Generative AI will not replace data analyst jobs. Artificial intelligence cannot replace humans in many areas, especially those that require human empathy and insight.
AI can process large amounts of data and provide quantitative analysis. It cannot understand the intricacies of human behavior, cultural nuances, or the complexity of human motivations and desires as human analysts can.
Data analysis may seem like a technical role, but the work is nuanced; it involves much more than calculations. Successful data analysis requires an understanding of the human elements behind the data, whether analyze customer behavior or detect fraudulent activity. A human analyst’s ability to empathize and understand the motivations, fears, ambitions, and interests of others can lead to compelling insights that go beyond what is immediately apparent in raw data . Obtaining information requires an element of human judgment and understanding that artificial intelligence is currently lacking.
Generative AI tools such as ChatGPT and Gemini can simulate text generation to a human-like standard, and they have the potential to automate some of the tasks currently performed by data analysts. But generative AI also has limitations: it cannot understand the full context of the data. Data analysts still need to interpret generative AI results and make data-driven decisions.
Limits of generative AI
Generative AI cannot do the nuanced work of analysts. Data analysis requires the synthesis of visual, numerical, and tacit knowledge, which analysts cannot convey through text alone. The training data used by generative AI models limits the text they can generate. GenAI cannot analyze raw data or produce original visualizationsand all the information it provides comes from linguistic patterns in the training data.
Another concern with generative AI models is accuracy. Without human oversight, AI-produced texts can contain logical gaps, biased perspectives, and factual errors that they inherit from the training data. Accuracy depends on quality and diversity of training data; Biased or inaccurate training data makes datasets biased or inaccurate.
Artificial intelligence models struggle to keep pace with the real world: training or retraining a model requires a lot of computing power, time and money. As the world evolves, the AI model falls behind until it is retrained. Depending on the last time a model was recycled, this could take several months behind on dataleading to a potentially significant knowledge gap.
Generative AI models also lack the critical thinking skills and insight to question the validity or relevance of their source material, which is an essential skill for a data analyst. An essential part of data literacy is checking data quality and identifying potential biases.
Due to its limitations, GenAI does not replace human analysts. Rather, models are a tool for analysts to generate text, identify patterns, and explore data. Under human oversight, generative AI models can be an asset. Without human engagement, they mostly produce repetitive and formulaic summaries of their existing knowledge.
How Data Analysts Can Use Artificial Intelligence
It’s almost impossible to predict future events, but it’s safe to say that the limitations of AI won’t be resolved for some time. Yet human data scientists and analysts can now use AI as a valuable assistant in their work.
The role of the human analyst is not obsolete.
Generative AI can suggest code to extract, clean and analyze data, helping to automate certain repetitive tasks. It lacks the deep understanding of context, business objectives, and interdependencies necessary for design. complex, scalable and maintainable code architectures. But AI can help an analyst who may need to work in multiple languages or various architectures generate useful code for quick review.
With the right information, AI can also suggest data structures, such as tables, especially for analytical purposes. patterns, such as stars and snowflakes. Although AI can identify patterns in data and suggest tables, the task of defining efficient and effective data structures still requires human intervention. AI often struggles to “get it right” the first time because it doesn’t have the same understanding of data as a human analyst. Human analysts often get it wrong in the first iteration as well, but they start with a richer understanding of the problem. It may be too much work to describe the details needed for the AI program, but the human analyst can take what they know and find other potential use cases.
An interesting use of AI is to recommend analytical methods. Analysts must always validate the suitability of a suggested method for the problem, consider business needs, data constraints, and possibly even budget constraints for compute and storage.
For example, suppose an AI system analyzes customer purchasing data to increase sales. It sifts through massive data sets and identifies a pattern: Customers who buy a laptop often also buy a wireless mouse. Therefore, AI recommends that bundling products into a promotional offer can lead to increased sales.
The human data analyst, drawing on their specific business knowledge and experience, can complement the insights generated by AI. They know that the laptop has a high profit margin and the mouse has a low profit margin. A package deal might increase sales, but might reduce overall profit. They might suggest a change to the AI strategy: instead of a bundle, offer the mouse at a discount, perhaps with a coupon, only after the customer has purchased a laptop. The proposal maintains the laptop’s profitability and overall sales could further increase due to the perceived deal. The human analyst can also provide context about supply chain constraints, seasonal trends, or upcoming marketing campaigns that the AI might not be aware of. With new information, analysts can ask the AI again and see if it has more or similar recommendations.
Will AI replace data analysts?
AI can augment, rather than replace, the role of data analysts. Using AI to automate routine tasks allows analysts to spend more time on strategic work, for example. But AI is not responsible for its own mistakes: the responsibility and blame always lies with humans.
Human judgment – coupled with a healthy dose of skepticism and business acumen – continues to be an indispensable asset that AI cannot replace. Intelligent analysts can use AI as a tool to augment their capabilities, rather than perceiving it as a threat to their role.
Today, AI can automate repetitive tasksprovide insights into large data sets, help write initial reports, write code snippets, and suggest potential avenues for analysis. As AI advances, the industry could expect more sophisticated data analysis assistance. AI can suggest potential data sources, generate effective test data, or drive operational and tactical decisions.
Even though generative AI reduces the number of analysts needed in a given organization, the key role of the human analyst remains. Their knowledge of the specific context, their ability to apply critical thinking and a deep understanding of human needs remain, even with new advances. The role of the human analyst is not obsolete. It is more essential to ensure that their organization harnesses the potential of generative AI effectively and responsibly.
Donald Farmer is a data strategist with over 30 years of experience, including as a product team leader at Microsoft and Qlik. He advises international clients on data, analytics, AI and innovation strategy, with expertise ranging from tech giants to startups. He lives in an experimental forest house near Seattle.
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