You can’t define the scope of a chimera data platform without defining what you mean by data.
Most discussion of data science, machine learning and AI uses the term “data” to mean datasets that are snapshots in time, collected for analysis. This is analytical data.
Outside the data science team, however, most data flows through the organization as individual pieces being created, updated and deleted for immediate needs. This is operational data.
While your data science team’s software manages analytics data, every other piece of software used inside your organization manages operational data.
So to incorporate all these components into a single coherent experience - a chimera data platform - you have to expand the scope of what you consider data from just analytical data to include both analytical and operational.