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Data Dynamics: Embracing Generative AI Innovations

“About the Series: As current students, most of us have explored ChatGPT in work or study, or out of curiosity, mostly to facilitate some dirty work to increase productivity. As a CSE Career Captain, ChatGPT really helps a lot with my industry research work, acting like an AI expert across various industries

While traditional AI has focused on detecting patterns, honing analytics, and classifying data, generative AI deals with creating new content. The generated AI makes a significant impact in the space of data analytics, and many of the possibilities of generative AI in data analytics are yet to emerge. Let’s explore the influence of the emerging AI technologies.”

Ruolan Li, MA’24

Check out the fifth installation in this CSE Connect Series about how ChatGPT is influencing the various industries of interest to Brandeis International Business School Students!

AI’s Impact on Data Analytics

An article from Forbes, “How Could Generative AI Impact The Data Analytics Landscape”, introduces the potential areas of AI’s impact on data analytics. Generative AI brings efficiencies by leveraging large language models. It helps data analysts get the data, analyze the data, generate insights, deliver insights, and drive decisions. While the future is exciting, organizations also face various limitations as well as challenges of generative AI, including data security and privacy, bias and ethics, IP risks, accuracy and “black-box” issues.

Data Needs AI

The Data Paradox Artificial Intelligence Needs Data; Data Needs AI” from Forbes indicates the interdependence between data and AI. AI brings out a new generation of enterprise analytics, preparing insights and recommendations that can be delivered directly to decision-makers without requiring an analyst to prepare them in advance. Business intelligence analysts and quantitative professionals will still have important tasks to perform, but many will no longer have to provide support and training to amateur data users.

AI Cannot Replace Data Analysts

We cannot expect the ChatGPT model to produce accurate results according to “I’ve worked as a data analyst at companies like Amazon for 20 years. Using ChatGPT for data analytics is a risky move — AI can’t do the work we do” from Business Insider. The former head of analytics at Amazon points out that using Chat GPT as a replacement for data analysis will accelerate poor decision-making and propagate bad data across corporate networks, given the current state of analytics environments and how GPT works. There’s a real risk of ChatGPT being trusted in organizations.

Generative AI Is Rewriting the Rules across Industries

How generative AI and data are shaping industries of the future” from TechTalks discussed how generative AI reshapes multiple industries. Generative AI impacts not just the technological sphere but also industries like creative arts, photography, graphic design, news, and content production. The potential of generative AI was given by its data-processing capabilities of big data analytics. For businesses, generative AI could democratize access to data insights, making every employee a potential analyst.


As generative AI continues to evolve and reshape industries across the board, its impact on data analytics is becoming increasingly evident. From streamlining processes to unlocking new insights and democratizing access to data, the potential of generative AI in data analytics is immense. With continued exploration and innovation, the possibilities of generative AI in data analytics are truly limitless, promising to revolutionize how we understand, interpret, and leverage data in the years to come.

Written by Ruolan Li, MA’24, and run through ChatGPT for grammar and spelling, this is the fifth installation in the CSE Connect Series about how ChatGPT is influencing the various industries of interest to Brandeis International Business School Students.

By Ruolan Li
Ruolan Li CSE Career Captain