From SiliconANGLE · Mark Albertson · · 1m
McKinsey Uses Knowledge Graph for Enterprise AI
McKinsey's knowledge graph connects enterprise data to power AI decisions.
In brief
McKinsey's knowledge graph connects enterprise data to power AI decisions. McKinsey uses a knowledge graph to link enterprise data with business relationships for AI applications. Originally reported by SiliconANGLE.
Knowledge Graph Basics
Knowledge graphs connect data with business relationships needed for AI decisions.
“If you’re using LinkedIn, if you’re using Wikipedia, if you’re using any social media, that’s a graph”
Social Media as Graphs
Social media platforms use graphs before enterprises did, according to McKinsey partner James Kaplan.
“Many of the social media companies have arrived at this and the power of this technology before the enterprise did”
Relational Databases Limit
Relational databases are good for transactional data but less effective for ambiguous or complex data.
“It’s much more intuitive than a relational database. We’re all used to massive relational databases”
AI Turns Unstructured Data
AI can convert unstructured information into structured business rules for knowledge graphs.
“Now, for the first time, we have the ability to interrogate complicated processes and create deterministic business rules programmatically”
Business Priorities Guide
Customer experience improvements may take precedence over productivity gains in ai applications.
“What if i pointed these ai improvements at customer experience rather than productivity”
McKinsey's EcliptOS System
McKinsey uses EcliptOS to connect c-suite strategy with everyday execution through agentic workflows.
“What we in effect created was a graph of databases”
Knowledge Graph Power
Flexible data schemas allow knowledge graphs to connect multiple databases for ai applications.
“One of the nice things about graphs is they have more flexible data schemas”
The bottom line
McKinsey uses a knowledge graph to link enterprise data with business relationships for AI applications.
