Resources & Perspectives

Delivering value via combination of technology, domain expertise, and foundational methodologies is driven by one’s ability to tailor nature of the knowledge framework (e.g., the knowledge graph plus ancillary models and methods), the problem at hand, and the nature of outputs or outcomes needed. Here is just a sample of how to assemble and organize data/knowledge, to activate and apply data and knowledge efficiently, to accelerate and improve asset management system implementations, and to explore options and act in terms of futures, not just the past or today.
Why? • Digital Transformation has failed to deliver on its promises due to a focus on data rather than knowledge. • Industrial knowledge, including proprietary and lessons learned, remains untapped. • Complexities in data silos and namespace issues hinder effective data utilization. • Companies struggle with achieving value without “perfect data”
What? • Capture and persist institutional and tribal knowledge using advanced technologies. • Develop a knowledge graph- based model to integrate and scale organizational knowledge. • Enable a "Data Do-Over" by organizing data without disrupting existing systems. • Facilitate understanding and decision-making through demystification of data.
How? • Use knowledge graphs to model and embed knowledge across the organization. • Implement data and knowledge model-based retrieval systems to break down silos. • Utilize generative AI to translate plain language queries into actionable insights. • Leverage AI and simulation to explore scenarios and inform strategic decisions.
Value? • Transform data into actionable insights and understanding. • Enhance decision-making capabilities by integrating knowledge with data. • Achieve scalable and sustainable digital transformation. • Deliver piecewise value through a building crescendo approach, avoiding "big bang" failures.
Transforming Data into Strategic Assets
Unlocking the Power of Knowledge Capture
Why? • Data are locked in silos with unique naming conventions, creating a "namespace problem." • Companies believe data must be perfect to be valuable, which is costly and impractical. • Existing digital transformation efforts have been stymied by data complexity. • Organizations lack a unified approach to leverage data for decision-making.
What? • Implement a knowledge graph to integrate and organize disparate data sources. • Develop a model that allows for data fusion without altering existing systems. • Facilitate a "Data Do-Over" to achieve insights without needing perfect data. • Use data as a foundation for understanding and strategic decision-making.
How? • Employ knowledge graphs to create a unified data model across silos. • Use AI to enable plain language queries and fuse data for comprehensive insights. • Layer an understanding graph over existing data systems to enhance accessibility. • Address the namespace problem by organizing data in a user-friendly manner.
Value? • Unlock the potential of existing data to drive operational efficiency. • Enable data-driven decision- making without the need for perfect data. • Break down silos to create a cohesive data strategy. • Deliver incremental value through a structured, scalable approach to data management.
Revolutionizing Asset Management
Why? • Asset management data is siloed and difficult to access across different domains. • Traditional systems struggle with integrating asset data for comprehensive insights. • Companies face challenges in optimizing asset performance and maintenance. • Existing asset management practices are not leveraging advanced technologies effectively.
What? • Integrate asset management data using a knowledge graph and generative AI. • Enable plain language queries to access and analyze asset- related information. • Develop a model that links asset data with organizational and operational insights. • Facilitate advanced asset management through data fusion and AI-driven insights.
How? • Use knowledge graphs to model assets and their relationships within the organization. • Implement generative AI to translate queries into actionable asset management insights. • Create a unified view of asset data across silos for better decision-making. • Leverage AI to enhance asset monitoring, diagnostics, and performance optimization.
Value? • Improve asset performance and maintenance efficiency through integrated insights. • Enable proactive asset management with real-time data access and analysis. • Enhance decision-making capabilities with comprehensive asset data visibility. • Deliver value by optimizing asset utilization and reducing operational costs.
Exploring Futures with Scenario Modeling
Why? • Companies struggle with strategic planning due to uncertainty and complexity. • Traditional methods rely heavily on gut feelings rather than data-driven insights. • There is a need to understand potential outcomes under various scenarios. • Existing models do not adequately incorporate dynamic and complex interactions.
What? • Develop dynamic process models that simulate asset, system, and enterprise behavior. • Use AI to explore different scenarios and inform strategic planning. • Incorporate reinforcement learning to identify optimal decision levers. • Extend data and knowledge models to include scenario simulation capabilities.
How? • Utilize knowledge graphs to model complex relationships and interactions. • Implement AI agents to simulate business processes and test performance. • Use reinforcement learning to optimize decisions under various scenarios. • Create simulations that integrate internal and external factors for comprehensive analysis.
Value? • Enhance strategic planning with data-driven scenario analysis. • Identify opportunities and pitfalls through comprehensive simulations. • Improve decision-making by understanding potential outcomes and risks. • Deliver reliable and explainable insights for capital and portfolio planning.
intessellate

