In this work, the authors propose that the information from different laboratories, linked to animal health analysis (here called interlaboratory data), can be examined in the light of Knowledge Management and Data Science technology. And when interlaboratory data crosses, new knowledge assets are generated, through information useful in animal diagnosis, contributing to scientific studies in this area and providing reliable data for the decision making of the laboratories that originated them. For this, a proof of concept was developed and demonstrated, using the Python programming language, simulating some scenarios, crossing data collected in the laboratories and possible practical uses. It also presents a bibliographic review that addresses the fundamentals of Knowledge Management and its relationship with the modern tool known as Data Science and the so-called insights.