Challenges moving data science proof of concepts (POCs) to production
Many companies want to be able to leverage the power of data and look to invest in data science proof of concepts as a way to tiptoe into it. Unfortunately, a high number of proof of concept initiatives fail to make it to production. From my experience, there are multiple reasons why this happens. The challenges with operating data science are more than purely about creating a predictive model out of sample data. There are organizational, project, data, and infrastructure that an organization must face along their journey to be data-driven.
Organizational Issues:
Multiple organizational factors can impact how likely data science projects are to work out. Having teams empowered to put application code into production, the composition of the teams, and the organization’s mentality all contribute to the success of the project.
Empowerment
In large traditional companies, it is often the case that the data science team is not empowered to put models into productions. From not having access to production data, to not being allowed to push code or applications to…
