SlideShare a Scribd company logo
Data Science and Artificial Intelligence
towards improving Data
Somu Vadali
Head - C&D Labs
Chief Data and Product Officer @ Future Group
The Trinity of ML/AI
 Consolidation
 Cleansing
 Augmentation
 Optimization
 Scalability
 Multi-dimensionality
 ML Platform & Stack
 Frameworks
 Application services
Data
AlgorithmsML/AI
Data Consolidation + Insights
360° Customer View
Customer Insights
• Habits & Interests
• Needs & Predictions
• Buying patterns
• Basket Size
• Behavior
• Share of wallet
Customer Engagement
• Hyper Personalization
• Broader access to
products and services
• Faster & convenient
process
Data Cleansing - Is it important?
60%-80% of time – 12x Engineers Vs
Scientists
Better Data > Fancier Algorithms
Real-time needs – time-lag in
decisions
Not the glamorous part
AI can help in Many ways
Duplicates
• Name-identity recognition
• Irrelevant observations
• Filter unwanted outliers
Missing
Data
• Many algorithms don’t accept
• Dropping and imputing?
• Categorical and Numeric data
Context
• Diversity including alternate data sets
• Alternate data – linking
• Semantics & Integrity checking

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Somuvadali 180712051740

  • 1. Data Science and Artificial Intelligence towards improving Data Somu Vadali Head - C&D Labs Chief Data and Product Officer @ Future Group
  • 2. The Trinity of ML/AI  Consolidation  Cleansing  Augmentation  Optimization  Scalability  Multi-dimensionality  ML Platform & Stack  Frameworks  Application services Data AlgorithmsML/AI
  • 3. Data Consolidation + Insights 360° Customer View Customer Insights • Habits & Interests • Needs & Predictions • Buying patterns • Basket Size • Behavior • Share of wallet Customer Engagement • Hyper Personalization • Broader access to products and services • Faster & convenient process
  • 4. Data Cleansing - Is it important? 60%-80% of time – 12x Engineers Vs Scientists Better Data > Fancier Algorithms Real-time needs – time-lag in decisions Not the glamorous part
  • 5. AI can help in Many ways Duplicates • Name-identity recognition • Irrelevant observations • Filter unwanted outliers Missing Data • Many algorithms don’t accept • Dropping and imputing? • Categorical and Numeric data Context • Diversity including alternate data sets • Alternate data – linking • Semantics & Integrity checking