Data scientists are busy enough with statistical machine learning models and doing the data prep needed to create useful models. And data engineers are consumed with creating pipelines and tapping and making accessible the resources the data scientists and others need. The specialists staffing these roles are too focused on their own disciplines to worry about the hows and whys of semantic knowledge graphs and architectural transformation overall.
It’s obvious that other roles need to be created or updated to complement the data scientist and data engineering roles. Better data beats better algorithms in creating the kind of findable, accessible, interoperable, and reusable (FAIR) data most suited to large-scale AI efforts. There’s a need for knowledge engineers, architects, ontologists, taxonomists, and stewards to establish means of ownership and sharing the disparate kinds of FAIR data that are best managed and scaled via a knowledge graph.
A suitable data foundation for enterprise-wide AI to scale AI efforts is a necessity to guide real AI-scaling transformation. The AI-ready enterprise needs a proper, interoperable data foundation–the kind a good knowledge graph can provide–an enterprise can grow its own data, rules, and processes from that foundation to suit its AI needs.
Knowledge graphs have been around for over a decade now. The technology is mature and extensible. At this point, it’s hard to find a member of the Fortune 50 that hasn’t built a knowledge graph yet. But what is difficult to find is a large enterprise that’s taken its knowledge graph activity and used that graph as a foundation for its larger AI efforts. Most haven’t taken that mental leap yet. The complementary nature of the neural nets (statistical deep learning) and symbolic AI (embodied today in semantic knowledge graphs) can build awareness of this aspect and realize the power of reliable and scalable AI-led transformation.
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