Beyond Predictive Maintenance: How Prescient Enhances Asset Productivity with Digital Twins and Data Integration

3 min readOct 16, 2023

In the realm of asset management, the term “predictive maintenance” often takes center stage. However, Prescient’s perspective on asset productivity extends beyond this singular focus, emphasizing a holistic approach that integrates diverse data sources and fosters a data-driven culture.

The concept of Digital Twins has emerged as a potent solution, offering more than just a visual representation of physical assets. But what does it bring to the table in enhancing asset productivity?

Let’s find out.

Understanding Asset Productivity

Understanding Asset Productivity

Asset productivity isn’t merely about predicting when a machine might fail. It’s about understanding the entirety of an asset’s performance, from its operational data to its maintenance history. This comprehensive view allows for more informed decisions, optimizing both the asset’s lifespan and its operational efficiency. Delving deeper, while predictive maintenance offers a glimpse into potential issues, it’s essential to recognize its scope and limitations in the broader spectrum of asset productivity.

Predictive Maintenance: Just the Beginning

Predictive maintenance, which employs sensors and algorithms to anticipate machine issues, is crucial, it’s just one piece of the puzzle. Sensors like vibration, current, flow, and pressure sensors can predict up to 80–90% of potential failures. However, they often require human expertise, such as a vibration analyst, to fine-tune the results. Moreover, these external sensors might not always pinpoint the exact root cause of an issue.

Integrating Diverse Data Sources

Prescient emphasizes the importance of integrating various data sources to achieve a more comprehensive understanding of asset health:

Machine Data: Modern equipment, like the mud pump in oil and gas drilling, generates vast amounts of data. This data, often referred to as EDR (Electronic Drilling Recorder) data, provides insights into various parameters, such as pressure and strokes per minute. By analyzing this data, one can gauge the stress levels and potential issues of the equipment.

Maintenance Records: These records, whether digital or manual, offer a historical perspective on equipment health. By combining real-time machine data with maintenance records, one can compute the exact operational hours of each component, setting thresholds for predictive maintenance. Bridging these diverse data sources not only enhances predictive accuracy but also sets the stage for a more proactive approach to asset management.

Continuous Improvement with Data Labeling

Data models, especially in their initial stages, may not encompass all possible failure mechanisms or events. Therefore, every failure should be meticulously labeled with its root cause and fed back into the data model. This iterative process ensures that the data model continually improves, becoming a robust system that not only aids in maintaining equipment but also becomes a critical repository of operational knowledge. With a refined data model in place, the foundation is set for cultivating a culture that values and acts upon data-driven insights.

Fostering a Data-Driven Culture

The success of any digital solution hinges on its adoption by the workforce. Prescient believes in:

  • Early Involvement: Engage teams from the implementation phase, incorporating their feedback and knowledge into the solution.
  • Utility: Ensure that the digital solution offers tangible benefits. For instance, if a logbook app doesn’t provide actionable insights, its adoption rate will likely be low.
  • Start with Enthusiasts: Begin the digital transition with teams eager for innovation. Their success stories will inspire others to follow suit.


Asset productivity optimization is a multifaceted endeavor. By merging diverse data sources, accentuating the role of predictive maintenance, and nurturing a data-centric culture, we at Prescient are poised to harness the full potential of digital solutions. Our methodology embodies this comprehensive vision, signaling a promising trajectory for industries reliant on assets.

For a more in-depth exploration of our methodologies and insights, we invite you to check out our video guide.




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