Why Data Analysts Can’t be Beaten by Artificial Intelligence

There are many jobs in this world that are at risk of being either taken over completely or becoming heavily reliant upon artificial intelligence (AI). However, one role that is pretty safe and always will be is that of an analyst. As a general rule of thumb analysts use measurements taken from the world to try and make good business sense of it all.


Earlier this month Apple paid $200 million for the acquisition of Lattice.io in order to automatically transform unstructured data into structured data. This is a job that would normally be done by analysts. New startup Lapetus uses AI to predict more accurately a person’s life expectancy. So, for those reasons, you may be getting a little nervous if your job is a data analyst, but there really is no need.

As much as we’ve grown to love AI, there will always be some circumstances where a human touch is required. AI isn’t just about robots devoid of any emotion simply making set decisions in very specific scenarios. Today, AI encompasses machine learning much of the time which allows algorithms to become based more upon human logic.


There are two systems of the human brain according to Daniel Kahneman. The first system is automatic whereas the second is the conscious, logical system. If our emotions are being produced by the automatic system our response is fear to things that may harm us and joy to things we like, and AI will be more emotional than rational. It’s been seen quite recently in reinforcement learning where both positive and negative signals are used that AI responses are becoming incredibly similar to the way our own emotional responses would act.

So, AI may still be advancing at an incredible rate, but we’re not quite ready to rid of humans just yet. AI is great at what it does and one of those things is pattern recognition.  But there is a limit to what they can do, and that stops with the algorithm. If something can not be precisely described by an algorithm then how can the AI be expected to learn it?  The final objective of data analysis will always be a human one, regardless of it’s to make decisions or create a product.


The analysis involves so much more than sorting raw data; it’s about understanding what it means to be human and using AI to enhance and achieve our human goals. As written by Kim Scott recently in her new book, Radical Candor, “Your humanity is an asset to your effectiveness, not a liability.” So, rather than AI take over the role of data analysts they will simply become tools of the trade in which to help them deal with the real problems of the world.

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