Using analytics and machine learning (ML) to better understand your customers has become an everyday event in any data-driven enterprise. The good news is that organizations have large data volumes at their disposal to build and train ML models. The not so good news is that data science and engineering teams are faced with a series of blockers/challenges that prevent them from being as productive as possible. These challenges include complex data access approaches, the need to migrate and/or reformat data before analysis can begin, and the adoption of different operating models. But the challenges don’t stop there.
