ChatGPT’s launch day was a momentous occasion in the world of the internet. Nearly everyone on every social media platform connected to ChatGPT to see how intelligent it was. It was undoubtedly more intelligent than people imagined and left them speechless with a sense of utter stupefaction.
It wrote computer programs, poems and essays, and everything in between. It even scored 1020 on the SAT. It was amazing!
My friend, a physicist was the one who returned from work and couldn’t stop giggling at the people using ChatGPT. He began to speculate about what this show of intelligence by AI would mean for human employment in the future. He said, “You don’t understand, this thing can do all, boy!” He was certain that some jobs would be lost to AI. But, I expected him not to tell me which jobs would be lost to AI.
I didn’t care a bit until he said that he had read somewhere that AI would replace data analysts. Instantly, I became interested in finding out how. ChatGPT was engaged by me to determine what human intelligence it could demonstrate right now and in the future.
I began with domain-specific questions. The application answered them very well. Next, I moved on to coding assignments. ChatGPT performed admirably within the constraints of the assignments.
Finally, I was able to move on to a data-centric assignment. I wasn’t as overwhelmed as my friend thought I would be.
Maybe I was expecting too much. The application was able to help me in ways that were productive. It helped me discover methods that would speed up data wrangling when I described my algorithm.
It could not, however, execute all data analytics processes on its own. According to Google Analytics, these are Ask, Prepare Process, Analyze Share, and Act.
Despite technological advancements and the available tools and systems capable of performing some of the data analysis tasks, human beings still play an important role in data analytics.
Machines and algorithms are capable of processing large amounts of data fast and accurately, but they cannot replace the human ability to interpret and understand the results.Also read: Top 10 IoT Mobile App Development Trends to Expect in 2021
Humans’ role in data analytics is vital at this point because they bring context, insight, and judgment to data analyses that are essential for making informed data-based decisions.
Humans can ask the right questions and identify trends and patterns in data. They also have the ability to draw meaningful conclusions. They can also communicate the results of the analysis in a way that is easy to understand and useful for decision-makers.
Humans are also responsible for the definition of the goals and objectives, the selection and preparation of data, as well as the design and implementation plan.
Human touch is also responsible to ensure data are collected ethically and responsibly and considering potential biases or limitations.
The role of humans in data analysis is to use their critical thinking and analytical skills to extract value from data and use these insights to improve and inform decision-making.
Although it is still unclear whether AI machines like ChatGPT have replaced humans in data analytics critical fields, it has been proven that they are incapable of performing tasks like formulating research questions, sourcing information, or conducting analysis.
While they may be able to provide guidance and information on these subjects, the actual execution of these tasks will require the participation of human beings with the required skills and resources.
To formulate research questions or design an analysis plan, for example, you need to have a good understanding of the problem or question being asked. You will also need creativity and critical thinking to find the best data sources and the most effective analytical approaches.
It can also be time-consuming and complex to source data and prepare it for analysis. It requires many skills and tools to access, clean, and transform the data.
Furthermore, data analysis and interpretation require a combination of technical skills and domain knowledge. This involves using statistical and computational methods to analyze the data, and critical thinking and judgment to draw meaningful conclusions.
Data analysts must be efficient. Any skill, or combination thereof, that can ensure efficiency is highly desirable. Although AI machines can’t apply data analytics to a particular problem, as that requires deeper knowledge and expertise in that domain, they can make sure that data analysts are efficient.
They might not have the resources or the skills to access the data and use it. They can still provide guidance and information on data visualization and statistical techniques.Also read: 11 best ways to Improve Personal Development and Self-Growth and its Benefit on our Life
AI machines can simplify the programming required for data analytics. This allows humans to focus their efforts on understanding and communicating the results. These skills require domain expertise as well as critical and analytical thinking skills.
Programming is the use of a set of instructions and syntax that tells a computer or another machine how to do a task. Programming requires deep knowledge of the problem and the best algorithms and approaches to solve it. It values the ability to debug and write code.
AI machines are able to provide guidance and information on programming concepts and languages, code completion, and error checking, as long as they understand the task at hand.
What will it take for humans to be able to perform data analysis until AI machines take control?
Although algorithms and chatbots can help with some aspects of data analysis tasks, humans are required to actively participate in the process of formulating research questions and sourcing data.
It is hard to predict how technology will develop in the future and to what extent machines and algorithms might be able to assist with data analytics tasks.
Humans will still play an important role in data analytics in the future. They bring context, insight, and judgment to the analysis, which is crucial for making informed data-based decisions.
In the future, humans will likely use automated tools and systems for certain tasks such as data cleansing and preparation. Pre-built machine learning models will be used for predictive modeling. Is this a sign that they will play a smaller role in data analysis?
One thing is certain: Data is growing exponentially, and AI tools are essential to discover the hidden insights within it. These tools will allow humans to focus their brain power on understanding and communicating these insights, giving them meaning and making them actionable.
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