Industrial Data Operations
Using DataOps and AI in Industry
Transforming and managing IT/OT/ET data for enhanced operational efficiency and governance
Data connectivity and orchestration
Seamless integration with various data sources through pre-built industrial connectors, ensuring a secure transition of all available industrial data from OT to IT systems.
Unified namespace effortlessly organizes industrial data sources, with the capability to connect MQTT and Sparkplug B clients, transform data to OPC DA, UA, Modbus formats, or store it in a historian or ODBC-compliant database or data lake for further processing.
Exceptional versatility, speed, and user-friendliness, all while maintaining the highest standards of data integrity and security.
Automatic discovery of all possible data points from sources and can virtually eliminate cache latency and stale, outdated data.
Data transformation
Converting raw data into meaningful and interpretable information that provides valuable insights to enable more informed and accurate decision-making in business strategies and operations.
Complex data processing tasks and routines are streamlined, effectively reducing manual effort and error usually associated with data processing, significantly boosting operational efficiency and productivity.
Transforms data by standardizing formats, cleaning, and enriching data, adding to the assurance of data integrity and quality across an entire enterprise.
Enables dynamic scalability and adaptability in data handling, adjusting in real-time to accommodate new data sources, formats, and analytics requirements.
Unified namespace
Provides universal access to all enterprise data with Unified Namespace, enabling employees at all organizational levels to instantly understand and interact with contextualized information from any industrial network node cohesively.
Painless integrations, standardized data presentation across various formats, and reduced network complexity, achieving benefits with improved process efficiency and faster time-to-market.
Reduces network and data storage resources by eliminating the need for multiple point-to-point data connections, inherently bypassing highly specialized engineering services and personnel.
Enhances predictive analytics and decision-making by enriching data with contextual metadata, such as time, location, and source, allowing for more accurate and relevant insights tailored to specific operational scenarios.
Knowledge graph
Industrial Knowledge Graph integrates data from diverse sources and efficiently combines powerful analytical tools through physics, machine learning, and advanced analytics.
Panoramic view of data systems that encompass processes, products, customers, and entire ecosystems.
Generates predictions and contrives insightful inferences, significantly improving informed decision-making processes effectively.
Identifying and implementing predictive relationships within data, which are crucial for building quality analytics and machine learning models.
Data governance
Maintain high standards for processing and consumption of data sources, crucial for quickly identifying, addressing, and resolving production issues.
Supporting compliance and integrity with stringent regulations and specifications, facilitating accurate tracking and reporting of important data, and ensuring regulatory compliance at all stages of Industrial Data Operations.
Ensure consistent and accurate data sharing across different supply chain partners, resulting in enhanced visibility, forecasting, and coordination within enterprise operations.
Promotes a culture of accountability and transparency in data handling, empowering personnel at all levels to understand their roles in data stewardship.
Data insights
Real-time KPI monitoring of data sets with real-time alerts delivered to all of the right people, along with specific contextual information in a single, intuitive interface.
Predict events before they occur and automatically analyze the root cause of issues and events using state-of-the-art deep learning and generative AI engines.
Built-in physics and engineering computations and adaptive models to enrich enterprise data, resulting in immediately useful, high-value data insights.
Advanced analytics for trend identification and forecasting, leveraging machine learning algorithms to uncover hidden patterns, and predict future trends in operational and market data.
