  Combining discovery and augmentation is important in the era of data usage
when it comes to predicting the outcome of tasks. However, having to ask the
user the utility function to discover the goal to achieve the optimal small
rightful dataset is not an optimal solution. The existing solutions do not make
good use of this combination, hence underutilizing the data. In this paper, we
introduce a novel goal-oriented framework, called BOD: Blindly Optimal Data
Discovery, that involves humans in the loop and comparing utility scores every
time querying in the process without knowing the utility function. This
establishes the promise of using BOD: Blindly Optimal Data Discovery for modern
data science solutions.
