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Key Takeaways: Pivotal’s Top 10 2015 Predictions

On Tuesday 12/16, I attended Pivotal’s Top 10 Data Science Predictions in 2015 webinar.

The webcast was ran by leaders from the Pivotal Data Science  team – Annika Jimenez, Kaushik Das and Hulya Farinas – who shared their insights on the key Data Science industry trends for the coming year. The webcast came off as a bit scripted, but one could tell that these three individuals have a passion for Data Science discipline and it’s future.

In this post, I’d like to take a few points from their discussion and share some of my key takeaways in regards to their predictions:

Point #3The pressure to make machine learning more accessible will drive further tool evolution- Come 2015, the team predicts that the shortage of deep analytical skills will result in an increased focus on making machine learning available for practitioners besides Data Scientists. That is a big idea. The tools utilized in Data Science today require a steep learning curve, and for someone like myself who doesn’t exactly have an advanced degree in mathematics, will tell you that sometimes learning these tools can be exhausting. Yes, it comes with the territory and I enjoy being challenged by something new, but utilizing less brainpower on  machine learning portions allows for more time to develop the data’s narrative.

Point #4 : 2015 will be the year of headlines of Data Science being unethically used &  Throughout the discussion of point #4, the team had a cute little saying: “with Big Data powers come with Big Data responsibility”, which is true for any aspect of analytics. The point here is that people now have access to information beyond our wildest dreams and with the right training, we can do whatever we’d like with it. There are already stories today of Big Data being used against individuals, which is concerning. We live in a connected, always sharing world (whether we are aware of it or not). Luckily, a non-profit professional group the Data Science Association published a Code of Conduct (link to that is at the end of the post) which many organizations are adopting as their policy. It’s comforting to know that the ethics of this discipline has been nipped in the backside early, for the most part.

Point #8: Enterprises will start exploiting under-used video and image data- Here is a point that I felt should have went hand in hand with point #4, but it seemed to be a completely different topic. I believe video and image data is much more dangerous than any text. People have pictures and videos out there that they aren’t too proud of and stating that enterprises will be exploiting these forms of data seems terrifying. I’m not suggesting that corporations are shady entities mining through images and video it’s employees to to slander them, they certainly aren’t. In my opinion, there is just too much power in a picture. If there are algorithms in the works today (and we all know there already are) which can mine through images and videos, what is being done during development of these tools/algorithms to discourage misuse?

Overall, Pivotal’s Top 10 Data Science Predictions in 2015 webinar was fantastic. It left me questioning sides of Data Science that generally don’t come to mind as often as they should.

Link to the slides: http://www.slideshare.net/Pivotal/data-science-predictions2015

Data Science Association Code of Conduct: http://www.datascienceassn.org/code-of-conduct.html

A.P. Dutra

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Tags: Analytics, Data, Pivotal, Science

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