Decisionhouse: Prescriptive Analytics in the Data Stack

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Abstract

Data platforms have evolved by making data-intensive workloads native: SQL and query optimizers eliminated bespoke data-retrieval programs; Lakehouse added first-class support for ML (training and serving over the same data). Prescriptive analytics (computing optimal actions subject to constraints over data) is equally data-intensive, yet remains outside the platform: every optimization problem requires a hand-built pipeline from data extraction to solver invocation, rebuilt from scratch whenever the data or the requirements change. We propose Decisionhouses, a new class of data infrastructure that makes prescriptive analytics native. A Decisionhouse provides (i) DeQL (Decision Query Language), a declarative SQL extension where users express decision problems over relational data; (ii) automatic formulation selection that exploits query and data semantics to pick the right problem class and solver—a choice that can change a query’s complexity class from NP-hard to polynomial; and (iii) end-to-end integration of optimization into the data platform, from query parsing through solver execution. Decisionhouses can help address several challenges that have kept optimization outside data platforms, including pipeline brittleness, formulation expertise, structural blindness, and scalability cliffs, and make decision-making as accessible as querying data.