nature of industrial data brings several challenges:
• Variability and Interferences: Sensors may present inconsistent readings due to adverse environmental conditions and lack of maintenance.
• Communication Failures: Equipment in continuous operation may suffer interruptions in data transmission.
• Need for Specific Data Processing: It’s essential to filter out noise and validate data integrity before using it in analytical models.
We discussed some of these challenges in our first blog post.
Now that we’ve discussed the relevance of DataOps, let’s explore how it can support AI initiatives in OT.
Leveraging Existing Infrastructure: The Role of PIMS as basic DataOps functionalities
Industries have long utilized Plant Information Management Systems (PIMS) to manage critical data operations. These systems have, in many ways, introduced and implemented certain aspects of DataOps, effectively democratizing data access over the past two decades.
To date, PIMS systems provide only a subset of DataOps capabilities, including:
• Data Ingestion and Collection: PIMS effectively manage data collection from various systems and communication protocols commonly found in factory automation.
• Data Provisioning: Access is provisioned for personnel to utilize this data, supporting operational decisions.
• Data Cataloging and Organization: Data is cataloged and organized within the system, enabling easier retrieval and analysis.
While these systems are robust for operational needs and incorporate some DataOps functionalities, they still lack modern DataOps features essential for AI and ML applications.
To enable the safe proliferation of AI across the industry, additional layers of DataOps functionalities must be built upon existing PIMS systems.
The Missing Pieces: Automated Data Preparation and Quality Assurance
Despite the strengths of PIMS, there are critical areas where they fall short, especially concerning data provisioning for AI and ML applications:
1. Automated Data Preparation:
• Current State: Some level of data preparation exists but is often manual and time-consuming.
• What’s Needed: An automated layer that simplifies data preparation, making it readily usable for AI models without extensive manual intervention.
2. Validation and Quality Assurance:
• Data Lineage and End-to-End Tracking: There’s a lack of mechanisms to trace data from its origin through to its use in AI models.
• Purpose-Built for AI/ML: Existing validation processes aren’t designed to meet the rigorous demands of AI and ML applications.
3. Monitoring and Observability:
• Pipeline Quality Monitoring: Industries monitor the data values but not the quality/integrity of the data pipelines.
• Data Integrity Checks: There’s no systematic way to detect anomalies or integrity issues in the data feeding AI models.
By augmenting PIMS with these additional DataOps layers, industries can bridge the gap between existing capabilities and the requirements of modern AI and ML applications. This evolution is crucial for safely and effectively integrating AI into industrial operations.
The Crucial Role of DataOps in Data Reliability and Security
DataOps is vital for professionals developing analytical models, enabling them to focus on value creation without worrying about the complexities of data engineering. By automating and standardizing data collection, processing, and integration processes, DataOps ensures that only reliable and suitable data is used.
This is especially important in industry, where incorrect inferences can have severe consequences:
• Inaccurate Inferences: Due to unvalidated or poorly prepared data, leading to suboptimal or even hazardous operational decisions.
• Operational Risks: Equipment may operate outside safe parameters, causing damage or reducing lifespan.
• Human Safety: Decisions based on incorrect data can lead to accidents that endanger employees’ lives.
• Financial Impact: Production interruptions and equipment damage result in significant financial losses.
Unlike a digital environment, where a failure might result in a lost sale, in industry, the impacts are much more severe. Therefore, ensuring data reliability through DataOps isn’t just a matter of efficiency but also of safety and responsibility.
Best Practices for DataOps in Industry
To implement DataOps effectively, it’s important to follow some best practices:
1. Process Automation: Utilize tools that automate data collection, processing, and validation.
2. Continuous Integration: Adopt pipelines that allow continuous integration of new data and models.
3. Monitoring and Alerts: Implement monitoring systems to quickly detect and respond to data anomalies.
4. Team Collaboration: Promote communication between operations, engineering, and data science teams.
5. Data Governance: Establish clear policies for data management and access, ensuring compliance and security.
Conclusion
DataOps is an essential component for modern industry, ensuring that data is reliable and that analytical models operate safely and effectively. By adopting DataOps practices, industries can not only improve operational efficiency but also mitigate significant risks associated with inconsistent or incorrect data.
Investing in DataOps means investing in the safety, quality, and competitiveness of your AI-driven industrial operations.
References
• Challenges of Industrial Data and How to Overcome Them – Our first blog post details the interferences that industrial data faces and the best practices to mitigate them.
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Optimize your industrial operations with DataOps. Stay ahead in safety, quality, and competitiveness by embracing data reliability and security.