Just like we have enterprise architecture for software projects, we need to have architects and data scientists working with them to define a project architecture (solution — application & data) for each data science project which has an end goal of being deployed to production..A standard layered enterprise architecture is depicted in the following figure.Layered Enterprise Architecture Hierarchy (Source: IBM)A lot of this is taken from an excellent guide called ‘Architectural thinking in the Wild West of data science’ which actually talks about a lot of these challenges in data science projects and also potential advantages of defining project architecture.Architectural thinking in the Wild West of data scienceHaving freedom of choice with programming languages, tools, and frameworks improves creative thinking and evolvement.developer.ibm.comTypically, an enterprise architect defines standards and guidelines that are valid across the entire enterprise..A solution architect works within the framework that the enterprise architect defines..This role defines what technological components fit specific projects and use cases..Optionally you also have application architects who focus more on components pertaining to the application within the framework of the solution architecture and data architects, who define the data-related components..Often the solution architect usually fills these roles.Usually, data scientists would rarely interact with the enterprise architect and work more with the solution architect (and application data architects) directly..Data Scientists should be able to envision an end-to-end solution architecture for their projects and also even provide essential inputs to architects to improve and transform the enterprise architecture over time..Evolution is key and since data science is an emerging and innovative field, you will need to come out of the regular software project architecture templates for data science projects.Force-fitting Tools and Industry Standard ProcessesJust because a process model works for specific projects or verticals doesn’t mean it will be effective for data science..A lot of enterprises, particularly companies with a larger employee base tend to use a lot of processes in their daily operations and project executions..We used to have the waterfall model..Now we have Agile, Kanban, Scaled Agile, Scrum , Scrumban and so many more variations!.The intent here is to not follow these process frameworks to the letter and adapt them into something which might be best for your team and also based on the type of projects.. More details