Within KeyPlants Automation’s Digital Solutions Division, we are driving the digital transformation of industrial operations across manufacturing and life sciences. A critical step in this journey is enabling trusted, scalable, and governed data flows from factory assets to the cloud — unlocking closed-loop real-time decision making. 

We are now offering a master’s thesis opportunity focused on exploring, comparing, and architecting cloud-based industrial data storage and governance solutions that bridge the OT and IT worlds through a Unified Namespace (UNS) and DataOps practices.

Industrial organizations generate massive volumes of machines and process data from PLCs, sensors, SCADA, etc. However, this data often remains siloed, underutilized, or difficult to govern when scaled to the enterprise or cloud. With the rise of Unified Namespace architectures, MQTT-based event-driven data exchange, and industrial DataOps frameworks, the opportunity exists to create an end-to-end, governed, and future-proof topology for industrial data. 

• Explore and evaluate available industrial data platforms, UNS tools, and cloud storage solutions for OT data (e.g., Azure, AWS, InfluxDB Cloud, Aveva CONNECT, HighByte Intelligence Hub, HiveMQ, etc.).
   
• Design a reference architecture (topology) that enables reliable, governed, and scalable OT-to-cloud data flows.
   
• Establish data governance principles (naming conventions, security, lineage, metadata) to ensure data trust and reusability across enterprise layers (L2–L5).

• Conduct a technology landscape study of existing industrial data platforms and UNS tools.
   
• Map OT-to-Cloud data flow architectures, including edge gateways, brokers, pipelines, and cloud storage.
   
• Define evaluation criteria for scalability, governance, integration, and lifecycle management.
   
• Design and document a conceptual and logical topology for industrial data storage using DataOps principles.
   
• Optionally, build a prototype implementation connecting simulated or real OT data to a cloud database via a UNS and broker layer.
   
• Summarize findings in a guideline for selecting and deploying future industrial data storage architectures.

• A comparative analysis report of leading UNS and DataOps/Cloud tools for industrial use.
   
• A reference architecture diagram for OT data storage and governance in the cloud.
   
• A prototype data pipeline.
   
• Recommendations for governance models, standards, and best practices to ensure long-term scalability and trust.

Master’s students in:
- Industrial Engineering & Management
- Computer Science / Information Technology
- Automation Engineering / Industrial Digitalization
- Data Science or related programs with interest in OT/IT convergence

Number of students: 1–2

Start date: January 2026

Duration: 20 weeks
 

Ehsan Keramati
Digital Solutions Leader – KeyPlants Automation

ehsan.keramati@keyplantsautomation.com

Your application should include:
- CV
- Transcript of grades

We review applications continuously and may close the position earlier than the specified date.