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I agree with you. IBM is late to the show and far behind in terms of Data Scientists using their tools and technologies. I myself ( statistician and data scient
by wajdix 7y ago
I agree with you. IBM is late to the show and far behind in terms of Data Scientists using their tools and technologies. I myself ( statistician and data scientist) used IBM products for a short period of time in the context of a PoC. my experience is that, using python/R is much better in the long run than using proprietary software that costs 100k $/year in licencee fees, without forgetting the training /setup cost that comes with any migration to IBM suite.
- Bartweiss 7y ago> using python/R is much better in the long run than using proprietary software that costs 100k $/year in license fees It does seem like IBM has been caught out by freely available alternatives which are simply better at most scales. It reminds me of what happened to companies selling compilers or corporate VOIP solutions; they didn't just fail to keep up with the market, their entire market was supplanted by open-source solutions and single features in much larger offerings. IBM has made a fairly productive effort to avoid that, I think, via their consulting/outsourcing divisions and value-added things like cloud solutions. As a result, their data science products are still appealing in terms of ecosystem integration and strong support options for less-technical buyers. But that doesn't seem like a wonderful position to be in long term, since it puts them in competition with AWS/GCP on one end and free tools on the other.