A DataOps Engineer owns the assembly line that's used to build a data and analytic product. Data operations (or data production) is a series of pipeline procedures that take raw data, progress through a series of processing and transformation steps, and output...
Wayne Eckerson discusses the critical capabilities required for DataOps success and how to compare different vendors’ DataOps offerings
Data organizations don’t always have the budget or schedule required for DataOps when conceived as a top-to-bottom, enterprise-wide transformational change. An essential part of the DataOps methodology is Agile Development, which breaks development into incremental...
Adopting DataOps can be easy; by following DataKitchen’s ‘Lean DataOps’ four-phase program, you can roll out DataOps in smaller, easy-to-manage increments.
Chris Bergh chats with author Randy Bean about his book, Fail Fast, Learn Faster: Lessons in Data-Driven Leadership in an Age of Disruption, Big Data & AI.
In this five-module course, Mike Lampa & Chris Bergh teach data professionals to plan their organization’s DataOps program for low errors & fast deployment.
“How do you make it so that your data and analytics team can get the ideas from their heads into production quicker? How do you create a process like a factory that produces Toyotas and not AMC Pacers? How can you get these teams who work all over the company to collaborate better? Those are the problems that DataOps solves.”
Cognizant’s JP Thakur & DataKitchen’s Chris Bergh discuss how DataOps sets the foundation for Data Modernization initiatives enabling continuous data & insight.
Learn about DataOps from data leaders Jim Tyo, Invesco CDO; Kurt Zimmer, AstraZeneca Head of Engineering for Data Enablement & Ryan Chapin, former GE exec.
DataOps “equity as code” provides the approach and methodological tools to impose equity controls on AI algorithms. A program of automated testing and continuous monitoring can help avoid deploying AI systems that instantiate and perpetuate inequities at scale.