AutoML on the Gartner Hype Cycle — The future of Analytics?
I was having lunch and my boss came and sat down with me for a chat. We got into the depths of the future of Data Science, where we stand and what the future of analytics is. Everyone has an opinion, here is mine:
What is Data Science and why is it so tough to find “Data Scientists”?
Data Scientists are subject matter experts. In what fields? Check it out:
I can’t help but be confused. I’m coming up to the one-year mark as a data scientist, and I am still not sure what it is exactly we do. It so happens that we try to find potential problems that exist and try to solve everything.
Data scientists hold the key to unveiling better solutions to old problems
(Source: Gartner)
Is all of Data Science jargons? We have tech behemoths so advanced that it is difficult to comprehend, then, on the other side, we have “data scientists” running a bunch of regression algorithms. Is everyone really on the same page? A decade ago, if you knew the word Data Science and worked towards it, you were ahead of the curve. With thousands of courses dedicated to helping you master Data Science, you have to know the basics of Data Science just to be associated with this new buzz word to keep your profile relevant. The future of Data Science is picking out a domain and deep-diving.
What do I think Data Science is?
Data Science is definitely not following the same process. We have automated steps, algorithms and process flow to deal with specified business problems. We still notice that trying to re-implement steps is not the best way ahead.
Simply put, Data Science is curiosity.
There is an evident gap in the market right now. Everyone wants to have all the buzz words of the decade on their companies website or on their profile. It’s the same words HRs are using to scour through LinkedIn profiles and resumes. Mine is no different, check it out. I’ve been working on one of the leading NLP machine learning toolkits, check out my series on it here! Right now there is more focus on the Mathematics and Technology aspect of Data Science.
The future of Data Science is Business Expertise and Design Thinking.
The market has a way of bridging gaps. A couple of years, the software by Google, Amazon or Microsoft will either be production-ready out of their current Alpha and Beta stages of development, or one of them will buy one of the many start-ups making great strides in these areas. Anyways, let’s leave the work to those who understand it.
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The reason I said that is because it is possible that a graduate fresh out of college will just need to learn a single tool that has an intuitive GUI (Graphical User interface). Data Science in the future will not have huge teams working towards solutions, instead, it will have SMEs (Subject-Matter Experts) deriving insights from the drag and drop tools that freshers learn to use.
This is AutoML (Automated Machine-Learning), possibly the future.
Datasets, algorithms to clean data, improve accuracy, validate and improve models will be as simple as elements that we drag and drop.
Companies are willing to invest efforts in Data Science as they see it as a part of future investment. The hurdle comes wherein the purseholder or the stakeholder is not able to justify the quantifiable outcome for the effort (yes, money) put in. Data Scientists are a rare breed. Real Data Scientists at least. They do not seem to stick to roles and keep moving around. The reason is for you to answer, as an organization or a project, what can you do to give a great environment that cultivates and leverages curiosity. That’s for now.
Current situation: This is not a joke, this is the reality.
My vision for the future is that AutoML will help us do more things, faster. The way we currently moving forward does not seem to be sustainable. I believe Data Science has the ability to change the world, it shouldn’t be a buzzword that just faded away. (No offence, blockchain ;)
Anyways, it was great writing this! This was my point of view, chat me up down if you had a great read and have any thoughts on the same! Cheers!
I had a great time writing up this article. I hope you learned something new today! Find me on Medium Anish Mahapatra. Do hit the clap button if you would like to encourage a 22-year-old writer :)
