How to Build Efficient Low-code Operational Digital Twins

3 min readAug 24, 2023


Low-code Operational Digital Twins

In today’s digital revolution, the concept of the Operational Digital Twin stands out as a beacon of innovation. But what does it entail, and how can we efficiently construct them using low-code platforms?

This blog post seeks to answer these pressing questions.

Decoding the Operational Digital Twin

At its core, an Operational Digital Twin serves as a digital mirror, reflecting both the real-time and historical operations of tangible systems or assets. This could span vast infrastructures like oil rigs or be as compact as a single machine. Dr. Adam Rasheed insights from AWS shed light on its essence, emphasizing the real-time transparency it grants into the workings of physical systems.

operational digital twin

Such transparency paves the way for:

  • Boosting Efficiency: Real-time data becomes a catalyst for system enhancement. For instance, predictive maintenance can curtail downtime.
  • Economizing Costs: Monitoring component lifespans allows for optimal replacements, translating to cost efficiency.
  • Analyzing Trends: Past data becomes a goldmine for discerning system performance and pinpointing optimization zones
  • Augmenting Performance: Real-time insights empower both IT and field teams, equipping them to optimize system operations.

Architectural Blueprint of an Operational Digital Twin

Operational Digital Twin Architecture

Visualize the Operational Digital Twin as a tri-layered structure:

  1. Physical Data Reservoirs: The genesis of raw data, be it from sensors, machinery, controllers, or even multimedia sources. This data can be extracted directly from tangible equipment, databases, or software conduits.
  2. Data Engine: This powerhouse processes raw data, distilling insights that find their home in a database or data repository.
  3. Visualization Facade: Think of this as an interactive dashboard or a 3D canvas, painting a picture of trends, insights, and directives.

Traditional Implementation: The Roadblocks

While the digital twin paradigm is groundbreaking, its conventional blueprinting has been marred by sluggishness, exorbitant costs, and internal support challenges. The culprit? Over-reliance on intricate software programming techniques.

The Low-Code Digital Twin Revolution

Data Engine Node-Red

Prescient’s methodology is a game-changer, championing low-code platforms:

  • Node-RED: A visual-centric programming dialect rooted in blocks. Its hierarchical nature ensures simplicity and clarity, making data analytics a breeze.
  • InfluxDB: The go-to database for chronological data, renowned for its speed, efficiency, and versatile deployment options.
  • Grafana: A visualization maestro, offering a plethora of visualization genres and syncing flawlessly with InfluxDB.

Prescient has supercharged these tools, introducing distributed data processing in Node-RED, infusing Grafana with custom JavaScript for interactivity, and crafting APIs to bridge Grafana and Node-RED.


The low-code paradigm promises swift rollouts, cost-effectiveness, and in-house support. It’s a catalyst for ceaseless innovation, sans the need for mammoth software brigades. In our fast-paced digital realm, the ability to double-speed solution implementation can be a game-changer.

For a comprehensive visual guide, do check out this detailed video. We value your insights, feel free to share your thoughts!




Prescient Edge specializes in fusing massive, diverse data streams from physical assets, control software, work logs & other sources to extract new insights.