OT Edge DataOps
Data-driven, intelligent industrial shop floor operations
Achieve Industry 4.0 goals through a unified IoT data layer
Learn how the Nokia MX Industrial Edge can help you achieve your Industry 4.0 objectives through a unified IoT data layer.
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What is the driver behind intelligent industrial shop floor operations?
Beyond traditional objectives around efficiency, productivity, resilience, and worker safety, enterprises also want to leverage Industry 4.0 and accelerate their digital transformation to initiate their transition to a circular economy. This will allow them to monitor emissions and material flows, identify ways to reduce carbon emissions and demonstrate how they are tackling the global climate and biodiversity crises.
How can industries adopt data-driven operational intelligence?
Industrial digitalization embraces the entire industrial process across the business, from resource extraction, through supply chain management, production, and delivery of goods to the end customer.
At the enterprise level, this requires IT/OT convergence: effectively utilizing OT data with IT tools to measure KPIs and compliance targets, while also promoting automation throughout the data lifecycle.
To make this happen organizations need data-driven operational excellence solutions. They want to benefit from the low-code/no-code application of a unified data layer to connect all the industrial sensors, controls, automation assets and systems used by various stakeholders across the company.
Data-driven operational excellence
Data-driven operational excellence, an augmented and automated decision-making approach is key to achieving business goals using Industry 4.0.
Key technology concept enabling this is DataOps. It's about how data is harnessed and in particular, how it’s leveraged to support daily decision-making at every level of the business – ensuring the right people and machines have access to the right information at any given moment. It involves comprehensive data collection, aggregation and harmonization close the data source, from various sources to gain a holistic understanding of operations.
In certain cases, this can augment human decision-making, for example, using an AI assistant, however in safety critical environments enterprises always require humans to be the final and ultimate decision makers.
Data-driven operations require modern data-architectures that include Edge DataOps and Industrial DataOps/Data Lakehouse with IT/OT convergence.
OT Edge DataOps
As edge computing becomes more prevalent, DataOps practices will extend to the edge, enabling real-time data processing and analysis closer to data sources. Edge DataOps will address the challenges of data locality, bandwidth limitations, and latency, enabling faster and more efficient processing at the edge. This makes OT data generated at the shop floor readily available for multiple use cases, including reporting, analytics, sharing, and decision-making (OT/IT machine learning/AI), for shop floor apps (at the edge) and in the cloud. Edge DataOps is a crucial part of successful IT/OT convergence
Industrial DataOps
As edge computing becomes more prevalent, DataOps practices will extend to the edge, enabling real-time data processing and analysis closer to data sources. Edge DataOps will address the challenges of data locality, bandwidth limitations, and latency, enabling faster and more efficient processing at the edge. This makes OT data generated at the shop floor readily available for multiple use cases, including reporting, analytics, sharing, and decision-making (OT/IT machine learning/AI), for shop floor apps (at the edge) and in the cloud. Edge DataOps is a crucial part of successful IT/OT convergence
Industrial DataOps is about making data available to multiple use cases including reporting, analytics, sharing and decision making (OT/IT machine learning/AI), at shop floor level (on the edge) and in the cloud. Key activities are data collection and aggregation, normalization/harmonization, and contextualization.
Industrial DataOps typically uses Data Lakehouse capabilities – these are modern data architectures that create a single platform by combining the key benefits of data lakes (large repositories of raw data in its original form) and data warehouses (organized sets of structured data).
Deep dive to data-driven operational excellence
Get an in-depth look at data-driven operational excellence from our White Paper: Rethinking IT/OT convergence: the future of data-driven, intelligent industrial shopfloor operations
Nokia solution for Edge DataOps
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