Because DataOps builds on DevOps, cross-functional teams that cut across “skill guilds” such as operations, software engineering, architecture and planning, product management, data analysis ...

Putting the “Ops” in DataOps: Success Factors for Operationalizing Data, a recent report from BMC in partnership with 451 Research and S&P Global, found that defining a successful DataOps ...

Ashish Thusoo, co-author of Creating a Data-Driven Enterprise with DataOps (O'Reilly 2017) offered a more pragmatic definition. "DataOps is a new way of managing data that promotes communication ...

In this expanded taxonomy, Gartner constrains DataOps to the challenges associated with building, managing, and scaling data pipelines in a way that promotes reusability, reproducibility, and ...

DataOps is still in its early days, ... where what you do from a data management and architecture perspective, ... Cross-functional ownership of operations over siloed responsibilities ...

Cross-functional methodologies including DataOps, DevOps and MLOps have exploded over the last few years as a way to extend beyond traditional IT, tap into unique business functions, and drive faster ...

We report on Jesse Anderson's 2024 Data Teams Survey which showed a lag in DataOps capabilities, slow LLM adoption, and a concerning decline in perceived value creation by data teams. It called out th ...

Developing: Strategy is developing, but practices and architecture may not yet be closely linked to critical business outcomes. Functional: Strategy is largely developed, with some high-priority ...

But this means successfully implementing DataOps practices, technologies and expertise to get the most out of their data management initiatives. In our report, we rank an organisation’s maturity based ...