Reading this piece is like going through my sales calls from the last three years, as a Governed Semantic Layer is inherently a logic/knowledge layer necessary for aligning these 3 bodies which of course stretch across domains.
For example, in my world, when discussing architecture frameworks to address this three-body problem, I would argue for a Governed Semantic Layer in the stack that turned the domains into interconnected semantically aligned micro kg's and state that a Governed Semantic Layer would:
-Aligns terms across BI dashboards, ML features, and Ops systems
-Validates policies before actions or predictions are taken
-Feeds AI with grounded, explainable knowledge
-Provides decision traceability across all three “bodies”
But you would still need other layers to complete the picture with other layers.
Sachin, this is a fantastic piece! You've articulated a problem that resonates deeply with what we see every day. The analogy of the orchestra is spot on. It's not just about having the instruments (the data, the models), but about having them play in harmony towards a common goal.
I especially appreciate you highlighting the human element, the 'trust and system timing.' Tech alone doesn't solve this; it's about how people interact with the data and the systems. It's about bridging the gap between insight and action. That chasm is where value is lost, and frankly, where most data initiatives fail to deliver on their promise.
Your 'AI-Readiness Maturity Staircase' is a great framework. It's a reminder that AI isn't some magical solution, but a journey that requires a solid foundation. Fast, clean, reliable data isn't just table stakes; it's the oxygen that fuels the entire operation. Without it, the fancy models and agentic systems are just castles in the sky. Thanks for sharing your insights!
Reading this piece is like going through my sales calls from the last three years, as a Governed Semantic Layer is inherently a logic/knowledge layer necessary for aligning these 3 bodies which of course stretch across domains.
For example, in my world, when discussing architecture frameworks to address this three-body problem, I would argue for a Governed Semantic Layer in the stack that turned the domains into interconnected semantically aligned micro kg's and state that a Governed Semantic Layer would:
-Aligns terms across BI dashboards, ML features, and Ops systems
-Validates policies before actions or predictions are taken
-Feeds AI with grounded, explainable knowledge
-Provides decision traceability across all three “bodies”
But you would still need other layers to complete the picture with other layers.
Saved! Looking forward to reading it
Sachin, this is a fantastic piece! You've articulated a problem that resonates deeply with what we see every day. The analogy of the orchestra is spot on. It's not just about having the instruments (the data, the models), but about having them play in harmony towards a common goal.
I especially appreciate you highlighting the human element, the 'trust and system timing.' Tech alone doesn't solve this; it's about how people interact with the data and the systems. It's about bridging the gap between insight and action. That chasm is where value is lost, and frankly, where most data initiatives fail to deliver on their promise.
Your 'AI-Readiness Maturity Staircase' is a great framework. It's a reminder that AI isn't some magical solution, but a journey that requires a solid foundation. Fast, clean, reliable data isn't just table stakes; it's the oxygen that fuels the entire operation. Without it, the fancy models and agentic systems are just castles in the sky. Thanks for sharing your insights!