ChatGPT in Data Analysis
- Karolina Dyrla-Mularczyk
- Feb 19
- 3 min read
Updated: 4 days ago
In the era of artificial intelligence, more and more companies and scientific institutions are using modern tools, as exemplified by the use of ChatGPT in data analysis. But can AI really replace experienced analysts? Here are the key limitations of this approach and the reasons why the role of experts remains irreplaceable.
ChatGPT in Data Analysis - limitations
Lack of Business and Scientific Context – ChatGPT does not possess specialized knowledge of your industry, which may lead to misinterpretation of data.
Generation of Incorrect Results – AI can create responses that sound correct but… are actually wrong!
Lack of Critical Thinking – The AI model cannot assess the reliability of results in the way an experienced analyst would.
Limited Understanding of Statistical Methods – ChatGPT may suggest an analytical model, but it does not always choose the right one for the specific problem.
Issues with Data Quality – It will not detect subtle errors in datasets, such as inconsistent values or missing information.
Lack of Ability to Explore and Interpret Trends – ChatGPT can perform calculations, but it cannot uncover hidden relationships or trends that require experience and analytical intuition.
Inefficiency in Analyzing Large Datasets – The AI model has limitations in processing and analyzing large amounts of data in real time.
Problematic Decision-Making – ChatGPT can provide data, but it will not make strategic decisions that require business experience.
No Accountability for Results – In case of errors, AI does not bear any consequences, whereas humans must take responsibility for their analyses.
Risk of Misinterpretation by Non-Experts – People without analytical knowledge may blindly accept incorrect AI-generated answers as true.
Data Security Risks – Using AI for company data analysis can pose privacy and security threats. Sensitive information, such as financial data, business strategies, or customer details, may be exposed to unauthorized access or misuse, especially when processed in the cloud or by external AI systems. Therefore, implementing proper security measures and ensuring compliance with data regulations is crucial.
The last three points are, for me personally, the biggest deal-breakers.
If AI generates incorrect data and someone makes a decision based on it, the consequences can be serious - ranging from financial losses to regulatory violations (e.g., GDPR). The risk is even greater when company data is involved, especially if it is confidential.
Advantages of Using ChatGPT in Data Analysis
Speed and Convenience – Get answers to analytical questions quickly, especially when errors occur in your code.
Coding Assistance – AI can help with writing scripts, optimizing queries, and debugging errors.
Inspiration and Suggestions – Provides alternative approaches to analysis and suggests relevant statistical models.
Easy Access – No complex setup or advanced technical knowledge required, making it accessible to a broader range of users.
Why Is the Role of an Expert Essential?
Using ChatGPT without analytical knowledge can lead to incorrect business decisions. Experts are crucial to:
Verify the accuracy of results and assess their real-world validity.
Select the appropriate analytical methods tailored to specific problems.
Interpret results in a way that adds value to business or scientific research.
Manage data by cleaning, transforming, and structuring it.
ChatGPT is a powerful tool for supporting data analysis, but in the hands of an untrained user, it can lead to incorrect conclusions. AI is an assistant, not a replacement for human expertise, experience, and intuition. To avoid errors, collaboration between AI and data analysis experts is essential.
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