Overview of handling a recurrent problem in Data Science projects, regarding the short time dedicated to actual analytics. Real cases worked at Fligoo will be presented, covering both management and technology aspects, as well as highlighting some current trends to deal with the 80:20 ratio.
Nowadays, most of the time in Data Science projects are not spent in performing analytics but in other tasks, such as organizing data sources, collecting samples and preparing datasets, compiling and validating business rules in data, etc. This fact has been studied as the "80/20 dilemma in Data Science projects", and its treatment is important to have better management of time and resources, especially in real-life projects that demand results in a short time frame. In this talk, we will present the main problems involved and exhibit some cases worked at Fligoo and technologies implemented to deal with this dilemma, then some current trends will be mentioned in order to optimize the effort in these projects. This presentation is intended for every data scientist, data engineer and project manager who may be interested in learning some tips and methodologies to tackle this issue.