Transforming Insights: How Generative AI is Revolutionizing Data Analytics
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This presentation provides an introduction to the use of artificial intelligence in data analytics, outlining the progression from descriptive and diagnostic analytics, which summarize and explain past events, to predictive and prescriptive analytics that forecast future outcomes and suggest actions. It highlights the role of Generative AI in automating data synthesis with GANs and VAEs, accelerating data exploration, and enhancing the entire data analytics pipeline. Key applications are detailed, including data synthesis to improve model training, anomaly detection to identify fraud, and data imputation to handle missing values. The presentation also emphasizes the importance of human-AI collaboration, ethical considerations like privacy preservation through synthetic data, and the need for transparency using techniques like SHAP and LIME. Finally, it looks to the future, focusing on technical trends like explainable GANs, emerging applications in real-time IoT analytics, and research into scalable, privacy-preserving frameworks.
Transforming Insights: How Generative AI is Revolutionizing Data Analytics
- 1. Introduction to artificial intelligence in Data Analytics and how it has being utilized. Generative AI in Data Analytics: Technical Innovations
- 2. Descriptive Analytics Diagnostic Analytics Predictive Analytics Prescriptive Analytics Provides insights into what has happened by summarizing historical data. It helps identify trends, patterns, and anomalies. When to use? when you need to understand past performance or trends. It’s ideal for reporting, monitoring KPIs, and making data-driven summaries of historical events. Uses statistical techniques and machine learning to analyze historical data and predict future outcomes. It identifies patterns and relationships in data to forecast trends, behaviors, or events. When to use? When you need to Investigate sudden drops in sales or user engagement or analyze why a campaign underperformed. Focuses on understanding why something happened by analyzing data patterns and relationships. It uses techniques like drill-down, data mining, and correlation analysis to uncover root causes. When to use? when you need to understand past performance or trends. It’s ideal for reporting, monitoring KPIs, and making data-driven summaries of historical events. Recommends the best course of action by using optimization, simulation, and machine learning. It not only predicts outcomes but also suggests decisions to achieve desired goals. When to use? When you need to Optimize supply chain logistics or Allocate budgets or resources efficiently.
- 3. Automates data synthesis using GANs/VAEs. Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) facilitate automatic data generation Enhances the OSEMN(Obtain, Scrub, Explore, Model, and Interpret) pipeline with generative models. Generative models improve each stage from obtaining to interpreting data. Accelerates exploration via pattern recognition. Generative AI identifies patterns quickly, streamlining data exploration. Optimizes cleaning with probabilistic imputation. Probabilistic methods enhance data cleaning processes, ensuring higher accuracy. Role of Generative AI in Data Analytics
- 4. involves generating new, realistic data to enhance existing datasets. Using generative models like GANs, VAEs, or transformers, synthetic data mimics real-world patterns to improve model training, reduce bias, and handle imbalanced or limited data. Technology: GANs Utilizes a generator and discriminator to mimic data distributions effectively. VAEs Employs an encoder-decoder architecture for probabilistic data generation. Transformers Uses token-based sequence modeling for generating structured data. Application Synthetic datasets are used for training ML models, such as in fraud detection. Data Synthesis and Augmentation
- 5. 1.Principle: Statistical deviation detection using learned distributions. 2.Technical Details: GANs/VAEs model 'normal' data distribution. 3.Anomalies flagged via high reconstruction error or low probability. 4.Implemented models like Isolation Forest or AutoEncoder. 5.Application: Fraud detection, network intrusion detection. Anomaly Detection
- 6. Probabilistic estimation of missing values Estimating missing data using probabilistic methods enhances data integrity. VAEs infer missing data via latent space reconstruction Variational Autoencoders (VAEs) reconstruct data in a latent space to fill in gaps. GANs generate plausible values based on data patterns Generative Adversarial Networks (GANs) create realistic data values by learning from existing patterns. Probabilistic NNs estimate uncertainty Probabilistic Neural Networks, such as Bayesian NNs, provide estimates of uncertainty in predictions. Custom GenAI pipelines for advanced imputation Building tailored GenAI pipelines allows for sophisticated data imputation techniques. Data Imputation
- 7. Augmented intelligence via AI-driven tools. Enhances decision-making and operational efficiency. Integration of GenAI with visualization APIs like Plotly. Facilitates advanced data visualization capabilities. Real-time data processing with Flask-based apps. Supports dynamic data interaction and analysis. RESTful APIs for seamless AI-model integration. Enables efficient communication between applications and AI models. Scalable, interactive analytics dashboards for IT teams. Human-AI Collaboration
- 8. Principle: Responsible AI with differential privacy Focus on creating AI systems that prioritize privacy and ethical data usage. Utilizing synthetic data to protect personally identifiable information (PII). Synthetic data generation ensures no real PII leakage Ethical AI and Privacy Preservation
- 9. • Interpretable AI models foster trust and validation. ⚬ Utilizing models that are transparent helps stakeholders understand AI decisions. • SHAP/LIME techniques for feature importance. ⚬ These tools help identify which features influence prediction outcomes. • Attention mechanisms in transformers enhance explainability. ⚬ They provide insights into which parts of the input data are most relevant for predictions. • Research in XAI includes explainable GANs. ⚬ This research aims to create transparent data generation processes. • Tools like SHAP or Captum support model interpretability. ⚬ These tools assist in making complex models understandable. Explainability and Transparency
- 10. Distributed training with optimized compute