Business Intelligence Evolution and Trends

Introduction to Business Intelligence (BI)

• Early 90s: Start of business intelligence as a concept.
• 2000s: BI became synonymous with reporting and dashboarding.
• Books: Author wrote a book on dashboards in 2005 and a second edition in 2010.

Recent Developments in BI

• ThoughtSpot Campaign: "Dashboards are dead" created controversy.
• Critique of BI:
  • Reactive: Only looks at past results.
  • Generalized: Provides top-level summaries, missing details.
  • Manual: Difficult to assemble and interpret.
  • Descriptive: Lacks analytical and predictive capabilities.
  • Inflexible: Metrics and thresholds need predefined definitions.
  • Difficult for casual users.
  • "Last Mile Problem": Difficulty in turning insights into actionable outcomes.

BI Market Trends

• Vendor Challenges:
  • Need to justify expenditures on BI tools.
  • Questions about the value derived from reporting tools.
• Product Development Approaches:
  1. Broad Approach: Decision intelligence platforms integrating various analytics capabilities.
  2. Specialized Tools: Focusing on enhancing productivity for data analysts.

User Categories in BI

• Data Consumers:
  • Majority of users (executives, frontline workers, customers).
  • Often unaware they are using BI (embedded analytics).
• Data Explorers:
  • Want to engage more deeply with data (30% of employees).
  • Prefer "silver service"—data served up to them without heavy lifting.
• Power Users: Data analysts and scientists (2% of employees).
  • Require self-service tools for data manipulation, analysis, and visualization.

BI Adoption Rates

• Survey Findings:
  • 20-25% of employees actively use BI tools.
  • The figure has not changed significantly over the last 10-15 years.

Innovation in Tools for Data Analysts

• Business Monitoring:
  • AI-driven systems monitoring business metrics.
  • Helps data analysts by surfacing significant trends and anomalies.
• Analytics Workbench:
  • All-in-one tools supporting end-to-end workflow for data analysts.
• Collaborative Intelligence:
  • Allows data analysts to share work and collaborate, improving productivity.

Evolution of Intelligence in BI

• Three Eras of Intelligence:
  1. IT-Driven Era (1990-2005): Focus on consolidating data and ensuring trustworthiness.
  2. Self-Service Era (2005-2020): Need for governance of self-service analytics.
  3. Model-Driven Era (Present): Incorporation of AI and machine learning.
• Augmented Intelligence:
  • Utilizes AI to improve human decision-making processes.

Role of ChatGPT in BI

• Potential Impact:
  • Could serve as an interface for analytics.
  • Still limited to text queries, no direct DBMS connections.
• Current Limitations:
  • Not suited for complex data connections or visual displays yet.

Future Directions in BI Tools

• Decision Intelligence:
  • Tools incorporating reporting, dashboarding, predictive analytics, and AI.
  • Vendors are exploring ways to integrate more functionalities into their BI offerings.

Conclusion

• BI's Role:
  • Foundation for data-driven decision-making.
  • Need for continuous improvement in tools and governance to close the gap between insights and actions.