The Internet lit up the day ChatGPT was launched. Almost everyone has been connected via every social media platform to rate how smart they are. Undoubtedly, it captured people’s imaginations and made their mouths sink in ghostly amazement.
Like jeez, he didn’t just write computer programs, poems, articles, and everything in between and extended; He also wrote them with such subtlety, clarity, and relativity that could be attributed to a brilliantly intelligent human being. He even got a 1020 on the SAT test. How incredible!
I remember my friend, a physicist, who came back from his office and wouldn’t stop screaming in awe of what people were doing with ChatGPT on Twitter. Then, he began to speculate what such a display of intelligence by AI would mean for human labor from now on. “You don’t understand; this thing can do everything, boy!” “A lot of people are going to lose their jobs!” he said. He was sure that humans would lose some of the jobs they did. However, I expected him to tell me what kind of jobs would be lost to AI in general.
Will artificial intelligence replace data analysts?
I didn’t care about figs until he told me he’d read somewhere that artificial intelligence would replace data analysts. Immediately, I invested in checking out how to do it. Therefore, I engaged ChatGPT to see what qualities of human intelligence it can display now, and possibly in the future.
First, I started with the domain-specific questions, which the app did credibly–very well. Then I moved on to the coding tasks, and ChatGPT performed great within the specificity of the tasks.
Finally, I moved on to a more data-centric task, and guess what? I didn’t draw as my friend drew as if I would.
What is the current role of humans in data analytics?
Maybe I was expecting too much. When I described what I wanted to do with the algorithms, the app provided solutions in a way that made me productive, particularly in discovering methods that speed data hassles.
However, it cannot on its own carry out all data analysis operations, which according to Google Analytics are asking, preparing, processing, analyzing, sharing and acting.
Despite the advances in technology and the availability of tools and systems that can perform some data analysis tasks, humans play an important role in data analytics.
For example, while machines and algorithms can quickly and accurately process and analyze large amounts of data, they are unable to fully replace the ability of humans to understand and interpret the results of data analysis.
Humans ask the right questions
The role humans play in data analytics is critical at this time because humans bring a level of context, insight, and judgment to data analysis that is essential to making informed decisions based on data.
First, humans can ask the right questions, identify patterns and trends, and draw meaningful conclusions from the data. They can also communicate the results of the analysis in a way that is understandable and actionable to decision makers.
Furthermore, humans are responsible for defining the goals and objectives of the analysis, selecting and preparing data for analysis, as well as designing and implementing the analysis plan.
The human touch is also responsible for ensuring that data is collected and managed ethically and responsibly – and that potential data biases and limitations are accounted for.
In general, the role of humans in data analytics is to use their analytical skills, critical thinking, and creativity to extract value and insights from data and use those insights to inform and improve decision-making.
Why can’t AI machines replace humans in data analytics?
While the question is whether AI machines, such as ChatGPT, have replaced humans in important areas such as data analytics, it turns out that they cannot perform tasks such as formulating research questions, sourcing data, or doing analysis altogether.
Although they can provide information and guidance on these topics, the actual execution of these tasks requires the involvement of humans with the necessary skills and resources.
Formulating research questions and designing an analysis plan
For example, formulating research questions and designing an analysis plan requires a deep understanding of the field and the specific problem or question being addressed. It also requires critical thinking and creativity to determine the appropriate data sources and the most appropriate analytical methods to use.
Furthermore, data acquisition and preparation for analysis can be complex and time consuming. Therefore, it takes a variety of skills and tools to access, clean and transform data as needed.
More than that, performing data analysis and interpreting results also requires a combination of technical skills and domain knowledge. It involves applying statistical and computational techniques to data and using critical thinking and judgment to draw meaningful conclusions from results.
What can AI machines help humans do with data analytics?
Proficiency is required of a Data Analyst. Therefore, any skill or combination thereof would ensure that this is definitely desirable. While AI machines cannot apply data analytics concepts to a specific problem – because that requires a deeper understanding and expertise in the field in which the analysis is being done – they can ensure that data analysts are effective.
They may lack the skills and resources to access and work with relevant data. However, they can provide information and guidance on statistical techniques, data visualization, and machine learning algorithms.
AI will make programming and data analytics easier and more complete
AI machines can make the programming needed in data analytics easier, allowing humans to focus their power on interpreting and communicating the results of the analysis. These alone require critical and analytical thinking skills, in addition to domain expertise.
Programming involves using a specific set of instructions and syntax to tell a computer or other device how to perform a task. It requires a deep understanding of the problem being solved and the appropriate algorithms and methods to use. They also value the ability to write and debug code.
As long as humans understand the task to be done, AI machines can provide information and guidance on programming concepts and languages, perform code completion, and check for errors in codes.
How long will humans be able to do data analytics until AI machines take over?
While machines, such as ChatGPT, and algorithms can assist with certain aspects of data analysis tasks, the entire process of formulating research questions, sourcing data, performing analysis, and interpreting results typically requires the active participation and expertise of humans.
At the same time, it is difficult to predict the future development of technology and the extent to which machines and algorithms will be able to assist or automate certain aspects of data analysis tasks.
However, humans will continue to play an important role in the field of data analytics for the foreseeable future, bringing a level of context, insight, and judgment to analysis that is essential to making informed decisions based on data.
to move on
It seems that in the future humans will use more automated tools and systems to perform certain tasks, such as cleaning and preparing data. They will use pre-built machine learning models for predictive modeling. Does this mean, then, that their role will be more limited to data analytics?
One thing is for sure: data is growing in zillions, and for uncovering insights in it, AI tools are golden. It will enable humans to focus their mental power on interpreting and communicating these ideas – giving them meaning and ensuring that they are precisely actionable.
Featured image rights: provided by the author; Thank you!
