Data is dead... without what-if models
Peter J. Haas, Paul P. Maglio, Patricia G. Selinger, Wang-Chiew Tan · Proceedings of the VLDB Endowment · 2011
Current database technology has raised the art of scalable descriptive analytics to a very high level. Unfortunately, what enterprises really need is prescriptive analytics to identify optimal business, policy, investment, and engineering decisions in the face of uncertainty. Such analytics, in turn, rest on deep predictive analytics that go beyond mere statistical forecasting and are imbued with an understanding of the fundamental mechanisms that govern a system's behavior, allowing what-if analyses. The database community needs to put what-if models and data on equal footing, developing systems that use both data and models to make sense of rich, real-world complexity and to support real-world decision-making. This model-and-data orientation requires significant extensions of many database technologies, such as data integration, query optimization and processing, and collaborative analytics. In this paper, we argue that data without what-if modeling may be the database community's past, but data with what-if modeling must be its future.