Role of AI in Data Analytics

Introduction:

The rapid evolutionary growth of artificial intelligence (AI) is remarkable. Since the start of ChatGPT in November 2022, we've witnessed an explosion of AI applications across various sectors. Recently, DeepSeek has emerged as a significant player, offering innovative AI solutions at a much lower cost than rival existing models. The field of AI is continually advancing, with new technologies enhancing its applicability.

AI's influence permeates every aspect of our lives (from helping students write essays to giving us recipe ideas for our next meal), and its integration into data analytics is particularly noteworthy. Tools such as Julius AI and DataGPT have positioned themselves as "your personal AI data analyst" streamlining the data analysis processes. 

In this blog, I aim to share my perspective on the role of AI in data analytics, exploring its applications, benefits, and drawbacks and addressing the question: Will data analysts be replaced by AI?

How is AI being used in Data Analytics?

Before we can evaluate AI in Data Analytics it's first essential to understand how this tool is applied. How is AI making our lives easier as Data analysts?

• Data Cleaning and Preparation-

AI aids in automating the tedious tasks of data cleaning and preparation, such as handling missing values, correcting errors, and standardizing formats. This automation accelerates the process and ensures higher data quality, leading to more reliable analysis. Newer AI tools like OpenRefine and DataRobot Paxata are made solely based on cleaning large datasets and help make the initial process of working with large data sets easier.

• Debugging and corrective assistance-

The widespread adoption of AI tools like ChatGPT and GitHub Copilot has significantly impacted the way developers approach coding challenges. These tools assist programmers in debugging and rewriting code segments, offering instant solutions that were traditionally sought through community forums. Consequently, platforms like Stack Overflow have experienced a decline in user engagement. Data indicates that the volume of questions asked on Stack Overflow began to decrease rapidly following the release of ChatGPT in November 2022, with this downward trend continuing.

• Data Visualization-

As we move to the visualization stage of the data analysis cycle, we can see opportunities for AI implementation here as well.

Tools such as Tableau AI can automatically generate charts and reports from raw data, updating them in real time. This makes it particularly useful for recurring tasks like monthly sales reports or performance dashboards.

For non-technical users, no-code automation tools enable the creation and updating of visualizations without the need for programming expertise, democratizing data analysis across organizations. Users can now ask specifically what points of data to visualize or recommend the what graph to use for their dashboards

• Enhanced Decision-Making-

By providing deeper insights and more accurate predictions, AI empowers data analysts to make more informed decisions. This enhancement leads to improved strategies and outcomes across various business functions.

By automating data collection, analysis, and reporting, AI enables decision-makers to access relevant information quickly and easily, eliminating the need for manual data gathering and allowing teams to focus on higher-value tasks. Making this process extremely streamlined leads to a more informed and timely decision, ultimately improving strategies and outcomes across various business functions.

Challenges and Ethical Considerations

While AI tools significantly enhance various steps in the data analytics lifecycle, it’s crucial to address the challenges and ethical concerns that come with AI.

• Accountability and transparency-

AI models make it difficult to understand how they arrive at specific decisions. Recent updates from ChatGPT actively show the sources the tool used to produce its output, but this isn't enough. 

This significantly hinders accountability for one's work. Data analysts are responsible for making critical decisions that affect businesses. Promoting transparency in AI processes and establishing clear accountability frameworks are essential to maintaining trust and responsibility.

• Algorithm Bias-

Algorithmic bias refers to the systematic discrimination that can occur when AI decision-making is influenced by prejudiced or biased data. AI models can inadvertently perpetuate or further amplify existing biases present in the dataset they are given to work with, leading to unfair outcomes. For example, if historical data contains biases, AI systems may learn and replicate these patterns, resulting in discriminatory practices without considering the external domain.

• Imperfect-

Even in 2025, LLMs and AI tools are still susceptible to making inaccurate claims because they are hardwired to do so. Tools like ChatGPT are trained to always provide results and are encouraged to generate output, even if the result is not accurate. This phenomenon, known as ‘Data Hallucination,’ can be caused by factors including, but not limited to, a lack of data, overfitted datasets, and lack of data documentation on domain knowledge.

Keeping this in mind, we can say that AI tools tend to work at their best abilities when given the “Perfect Data”. This refers to a dataset which has no errors, missing values, or outliers, includes proper documentation (relevant to the domain), and is properly normalized and in an accessible format. However, as someone who has worked with many real-life datasets. I can attest that perfect data rarely exists. Most datasets require significant cleaning and preparation, reminding us not to take AI-generated results at face value.

Will Data Analyst be replaced?

So when it comes to answering the important question, Is it even worth working on that half-finished SQL project or that Tableau course you just started? What's the point if AI is going to replace us all and is doing our jobs faster and better? Even if it isn't perfect now, it could be in the next 5 years.

To answer this, I like to use a very nice anecdote that I heard on Avery Smith’s Data Analytics podcast. When Microsoft Excel was introduced in 1985, there was widespread talk about the "Death of the Accountant." People feared that this new wizardry technology would make accountants redundant due to its user-friendly interface and ability to perform financial calculations. But did that happen? Of course not!

Today, accounting remains a thriving profession, with students pursuing degrees to meet the growing demand. Accountants didn't become obsolete after Excel's introduction; instead, they embraced it as a tool to make their work more efficient. Excel automated many manual tasks that required long calculations and allowed accountants the time to provide meaningful insights to their clients. Making the entire process shorter and lacking errors.

Similarly, we shouldn't worry about AI replacing us or any other profession. We should instead view AI as a tool to help us make better and more efficient decisions, leading to improved business outcomes. AI can assist in data cleaning and preliminary analysis, but human expertise is still crucial for interpreting results and making strategic decisions. 

Conclusion:

AI has made giant leaps in innovation and shows no signs of slowing down. Its integration into every facet of our daily lives has brought unprecedented convenience and efficiency. However, as with any technology, there is a flip side—we must remember that AI is not perfect and should not be blindly trusted, as it is ultimately a product of human ingenuity, and like everything we create, it has its flaws.

Ultimately, NO AI will not be replacing us anytime soon but we should take this time to learn how to harness its true potential just like the accountants shifted from writing and calculating in their “physical books” to working on an Excel sheet.

*PS: This blog was made with the help of ChatGPT