Original article was published on Artificial Intelligence on Medium
Context Awareness In A Decision Intelligence System
Context-awareness is the distinguishing characteristic of a decision intelligence system. It puts it in an altogether different league than other data-driven intelligence options, such as BI, Intelligent Search, and Machine Learning powered prediction engines.
Many researchers and implementers of decision intelligence systems fall short of engaging the context fully in their design and architecture. Being context-aware does not end with using contextual information for reference purposes only.
Is there a deeper reason for the limited conscious utilization of context? Or is it merely the lack of available data or lack of attention?
Could it be that our years of training in the reductionist approach is somehow hindering us from being fully context-aware? In our zeal for separation of concerns, we may be inadvertently seeking to design components that work across all contexts.
Application independence has been a critical design principle in database systems design. The database schema design is for a domain—it is generally oblivious to actual query patterns—the real clients of the database.
In training machine learning models too, we seek for robustness across all contexts.
A decision intelligence system has to transcend beyond drawing rigid boundaries influenced by heuristics, such as separation of concerns. What transpires at the edges is often much more important than what happens within.
Understanding context, measuring and estimating the effects of context, and effectively deploying context is a fundamental aspect of designing a decision intelligence system.