Operational Excellence with Insight Integration in a Data-Driven World

Prescient
3 min readAug 29, 2023
Operational Excellence with Insight Integration

In today’s digital age, businesses are inundated with vast amounts of data. This data, if processed and analyzed correctly, can provide invaluable insights that can drive business growth, improve operational efficiency, and enhance overall performance.

Yet, the true challenge emerges in efficiently tapping into this data intelligence, particularly when the data is dispersed across diverse locations, from cloud repositories to edge devices.

The Problem

At the heart of the issue is the sheer volume and complexity of data. Many businesses struggle with maintaining operational efficiency, ensuring that their equipment runs at peak performance without any downtime. However, a deeper underlying challenge is improving the performance of personnel. No matter how advanced your analytic solutions are, if your team cannot utilize them effectively, their value diminishes.

Moreover, while some data has migrated to the cloud, a significant portion remains at the edge, such as on drilling sites or in refineries. To truly gain comprehensive insights, businesses need to access and integrate data from all these sources.

The Solution: Data Insight Integration

Data Insight integration is the process of extracting insights from multiple data streams and integrating them to provide a holistic understanding of physical systems and processes. This allows businesses to continually refine and improve their operations.

For instance, consider a scenario where an AI solution uses vibration sensors on equipment to detect potential issues. While the AI can identify a problem based on pattern matching, it might not provide the reason behind the issue. This is where data inside integration comes into play. By examining work logbooks, checking when components were replaced or serviced, and analyzing other relevant data, businesses can get a clearer picture of the problem and take informed corrective actions.

However, the sheer volume of data being collected presents a challenge. Extracting insights from this data can be daunting, and many of today’s solutions take a considerable amount of time to prepare the data and then derive insights.

Introducing the Data Engine

The data engine is a revolutionary solution that addresses these challenges. It combines a data pipeline, which focuses on acquiring, transforming, and validating data, with data fusion, analytics, and an event data layer. This makes the data easily accessible for various applications, be it business intelligence, AI, or other data-driven applications.

One of the standout features of the data engine is its scalability and flexibility. It can be deployed in various locations, whether in the cloud or at the edge, and can be scaled both horizontally and vertically to handle varying data streams and complexities.

Furthermore, the data engine simplifies the process of data inside integration. With its visual, modular, and hierarchical design, it allows for easy management of complex data operations. This self-documenting solution fosters collaboration among teams, making it easier to understand and work with.

Also Read: Why Your Organization Need a Data Engine

Operational Digital Twin: A Practical Application

An operational digital twin is a real-time representation of physical assets, processes, or systems. By leveraging the distributed data engine technology, businesses can build a digital twin that provides real-time visibility, alarms, warnings, and instructions. Additionally, it offers historical data analysis, enabling businesses to understand long-term operational trends and improve efficiency.

Building such a solution using the data engine is not only efficient but also significantly faster than traditional methods. The low-code approach ensures rapid iterations and continuous improvement.

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Prescient

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