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Open-Sourced Advanced Analytics is increasing…

by Alexander Linden  |  December 19, 2014  |  Submit a Comment

Just one week after Gartner’s European Symposium, I attended yet another IT conference in Barcelona, the Strata + Hadoop World, which finally prompted me to post this last blog entry for 2014. What really caught my attention there was the frequency of encounters, where Python and R were mentioned. Now Strata, of course – has some “population bias”, but already many of my previous encounters with end-users (also at the predictive analytics world in Berlin), indicate the same trend:  A lot of innovative data scientists really favor open source components (especially Python and R) in their advanced analytics stack. I hear this a lot, even from the most advanced of our clients… One department head, leading a dozen data scientists at one of the top retailers, gave me the following rationale:

“I would be paying about $5 million just in annual maintenance, if I stuck with vendor xxx … imagine how many gifted data scientists I can buy for that money (?) …  and by the way I did hire them and they all use a combination of R and Python”.

This is an argument very much worth considering. For us Garter this means, we will even more scrutinize all vendors regarding their value-add (e.g. debugging, security layers, model management, and decision management). For vendors, this must mean to better open up their platforms to R and Python and similar platforms. Just offering an “R integration” will not be good enough for 2015, vendors! Try to differentiate yourself a bit better than that.

I am really looking forward to your opinions and comments. Please post them here or send them to me directly via email.

Wishing all a great festive season break…

 

best regards

Alex Linden

 

 

 

 

Category: advanced-analytics  data-science  python  spark  

Alexander Linden
Research Director
2 years at Gartner
29 years IT Industry

Alexander Linden is a Research Director, specializing in advanced analytics, data science, machine learning, predictive and prescriptive capabilities, and big data analytic uses in multiple industries. He also has profound expertise in other areas, such as crowdsourcing/microwork, semantic technologies, innovation management and go-to-market strategies. Read Full Bio




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