How is Business Intelligence (BI) Evolving in the Age of AI?

The Evolution of Business Intelligence (BI) in the AI Era: How artificial intelligence is reshaping BI tools and strategies for better insights.
Last updated
May 3, 2024

In recent years, Business Intelligence (BI) has undergone significant changes, driven by the rapid advancements in Artificial Intelligence (AI). This evolution has led to new challenges and opportunities for BI practitioners and data analysts. Let's explore the key aspects of this transformation and its implications for the future of BI.

What are the Three Eras of BI?

BI has evolved through three distinct eras: the Enterprise Reporting Era, the Self-Service Era, and the AI Era. Each era is characterized by different priorities, tools, and methodologies, reflecting the changing needs and capabilities of the industry.

  • Enterprise Reporting Era: Focused on accuracy and standard reporting, with tools like Cognos and Business Objects. Data warehouses, such as Oracle and Exadata, were prevalent during this time.
  • Self-Service Era: Emphasized speed and visuals, with tools like Tableau and Power BI. This era saw the rise of data lakehouses, Snowflake, and S3 storage solutions.
  • AI Era: Automates dashboard and visualization building, changing the role of BI professionals and data analysts. This era is marked by the emergence of data mesh, data fabric, and last-mile ETL technologies.

What are the Four General Principles of BI in the Age of AI?

In the AI Era, BI professionals must adapt to new challenges and opportunities. Four general principles guide this adaptation:

  • Return of Data Modeling: Embrace data modeling and borrow concepts from the Enterprise Reporting Era, such as the Kimball Methodology, to help machines make sense of data.
  • Managing Metric Backflow: Monitor the edge of the analytics program and incorporate valuable insights into earlier parts of the data stack.
  • Focusing on Abstraction and Semantics: Ensure consistent and high-quality data for both humans and AI agents by emphasizing abstraction and semantics.
  • Domain-Oriented Management of Data Assets: Develop domain-oriented management strategies to handle the increasing complexity of large organizations, as advocated by experts like Joe Reese and Data Mesh.

What Skills Should BI Practitioners Focus on in the Age of AI?

As the role of BI professionals and data analysts changes, they must acquire new skills and knowledge to stay relevant in the AI Era. Some key areas to focus on include:

  • Semantics: Understanding the meaning and relationships between data elements.
  • Graph Theory: Modeling complex relationships between data points and entities.
  • AI Systems: Familiarity with AI-driven analytics tools and platforms.
  • Last-Mile ETL: Ensuring data accuracy and consistency in the final stages of data processing.
  • Business Knowledge: Deep understanding of the specific domain and industry context.
  • Soft Skills: Communication, collaboration, and problem-solving abilities to work effectively with diverse teams and stakeholders.

How Can Secoda Solutions Support BI in the Age of AI?

Secoda offers a single source of truth for an organization's data by connecting to all data sources, models, pipelines, databases, warehouses, and visualization tools. Powered by AI, Secoda makes it easy for any data or business stakeholder to turn their insights into action, regardless of their technical ability. By addressing the challenges and embracing the opportunities of BI in the AI Era, Secoda helps organizations stay competitive and make data-driven decisions with confidence.

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