"without any standards or frameworks"
And presumably without any firm definition either but most likely old-fashioned SQL-based reports, spreadsheets end the like won't be shiny enough to count.
Gartner has turned its annual Hype Cycling gaze upon data management and found that – shock – expectations of Blockchain remain hopelessly inflated. The Hype Cycle chart is an illustration of the analysts' theory that tech follows a depressingly familiar path every time a new shiny appears. It is first lauded by some press, is …
So data ops will be as overhyped as DevOps, which we all realize was always some buzzword-loaded pile of platitudes and already-known advice. Does the same apply to serverless, agile, etc.? If so, you'd probably better inform the people who make sure there's a post about just these things here every week. I know that's not the actual articles, but they're still discussing DevOps as if we don't know that it's not new.
DataOps is exactly what was formerly known as business process management. If you look at my personal ontology, it's filed under Intelligence Engineering. Anything that manipulates data into information gets chucked into that bucket including AI/ML. My favorite bucket as a matter of fact.
Do those have to go through this cycle, or are they just adopted?
Seems to me the ones going through the cycle are the ones that give someone
the opportunity to "assume authority" and charge for teaching how to implement said
idea, which as JoS mentions, is often an old one anyway with a new name or spin.
Which does, of course, generate ad revenue and paid postings in media for those
selling "whatever we can spin that makes you need my services" - and without the
media component, there's no hype at all outside the watercooler...it's quarantined...
DataOps will NOT go through the classic Gartner cycle of "hype" then "disillusionment." It will go straight to the plateau of productivity.
The reason is that DataOps is not new. Let me rephrase. It is a new term for several well-established methodologies being applied to data analytics. You can't argue against lean manufacturing (statistical process control), Agile development, version control, component-based development, automated orchestration, ... DataOps is all these things put together and applied to data operations. If you focus on methodologies that you know will add value, then you can't go wrong.
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