Unlocking the Power of Data Contextualization with Cognite

Table of Contents

1. Introduction
2. The Importance of Data Contextualization
3. The Role of Cognite in Data Contextualization
4. Understanding Contextualization and its Process
   • 4.1 Entity Matching
   • 4.2 3D Models and Point Clouds
   • 4.3 Engineering Diagram and Document Parsing
   • 4.4 Images and Video Data
   • 4.5 Data Modeling
5. Liberate and Contextualize Data with Cognite Data Fusion
6. Leveraging Contextualized Data with Generative AI
7. The Industrial Canvas: A Digital Workspace for Data-driven Planning
8. Creating Python Applications with low code and Generative AI
9. Conclusion
10. Resources

Introduction

Welcome to this Cognite product tour, a masterclass on data contextualization. In today's ever-evolving industrial landscape, the need for contextualized data to deliver business use cases at Scale has become paramount. Industries worldwide are recognizing the importance of data contextualization in maximizing the potential of generative AI and making informed, impactful business decisions. In this masterclass, we will explore how Cognite helps industrial leaders automate the contextualization of OT, IET, and engineering data, paving the way for accelerated digitalization and AI-powered solutions. Let's dive in!

The Importance of Data Contextualization

The reality is that in industrial environments, data is often siloed, difficult to understand, and rarely leveraged to generate real business value. Many subject matter experts struggle with manual data gathering and contextualization, spending the majority of their time on data rather than driving innovation and creating value. Data contextualization is the process of identifying and representing relationships between data, mirroring the relationships that exist between data elements in the physical world. By contextualizing data, we can break down these data silos and create a richer data model that enables easy access, efficient analysis, and Meaningful insights.

The Role of Cognite in Data Contextualization

Cognite, as an industry leader, has always recognized the importance of data contextualization. Since our founding, we have been dedicated to providing simple access to complex industrial data, empowering organizations to extract business value from their data. Our platform, Cognite Data Fusion (CDF), acts as a central hub for contextualized data, enabling industrial leaders to leverage the power of generative AI and make data-driven decisions at scale. With CDF, we help our customers transform their data into actionable insights, optimize production processes, and drive safe and sustainable operations.

Understanding Contextualization and its Process

To fully grasp the concept of data contextualization, let's explore some key techniques used in the process:

4.1 Entity Matching

Entity matching is a powerful tool for creating relationships between data from different sources. Whether it's matching assets to time series or correlating assets with nodes in a 3D model, entity matching allows us to contextualize diverse data within CDF. We offer different options for entity matching, including Quick Match for one-time batch jobs and Dimension Pipelines for continuous matching workflows.

4.2 3D Models and Point Clouds

3D models and point clouds provide a visual frame of reference for industrial data. These models allow users to easily find and Visualize data using Spatial references, making complex information Instantly understandable. Cognite's contextualization process involves linking different shapes and objects in 3D models to Relevant nodes in the Industrial Knowledge Graph, enabling interactive exploration and analysis.

4.3 Engineering Diagram and Document Parsing

Engineering drawings and documents contain critical information about industrial processes, systems, and equipment. However, accessing and extracting insights from these documents can be a time-consuming and manual process. Cognite Data Fusion automates the parsing of engineering diagrams, extracting important information such as Pin IDs and equipment specs. By contextualizing these diagrams, we create interactive representations that capture connections to relevant equipment tags and related documents.

4.4 Images and Video Data

Images and video data play a crucial role in understanding an industrial facility's state over time. Cognite's contextualization process enriches these files with computer vision capabilities, detecting objects, equipment tags, spills, and more. By contextualizing visual data, we provide subject matter experts with up-to-date real-world views of the industrial reality, facilitating efficient decision-making and troubleshooting.

4.5 Data Modeling

Data modeling is the process of organizing data objects and standardizing the properties of real-world entities and their relationships. In Cognite, we offer different types of data models, including source data models, domain data models (ontologies), and solution data models. These models allow users to customize the structure and expectations of data for various use cases and applications, providing a unified and coherent representation of industrial data.

Liberate and Contextualize Data with Cognite Data Fusion

Before we can start contextualizing data within CDF, we need to liberate it from its siloed source systems. CDF provides various extractors that allow users to efficiently export data from different sources and bring it into the platform. For example, you can export time series from OPC UA, orders from SAP, or build custom extractors for any relevant data source. By liberating and centralizing the data within CDF, we create a foundation for effective contextualization and analysis.

Leveraging Contextualized Data with Generative AI

Cognite Data Fusion serves as a gateway for leveraging contextualized data with the power of generative AI. With the integration of Large Language Models (LLMs), such as Chat GPT and DALL-E, we can Translate human queries into GraphQL and generate fact-based answers based on the specific inputs provided. The AI-based contextualization in CDF allows subject matter experts to interact with their industrial data using understandable human language, bridging the gap between complex computing code and actionable insights.

