By Ngwa Bandolo Bobga Cyril, Data Analyst | Data Scientist

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It is true that parts of data analytics are being automated every day like visualization and reporting, and I believe the trend will continue well into the future.

I can understand this statement because many professions such as call center agents are being replaced by Chat bots and aspiring data analysts are afraid that if they start learning data analytics in a few years’ time they too will be out of work.

What is good about AI?

When used on routine and repetitive tasks AI is very efficient.

  • AI can compute way faster than humans
  • AI does not make not make errors
  • AI greatly improves efficiency.

Finally, when we look at latest developments in “Strong” AI such as GANs, we see that machines can really take us to places we never imagined.

Why do I think AI will not kill data analysts but instead create more jobs for them?

A health care analyst, for example, is not required to only possess technical skills like SQL, Excel, R and Python, but most importantly they need to have key skills like health care industry knowledge (domain knowledge), communication, problem solving and critical thinking.

 

1. Domain Knowledge

 
This is the key strength the data analyst brings on the table. Being able to understand the following:

  • What is the context of the data you are working on?
  • What are the short comings of this dataset?
  • Where do I get more data to aggregate to the current dataset?
  • How do I most efficiently do my data mapping?
  • When sending the report, which items should I hide for some audience, but show clearly for other audience?

 

2. Communication

 
Being able to talk with project managers, top management, data engineers and at each level translate the conversion to business insights, is what we humans are best at. We are able to collaborate, network with our team members and inspire them so with synergy we get things done.

 

3. Problem Solving and Critical Thinking

 
Not every problem is the same and will strictly follow the same procedures to be solve. Based on the context, the same problem might be solved differently every other time.

 
Here is what I conclude.

AI will help speed up some repetitive stuffs like visualization, report generation, data cleaning, etc., but the whole aspect of creativity and explaining why we are doing what we are doing will not be automated.

Next, because of faster time-to-insight AI will create, companies will find more value in recruiting data analysts and so will gladly create more job opportunities for skills data analysts who will also help manage the automation.

Please, I would love to know what you think.

Wish you good Data Luck!!!

 
Bio: Ngwa Bandolo Bobga Cyril is a Data Analyst/Data Scientist with more than 10 years experience in analytics, working as Head of Data Analytics and Business Performance for a Telecom Company, Yoomee Mobile (Douala). Check out Ngwa’s YouTube videos, teaching machine learning, data science, and visualisation.

Original. Reposted with permission.

 
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