Insight Hub

Machine Learning, The Dynamic Risk Approach

Operators who have embraced a proactive continuous improvement approach to managing their Pipeline Integrity Management Programs are now including elements of data science to efficiently assess large volumes of data for trends and outliers. With structured and normalized data, data scientists can construct machine learning models that expose previously unavailable insights and automation.

Dynamic Risk has partnered with a growing number of pipeline operators across the globe to compile the vast amount of available data from their pipeline assets into our Insight Hub. With the support of these forward-thinking operators, and a significant investment of technical resources; the Insight Hub is poised to enable previously impossible insights and data applications.


Insight Hub Pipeline Predictive Analytics

Key Benefits Of Insight Hub

    • Detailed comparison metrics and like-in-kind analysis
    • Increased accuracy of predictive analysis
    • Data cleansing & augmentation
    • Trending analysis and insights
    • Ability to leverage an expansive dataset of pipeline information

Download Insight Hub Brochure

Who Is Insight Hub Designed For?

Our Insight Hub is open to all Transmission, Midstream and Upstream pipeline operators. Our team of subject matter experts are available to answer any questions or concerns to ensure your trust and confidentiality is not compromised. To learn more or arrange an exploratory discussion, please contact us directly at

Insight Hub – Pipeline Data Assessment and Analysis White Paper

This white paper describes how pipeline material and attribute data may be ingested into a multi-operator data warehouse to assess the quality and reliability of an Operator’s pipeline material and attribute data, and for evaluating certain components to pipeline data assessment and analysis.

Download our white paper to learn more about how we support pipeline operators with improved data quality for better decision-making.

Download White Paper