Demand for analytics and data science talent shows no signs of abating, and training and up-skilling existing resources from within remains challenging.
Will "democratization" and "citizen data scientists" come to the rescue?
"Citizen data scientists" using no-code, drag-and-drop tools are promised as the solution. However, this approach fails to meet the needs of enterprise data science at scale. Why?
- Technical Limitations: No-code tools restrict data volumes/types and are limited to basic machine learning development and deployment methods.
- Silos Reduce Collaboration: Disparate tools across analysts, citizen data scientists, and expert data scientists creates technology silos and limits interoperability, collaboration, and governance.
- Up-skilling Challenges: Siloed tooling creates siloed skillsets. Without a unified platform, training novice data scientists or up-skilling data analysts is more arduous.
Closing the Data Science Talent Gap with the Power of Code
Organizations need to reimagine their analytics and data science talent strategy, focusing on developing talent from within on a common platform using code-first tools alongside their expert data scientists.
That’s why Domino Data Lab and AWS worked with David Stodder, Senior Director of Business Intelligence at TDWI, to identify five key plays to scaling data science through a code-first strategy.
Here's a sneak peek at the plays
Embrace code-first data science
Include up-skilling training and career paths.
Build a talent mosaic
Embed expert data scientists at the center of analytics efforts.
Centralize data science work
A central system-of-record reduces silos and accelerates model operalization.
Integrate the end-to-end lifecycle
Increase velocity of model development and deployment.
Build off prior knowledge
Promote reuse, consistency, reproducibility, and explainability.