Data Liquidity
Definition. The ease with which customer data flows between systems — to the compute where work is most efficient, to the activation where the response is needed, to the analytics where insight is generated.
Why it's the foundational friction point. "The infrastructure decisions hit on the foundational friction point between Packaged CDPs (like AEP) and the Composable Data Stack: the definition of data ownership and liquidity. In a truly composable architecture, data flows freely to where compute is most efficient." (source.cdp-recommendation-agent-md)
How packaged CDPs constrain liquidity. Through quota-based egress limits (constraint.aep-first-gen-export-500kb, constraint.aep-second-gen-export-1500kb), through query-time caps (constraint.aep-adhoc-query-timeout-10min), and through ecosystem-locked destination frameworks. These are commercial decisions, not failures — they protect compute and storage economics for the vendor.
How composable stacks maximize liquidity. Cloud data warehouses impose no governance on egress beyond raw cost. Reverse-ETL tooling makes any CDW table a potential source for any destination.
The architectural cost of high liquidity. Liquidity isn't free. It moves the burden of governance (consent enforcement, suppression, identity stitching) from the platform to the customer. Organizations without strong data engineering can drown in liquidity.
The agent's job. Help customers articulate how much liquidity they need, where it's needed (analytics? activation? ML?), and which architectural posture matches their data-engineering capacity.