The Industrial Canvas: A Digital Workspace for Data-driven Planning

Cognite's Industrial Canvas is a groundbreaking digital workspace that empowers subject matter experts to engage in data-driven planning, troubleshooting, and collaborative analysis. With the Canvas, users can access and visualize data from various sources, including sensor data, 3D CAD models, time series, and engineering diagrams. This interactive workspace allows for seamless exploration and analysis, facilitating the identification of performance issues, root cause analysis, and informed decision-making.

Creating Python Applications with Low Code and Generative AI

Cognite's low-code approach combined with generative AI enables subject matter experts to create Python applications without the need for extensive programming experience. With the power of natural language, users can describe the functionality they want in plain language. The Co-Pilot feature in CDF generates fully functional applications based on these descriptions, allowing users to search, select, and modify asset data effortlessly. This democratization of AI empowers industrial end-users to unlock the potential of their data and realize their business objectives.

Conclusion

In conclusion, data contextualization is a fundamental process for unlocking the value of industrial data. Cognite plays a pivotal role in simplifying and automating this process, offering a robust platform, Cognite Data Fusion, that acts as a central hub for contextualized data. By providing simple access to complex industrial data and integrating generative AI capabilities, Cognite empowers industrial leaders to make data-driven decisions, optimize operations, and drive innovation. With Cognite, the possibilities of contextualized data and generative AI are truly limitless, enabling transformative industrial transformation.

Resources

• Cognite Hub: Our user community where you can find answers to your questions, engage with other users, and access additional resources. Visit hub.cognite.com.
• Open Letter from our CEO: Gain valuable insights into the transformative power of generative AI and its implications for the industry. Read the open letter from our CEO, Girish Rishi.
• Industrial Canvas: Stay tuned for our next product tour, where we will delve deeper into this exciting new technology and explore its applications in greater detail.

Highlights:

• Contextualization is the process of identifying and representing relationships between data to mirror the relationships that exist between data elements in the physical world.
• Cognite Data Fusion (CDF) is a central hub for contextualized data, enabling industrial leaders to leverage the power of generative AI and make data-driven decisions at scale.
• Entity matching, 3D models, engineering diagram parsing, images and video data, and data modeling are key techniques used in the data contextualization process.
• Liberating data from siloed source systems and bringing it into CDF is the first step in the contextualization process.
• Cognite's Industrial Canvas provides a digital workspace for data-driven planning, troubleshooting, and collaborative analysis.
• Low code and generative AI empower industrial end-users to create Python applications without extensive programming experience, leveraging the power of natural language to describe desired functionality.

FAQ:

Q: What is data contextualization? A: Data contextualization is the process of identifying and representing relationships between data to mirror the relationships that exist between data elements in the physical world. It involves breaking down data silos, creating a richer data model, and enabling easy access and efficient analysis of industrial data.

Q: How does Cognite Data Fusion help in data contextualization? A: Cognite Data Fusion (CDF) serves as a central hub for contextualized data, providing simple access to complex industrial data. CDF allows users to liberate data from siloed source systems and leverage the power of generative AI. It enables subject matter experts to make data-driven decisions, optimize operations, and drive innovation at scale.

Q: What are some techniques used in the data contextualization process? A: Some techniques used in data contextualization include entity matching, 3D models and point clouds, engineering diagram and document parsing, images and video data contextualization, and data modeling. These techniques help create connections between different data sources and provide a comprehensive view of industrial data.

Q: How can I leverage generative AI in conjunction with contextualized data? A: Cognite Data Fusion integrates generative AI capabilities, allowing users to translate human queries into GraphQL and generate fact-based answers based on the specific inputs provided. By leveraging generative AI, subject matter experts can interact with their contextualized data using understandable human language, bridging the gap between complex computing code and actionable insights.

Q: What is the Industrial Canvas in Cognite? A: The Industrial Canvas is a digital workspace provided by Cognite that empowers subject matter experts to engage in data-driven planning, troubleshooting, and collaborative analysis. It allows users to access and visualize data from various sources, such as sensor data, 3D CAD models, time series, and engineering diagrams, facilitating informed decision-making and optimization of operations.

Q: How can I create Python applications with low code and generative AI? A: Cognite's low-code approach, combined with generative AI, enables users to create Python applications without extensive programming experience. With the Co-Pilot feature in Cognite Data Fusion, users can describe the functionality they want in natural language, and the platform generates fully functional applications based on these descriptions. This democratization of AI empowers industrial end-users to unlock the potential of their data and realize their business objectives.