Resources

&

Perspectiv

es

Delivering value via combination of technology, domain expertise, and foundational methodologies is driven by one’s ability to tailor nature of the knowledge framework (e.g., the knowledge graph plus ancillary models and methods), the problem at hand, and the nature of outputs or outcomes needed. Here is just a sample of how to assemble and organize data/knowledge, to activate and apply data and knowledge efficiently, to accelerate and improve asset management system implementations, and to explore options and act in terms of futures, not just the past or today.
intessellate
Why? • Digital Transformation has failed to deliver on its promises due to a focus on data rather than knowledge. • Industrial knowledge, including proprietary and lessons learned, remains untapped. • Complexities in data silos and namespace issues hinder effective data utilization. • Companies struggle with achieving value without “perfect data”
What? • Capture and persist institutional and tribal knowledge using advanced technologies. • Develop a knowledge graph-based model to integrate and scale organizational knowledge. • Enable a "Data Do-Over" by organizing data without disrupting existing systems. • Facilitate understanding and decision-making through demystification of data.
How? • Use knowledge graphs to model and embed knowledge across the organization. • Implement data and knowledge model-based retrieval systems to break down silos. • Utilize generative AI to translate plain language queries into actionable insights. • Leverage AI and simulation to explore scenarios and inform strategic decisions.
Value? • Transform data into actionable insights and understanding. • Enhance decision-making capabilities by integrating knowledge with data. • Achieve scalable and sustainable digital transformation. • Deliver piecewise value through a building crescendo approach, avoiding "big bang" failures.
Transforming Data into Strategic Assets
Unlocking the Power of Knowledge Capture
Why? • Data are locked in silos with unique naming conventions, creating a "namespace problem." • Companies believe data must be perfect to be valuable, which is costly and impractical. • Existing digital transformation efforts have been stymied by data complexity. • Organizations lack a unified approach to leverage data for decision-making.
What? • Implement a knowledge graph to integrate and organize disparate data sources. • Develop a model that allows for data fusion without altering existing systems. • Facilitate a "Data Do-Over" to achieve insights without needing perfect data. • Use data as a foundation for understanding and strategic decision-making.
How? • Employ knowledge graphs to create a unified data model across silos. • Use AI to enable plain language queries and fuse data for comprehensive insights. • Layer an understanding graph over existing data systems to enhance accessibility. • Address the namespace problem by organizing data in a user-friendly manner.
Value? • Unlock the potential of existing data to drive operational efficiency. • Enable data-driven decision-making without the need for perfect data. • Break down silos to create a cohesive data strategy. • Deliver incremental value through a structured, scalable approach to data management.
Revolutionizing Asset Management
Why? • Asset management data is siloed and difficult to access across different domains. • Traditional systems struggle with integrating asset data for comprehensive insights. • Companies face challenges in optimizing asset performance and maintenance. • Existing asset management practices are not leveraging advanced technologies effectively.
What? • Integrate asset management data using a knowledge graph and generative AI. • Enable plain language queries to access and analyze asset-related information. • Develop a model that links asset data with organizational and operational insights. • Facilitate advanced asset management through data fusion and AI-driven insights.
How? • Use knowledge graphs to model assets and their relationships within the organization. • Implement generative AI to translate queries into actionable asset management insights. • Create a unified view of asset data across silos for better decision- making. • Leverage AI to enhance asset monitoring, diagnostics, and performance optimization.
Value? • Improve asset performance and maintenance efficiency through integrated insights. • Enable proactive asset management with real- time data access and analysis. • Enhance decision-making capabilities with comprehensive asset data visibility. • Deliver value by optimizing asset utilization and reducing operational costs.
Exploring Futures with Scenario Modeling
Why? • Companies struggle with strategic planning due to uncertainty and complexity. • Traditional methods rely heavily on gut feelings rather than data-driven insights. • There is a need to understand potential outcomes under various scenarios. • Existing models do not adequately incorporate dynamic and complex interactions.
What? • Develop dynamic process models that simulate asset, system, and enterprise behavior. • Use AI to explore different scenarios and inform strategic planning. • Incorporate reinforcement learning to identify optimal decision levers. • Extend data and knowledge models to include scenario simulation capabilities.
How? • Utilize knowledge graphs to model complex relationships and interactions. • Implement AI agents to simulate business processes and test performance. • Use reinforcement learning to optimize decisions under various scenarios. • Create simulations that integrate internal and external factors for comprehensive analysis.
Value? • Enhance strategic planning with data-driven scenario analysis. • Identify opportunities and pitfalls through comprehensive simulations. • Improve decision-making by understanding potential outcomes and risks. • Deliver reliable and explainable insights for capital and portfolio planning.