The study of knowledge acquisition and scientific discovery has evolved into its own field thanks to the development of the heuristic perspective. This approach suggests that these processes can be understood through logic, rationality, and problem-solving techniques. Unlike traditional views, which conceive the justification of knowledge logic and rational, the heuristic perspective conceives problem-solving as a means to generate new knowledge in a logical and rational way. Recent advancements in this view have improved our understanding of problem-solving, proposing new methods and even algorithms that could potentially automate these processes. This perspective builds on ideas from philosophers like Plato, Aristotle and Lakatos and has been further developed by scholars such as Popper, Simon, Cellucci, and Magnani. The paper examines the heuristic view, its fresh conceptualization of problem-solving and discovery, and its implications for fields like Big Data and machine learning.