Coming of Age in 2017: A Tale of Open Source & Open Data Science

 

It’s not surprising that as the calendar changed to a new year, there were not just a few articles outlining predictions for 2017 – including what this year’s list of industry buzzwords will likely include. Sexy terms like disruptive technology and augmented reality top the list and I can’t dispute the probability that this is more than a remote possibility. But we must also acknowledge some enterprise technology up-and-comers that are evolving at a pace that might make one believe they might dominate those lists of industry buzzwords sooner rather than later – including “open source” and “Open Data Science”.

It’s not surprising that as the calendar changed to a new year, there were not just a few articles outlining predictions for 2017 – including what this year’s list of industry buzzwords will likely include. Sexy terms like disruptive technology and augmented reality top the list and I can’t dispute the probability that this is more than a remote possibility. But we must also acknowledge some enterprise technology up-and-comers that are evolving at a pace that might make one believe they might dominate those lists of industry buzzwords sooner rather than later –  including “open source” and “Open Data Science”. 

The advancements being made in the data science world are quite literally transforming the industry. 2016 was the year of Python with more downloads of the language than ever, and the recent release of Python 3.6 put it on the enterprise map. These great strides (and others) are making Open Data Science more usable than ever, and it’s time to pay equal attention to the growing importance of data science in the enterprise. But first – what do these terms really mean? What is the difference between the two? Let’s start from the beginning.  

Once upon a time (A.K.A. today), there was a magical community called open source. The magical open source community was made up of villagers who were both incredibly insightful and smart, illuminating and desirable, but the villagers spoke a language only they could understand. And therein lied the problem – whilst the villagers’ language was rich with valuable information, they were unable to connect with other communities, limiting its potential. The villagers clearly needed a knight in shining armour to help them speak one unified language to become a stronger, larger, more interoperable community – and that’s where Open Data Science came to the rescue.  

When Open Data Science valiantly appeared, it created an open source ecosystem, allowing for everyone in communities near and far to have access to the wonders of the languages.The villagers could thrive, coexist, derive value and reap the benefits of each other’s languages using Open Data Science. It brought innovation from every community together, making the latest information readily available to all. It supercharged the communities for faster analysis and results. It propelled the kingdom forward and will look to do so for all enterprises in the future. 

The year of Open Data Science is upon us – and we’re ready to continue telling its story. 


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