Democratizing Data: How Generative AI is Transforming Business Analytics for Everyone

Two DePaul professors demonstrate how artificial intelligence is making powerful data analysis accessible to business professionals without coding experience

The promise of data-driven decision making has long captivated business leaders, but for many, the technical barriers seemed insurmountable. Complex coding languages, statistical models, and specialized software created a divide between those who could harness data’s power and those who simply had to trust others’ interpretations. That divide, according to two DePaul University professors, is rapidly disappearing.

At their July 2025 presentation, Assistant Professors Sina Ansari and Khadija Ali Vakeel from DePaul’s Driehaus College of Business shared compelling demonstrations of how generative artificial intelligence is revolutionizing business analytics. Their presentation revealed a fundamental shift in how we approach data analysis—one that prioritizes business insight over technical expertise.

Redefining the Analytics Landscape

Ansari and Vakeel began by establishing a crucial distinction that frames this transformation. While AI and data science focus broadly on replicating human intelligence through machine learning and deep learning tools, business analytics serves a more targeted purpose: using quantitative methods to uncover actionable information in big data that drives competitive advantage.

“The difference lies in the scope and application of domain knowledge,” explained Ansari, who co-directs DePaul’s Master of Business Analytics program. “Business analytics combines technology and mathematics with deep understanding of business contexts to enable effective decision-making and problem-solving.”

This distinction becomes particularly important when we consider the evolution of AI capabilities. Ansari traced this progression from early statistical machine learning models that required extensive technical expertise, through foundation models that began to generalize across tasks, to today’s generative AI systems that can produce novel outputs ranging from text to complex data visualizations.

The transformation represents more than just technological advancement—it signals a fundamental shift in who can participate in data analysis. “Prior to generative AI, analysts needed primarily technical skills,” Ansari noted. “Now, with these tools handling the technical complexity, domain knowledge has become the critical differentiator.”

From Coding Barriers to Conversational Analytics

The presentation’s live demonstrations illustrated this shift dramatically. Vakeel showed how business professionals can now accomplish sophisticated analytics tasks simply by describing their goals in plain English. Using ChatGPT, she generated R code for complex cluster analysis—a technique typically requiring significant programming knowledge—by simply asking the system to “write R code for K-means clustering with four clusters.”

“You don’t have to be a coder to do analytics,” Vakeel emphasized. “Large language models can handle the technical implementation while you focus on understanding what outputs you need and what they mean for your business decisions.”

The demonstrations extended beyond basic code generation. Using NotebookLM, the professors showed how written reports could be automatically transformed into professional podcasts, complete with realistic dialogue between virtual hosts discussing key findings. This capability transforms static business documents into engaging, accessible content that can reach broader audiences within organizations.

Perhaps most compelling was the demonstration of sentiment analysis using natural language prompts. Rather than wrestling with complex text mining algorithms, business professionals can now analyze customer feedback, news articles, or social media content by simply asking AI tools to “identify positive and negative sentiment” in their data.

Practical Applications for Chicagoland Businesses

These capabilities have immediate relevance for the diverse business community throughout the Chicago area. Consider a local restaurant chain wanting to understand customer reviews across multiple locations. Previously, this analysis might require hiring data scientists or consultants. Now, managers can upload review data to tools like Julius AI and ask for sentiment analysis, trend identification, or even predictive insights about customer satisfaction patterns.

Similarly, manufacturing companies in the region can use these tools to analyze supply chain data, identify operational bottlenecks, or predict maintenance needs—all through conversational interfaces that don’t require specialized technical training. The democratization of these capabilities means that insights previously available only to large corporations with dedicated analytics teams are now accessible to businesses of all sizes.

The implications extend to professional services firms, healthcare organizations, educational institutions, and nonprofit organizations throughout Chicagoland. Each can leverage these tools to better understand their stakeholders, optimize operations, and make more informed strategic decisions.

Strategic Considerations and Limitations

While enthusiastic about AI’s potential, Ansari and Vakeel emphasized the importance of understanding these tools’ limitations. They highlighted three critical considerations for business leaders.

• First, AI-generated outputs aren’t perfect. The systems can produce inaccurate or nonsensical results, making it essential for users to understand underlying concepts well enough to evaluate outputs critically. “You still need to learn the background topics and concepts to detect when something doesn’t make sense,” Ansari explained.
• Second, many AI tools exhibit non-deterministic behavior, potentially producing different results when given identical inputs. This variability requires businesses to acknowledge uncertainty and develop processes that account for this unpredictability.
• Third, data privacy and security concerns remain paramount, particularly when working with sensitive business information. The professors strongly recommended using enterprise versions of AI tools that provide better data protection, especially for confidential company data.

A Modular Approach to AI Integration

Rather than betting entirely on a single AI platform, the DePaul professors advocate for a modular approach to AI integration. This strategy involves identifying specific business tasks and selecting the most appropriate AI tools for each function, whether that’s ChatGPT for code generation, NotebookLM for content creation, or specialized platforms for sentiment analysis.

This modularity provides several advantages. It allows organizations to optimize costs by using the most efficient tool for each task, reduces dependency on any single vendor, and enables quick adaptation as new and improved AI capabilities emerge. For business leaders navigating the rapidly evolving AI landscape, this approach offers both flexibility and risk mitigation.

The Future of Business Decision-Making

The transformation these professors demonstrate suggests we’re witnessing a fundamental shift in how businesses approach data analysis. The traditional model that separated technical specialists from business decision-makers is giving way to a more integrated approach where domain experts can directly engage with data using AI assistance.

This change has profound implications for workforce development and organizational structure. Rather than replacing analysts, AI tools are augmenting human capabilities and enabling more professionals to participate meaningfully in data-driven decision making. The competitive advantage increasingly belongs to organizations that can effectively combine deep business knowledge with AI-powered analytical capabilities.

For the Chicago business community, this presents both an opportunity and an imperative. Organizations that embrace these tools and develop capabilities to use them effectively will gain significant advantages in understanding customers, optimizing operations, and responding to market changes. Those that resist or delay adoption risk falling behind competitors who leverage AI to make faster, more informed decisions.

Building Tomorrow’s Analytical Capabilities

As Vakeel noted in her closing remarks, “People who know AI and know how to use AI for their work might benefit a lot more—salary-wise, work-wise, work-life balance-wise—compared to people who do not get on this bandwagon of AI.”

This observation reflects broader trends in the job market, where AI literacy is becoming as fundamental as digital literacy was in previous decades. The lesson from history is instructive: technological revolutions don’t eliminate jobs so much as transform them, creating new opportunities for those who adapt while leaving behind those who don’t.

The DePaul professors’ presentation ultimately delivered a message of empowerment. The sophisticated analytical capabilities that once required years of specialized training are increasingly accessible to anyone willing to learn how to communicate effectively with AI systems. The barrier to entry for data-driven decision making has never been lower, but the potential for competitive advantage has never been higher.

For business leaders throughout Chicagoland and beyond, the question isn’t whether AI will transform how we work with data—it’s whether we’ll be active participants in that transformation or passive observers of competitors who embrace these powerful new capabilities.