DataOps is becoming increasingly important to enterprise competitiveness, but it is hard to start and even harder to scale. There is a tendency to model DataOps efforts after DevOps, which most organizations now have some experience with. This approach is problematic, and the most serious problems tend to surface when organizations try to scale their DataOps efforts. And programs will need to scale – data and the demand for data-driven insights are both growing quickly. The average company was managing 5,000 datasets in 2020, up from 4,300 in 2018, a 16 percent increase
This thought paper will highlight the unique DataOps characteristics, explain the most important differences between DevOps and DataOps, identify the relevant DataOps challenges, and provide enterprise guidance for establishing and scaling DataOps programs.
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