Enabling AI Through DataOps and Teamwork: How Banks Can Get Started
Supporting Tools and Techniques
Key research questions
- How can DataOps action data science?
- What benefits can DataOps bring?
- How do banks of all sizes reap the full benefits of data science?
Scaling up data science teams remains a vexing problem for banks and greatly hinders the implementation of artificial intelligence (AI) applications. The report examines the introduction of DataOps across the data science workflow to help banks automate and expedite many of the tasks for the development and running of analytic models. Celent believes DataOps has the potential to industrialize data science, through improved repeatability of findings and reduced time to identifying actionable insights.
DataOps can move analytics from discrete projects to a true business discipline with enormous potential.
The report describes the context of data science and AI, provides insight into the process for creating and deploying an AI model, and provides details about DataOps and its impact for banks desiring to make their data initiatives more efficient. The objective of this report is also to categorize tools banks can use when launching AI-based initiatives.