A discussion about the challenges data scientists face, why they’re looking to Python for help, and the need for enterprise-class features and support.

Businesses constantly strive to transform operations and differentiate their offerings to stay ahead of the competition. At the heart of most efforts is the need to rapidly develop and deploy many new data-centric applications. Unfortunately, projects often are slowed or delayed because data scientists are swamped with ever-more projects, or there are inefficiencies in the handoff between development and production.

Increasingly, Python—by virtue of its ease of use and powerful automation capabilities—is being used to speed the creation and deployment of data-driven applications. However, a limiting factor is that such open-source tools may not meet the performance, security, and replicability demands of production business environments.

To sort through these and other issues, RTInsights sat down with Stanley Seibert, Director, Community Innovation, at Anaconda; and Heidi Pan, Director, Data Analytics Software, at Intel. We discussed the challenges data scientists face, why they’re looking to Python for help, and the need for enterprise-class features and support.

Getting the Most Out of Data Scientists

RTInsights: With data scientists in such high demand, how can companies help them work to their fullest potential?

**Seibert: **Early data scientists were jacks of all trades, doing many different tasks. They would be involved in data prep, data quality checks, modeling, and then figuring out how to deploy the models. They did a bit of everything, which was part of the reason they were in such high demand. It was hard to find people with skills in so many different areas. But the field has matured. We’re starting to see specializations emerge. We’re starting to see more focus on organizations building out a team with different people with special skill sets.

You can have someone who just focuses on data prep and data quality, and you have someone who can focus on the model in question and how you validate the model. And you can have someone else focus on how to get models into production looking at what’s required to go from research prototype to something that you can deploy at scale. Each of these groups is becoming their own subspecialty. Getting more out of your data scientists might mean limiting the definition of what is a data scientist, but then augmenting them with other people with other job titles.

#analytics #big data platforms #data integration tools #data management #python #data analytic