Generative Business Intelligence (GenBI)
GenBI is a new type of business intelligence (BI) that uses generative AI to change the way users interact with data.
GenBI allows users to naturally interact with data, gain insights, and make predictions without writing code or creating complex queries.
How does GenBi work?
GenBI uses advanced technologies such as natural language processing (NLP) and machine learning (ML) to:
Understanding data in natural language
Users can ask questions in everyday language to query and analyze data. It uses large language models (LLMs) such as GPT, which translate natural-language queries into structured database queries.
Generate automated insights
GenBI can automatically identify patterns, trends, and anomalies in data and present them in easy-to-understand reports and visualizations. Various ML algorithms are used for this purpose:
- Clustering algorithms for pattern recognition
- Anomaly detectors based on statistical models
- Time series analyses for trend identification
Create forecasts and simulations
GenBI uses various ML models for predictive analyses:
- Random forests for robust forecasts
- Neural networks for complex relationships
- Gradient boosting for highly accurate forecasts
- Monte Carlo simulations for “what if” scenarios
Integration with existing BI systems
GenBi solutions offer various integration options
- API-based integration with existing BI dashboards
- Direct connect to common data sources
- Middleware solutions for legacy systems
- Real-time processing through stream analytics
Benefits of using Generative Business Intelligence (GenBI)
- Increased accessibility: GenBi democratizes access to data and enables users without technical expertise to gain valuable insights.
- Faster decision making: GenBI speeds up the analysis process and enables companies to react faster to new information.
- Improved data literacy: GenBI promotes data literacy in companies by simplifying interaction with data and providing intuitive insights.
- Increasing efficiency: GenBI automates many tasks in the BI process, which saves time and resources.
GenBi use cases and ROI
Essential Applications for Generative Business Intelligence (GenBI)
Ad hoc analyses: Quick answers to business questions in natural language.
- Example: “What was the turnover in Germany in the last quarter?”
- ROI: 60% time savings when creating ad hoc reports
Data exploration: Intuitive exploration of data sets and identification of patterns.
- Example: “Show me the most important trends in customer behavior over the last 12 months.”
- ROI: 40% more identified business opportunities
Advanced applications for GenBi
Complex multivariate analyses
- Example: “Analyze the correlation between weather data, social media sentiment, and sales figures.”
- ROI: 25% more accurate sales forecasts
Cross-domain optimization
- Example: “Optimize inventory based on supply chain data, customer behavior, and macroeconomic indicators.”
- ROI: 30% reduction in inventory costs while improving availability
Tools and cost comparison
Cost models
Pyramid Analytics
- Enterprise model from 750€/user/year
- Custom pricing for large installations
ThoughtSpot
- Cloud edition from 95€/user/month
- Premium features from 195€/user/month
Power BI
- Pro: €8.40/user/month
- Premium: 16,90€/user/month
- Premium for large companies: individual pricing
Future outlook
Technology trends
- Multimodal analysis: Integrate text, image, and audio
- Automated Machine Learning (AutoML): Self-optimizing models
- Edge computing: Decentralized analytics for IoT data
- Quantum Computing: Preparing for quantum-ready analyses
Vendor roadmaps
- 2024: Focus on improved NLP capabilities and AutoML
- 2025: Integrate multimodal analytics
- 2026: Edge analytics and IoT integration
- 2027+: Quantum-ready features
Challenges
- data quality: GenBi's accuracy and reliability depend on the quality of the underlying data.
- Distortion and ethics: GenBI models may reflect biases in the data and must therefore be carefully examined for ethical aspects.
- Complexity: Implementing and maintaining GenBi systems can be complex and requires specialized expertise.
Conclusion
GenBi is a promising technology that has the potential to revolutionize the way companies use data. By combining generative AI with traditional BI methods, GenBI enables faster, more intuitive and accessible data analysis. The integration of advanced ML models, increasing automation and the focus on usability make GenBi an important tool for data-driven business management of the future.
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