Industrial Data Engineering
At Consat, data engineering means building the pipelines, storage and transformation layers that make data usable. We turn raw telemetry from devices and systems into actionable insights, perfectly bridging the gap between data generation and value.
We engineer pipelines directly at the source, completely eliminating translation gaps. Our proven track record in manufacturing data modelling is something few engineering firms can match.
Case
Our service
areas
We offer end-to-end capabilities in industrial data engineering, enabling large-scale industrial data modelling and ensuring that your operational environments are fully integrated:
Data pipeline design and implementation: Designing both batch and streaming pipelines tailored to high-frequency machine data.
ETL/ELT processes: Connecting operational systems seamlessly to advanced analytics environments.
Data warehouse and data lake architecture: Creating structured, highly scalable storage solutions for diverse data types.
Real-time data processing: Building processing layers optimised for operational and industrial use cases.
Data modelling and governance frameworks: Establishing robust frameworks to ensure data quality and reliable reporting.
Integration of enterprise data sources: Unifying IoT, ERP, MES and other enterprise data streams into a single source of truth.
Industrial IoT partners
for
Nordic and global operations
Data trapped in siloed systems or unstructured formats can stall your digital transformation. We design and deploy enterprise data architecture that explicitly addresses these core bottlenecks:
- Unifying siloed systems: Breaking down barriers between ERP, MES and IoT platforms to deliver a single, comprehensive operational view.
- Handling high data volumes: Deploying scalable data pipelines for connected assets to efficiently process and store massive industrial data streams.
- Eliminating data delivery delays: Turning lag into insights so analytics teams no longer wait days for data that should be available in minutes.
- Ensuring data quality and governance: Eradicating unreliable reporting by embedding strict data quality frameworks from the edge to the cloud.
- Preparing data for AI and ML: Structuring and cleaning complex operational data so it is mature enough to build advanced machine learning models on.
Consat in numbers
Contact
Vinay Dhar
CSO, Consat-Orahi and Head of
Digital Transformation Business
Consat Digital Solutions