But what does that mean? I’ll let someone smarter than me explain, though (this is from another blog)…
Many organizations struggle with delivering high quality data due to their siloed nature and complex integration needs. Enterprises who adopt a considered and end to end approach to delivering trusted data can pinpoint and deal with their roadblocks, enabling them to deploy high-quality, accurate models fast with a reduced risk. Similar to the streamlined process DevOps delivered, DataOps is what the industry is recognizing as the defacto way to improve upon the process of data collection and governance.
The IBM Global Chief Data Office implemented DataOps capabilities that dramatically enhanced data quality and regulatory compliance. With a 90% reduction in analytics cycle time and $27M in productivity savings, these results prove that we have the best solutions in the market to win.
As of February 2020, IBM officially rebranded our UG&I business (aka Unified Governance & Integration; in which Information Server belonged) to IBM DataOps (Data Operations).
DataOps is trending in the market. According to a recent Nexla survey, 73 percent of companies plan to invest in DataOps.
With the industry shifting focus to this particular area, IBM is in a leading position to own the DataOps conversation. Several vendors speak to the practice, though none have developed the robust capabilities set IBM has in accordance with a broader Data and AI lifecycle strategy (AI Ladder). IBM delivers on the cohesive strategy with:
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Our proven DataOps methodology, and
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Technology and client support through DataOps Center of Excellence.
The practice is focused on enabling collaboration across an organization to drive agility, speed, and new data initiatives at scale. Using the power of AI/ML driven automation, DataOps is designed to solve challenges associated with inefficiencies in accessing, preparing, integrating, protecting, virtualizing and making data available.
BONUS QUESTION: What is DevOps for DataStage?
IBM Brian 