How AI Liberates BI Data from Dashboards

Business intelligence (BI) has traditionally been a domain for data and business analysts who create dashboards for executives to make informed decisions. However, the advent of generative AI (GenAI) over the past 18 months has revolutionized this landscape. The widespread adoption of tools like ChatGPT has led to a growing demand for natural language interfaces in every application. But can GenAI truly democratize access to powerful analytic data across an organization?

Organizations often struggle to distribute analytics effectively due to the complexity of traditional dashboards and the limited expertise or time available to many employees for data wrangling. In a conventional BI setup, software licenses for creating dashboards and exploring data visually are typically restricted to a small fraction of the workforce, usually between 3% and 5%. This limited access, coupled with the need for specialized data analytics training, creates a bottleneck that hinders the widespread adoption of data-driven insights.

Moreover, traditional dashboards are largely static and offer limited interactivity, serving only a small portion of the workforce. Frontline roles, in particular, are often excluded from accessing BI data, further exacerbating the issue.

Generative AI offers a potential solution to this lack of data access. Many professionals already use tools like ChatGPT to draft emails, brainstorm marketing campaigns, or create project plans. The integration of generative AI into business applications is becoming increasingly common, with many software vendors announcing new AI functionalities.

However, generative AI alone is not without its flaws. It is notoriously poor at crunching numbers and often hallucinates incorrect facts and figures, making it an unreliable foundation for data-driven decision-making. The key lies in combining AI with BI to address the weaknesses of each technology individually.

By integrating AI with BI, organizations can expand access to data and provide text explanations for data visualizations, answer natural language questions, and suggest SQL queries. This combination can democratize access to data across the organization, from the factory floor to the executive suite. BI can provide the data governance guardrails necessary for reliable intelligence, while AI can enhance the accessibility and usability of BI data.

A semantic layer can further enhance the effectiveness of AI+BI integration. Generative AI models often struggle to differentiate between good and bad information, leading to hallucinations. A semantic layer provides a consistent view of data across the organization, ensuring that everyone uses the same definitions and calculations. This technology allows authorized users to create reports, dashboards, and visualizations without needing to understand the underlying data structures.

In essence, a semantic layer vetted by demanding enterprises and public sector agencies can streamline the process of data access and analysis, making it more efficient and reliable.

The integration of generative AI with BI has the potential to revolutionize the way organizations access and utilize data. By addressing the limitations of traditional BI and leveraging the strengths of AI, organizations can democratize access to data-driven insights and make more informed decisions. The addition of a semantic layer ensures consistency and reliability, further enhancing the effectiveness of this AI+BI synergy.

This transformation is not just about technology; it's about empowering every employee with the tools they need to make data-driven decisions, ultimately leading to better business outcomes.

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