How to Operationalize Data Science in the Enterprise: The Five Challenges to Address
The end-to-end process for launching a data science project is daunting - and many enterprise projects never make it to production. The process is similar in most organizations and consists of: Data collection, last mile ETL, feature engineering, and machine learning. However, while the process is understood by most teams, the actual execution is very complex and involves a high-level of operational risk.We recently published a complete guide to operationalizing data science. In this guide, we identified five complex issues to be addressed, for a business to derive value from operationalizing data science. Highlights from the paper: Issue 1: Quality There are two groups in the data science process who are not aligned operationally:1) Data engineers build data pipelines with SQL or GUI-based tools, 2) Data scientists build machine-learning scoring pipelines using Python or R. Software engineers must often reimplement much of the work from these two groups before production…
