Rethinking the data assumption
For years, AI strategy has been built on a simple premise: either quality or quantity, preferably both, of data leads to better outcomes. That premise justified major investments in data platforms, pipelines, people, processes, and storage, enabling organizations to scale data access across the enterprise.
In many ways, that effort succeeded. Data is now abundant, accessible, and continuously generated. For many traditional AI applications, organizations have…