Faysal AzizSnowflake-Powered AI for Supply Chain and Retail
A leadership briefing on analytics, governed data and AI use cases
Why this course
This half-day briefing examines how Snowflake data, analytics and AI capabilities can support supply-chain and retail decisions. Leaders explore operational, inventory, logistics, transaction and customer data through short prepared demonstrations.
The course emphasises use-case selection, data quality, governed definitions, human oversight and platform-consumption trade-offs. Forecasting and optimisation are considered as potential applications, not guaranteed business results or complete solutions delivered during the briefing.
Learning outcomes
- Explain Snowflake data and compute concepts relevant to business decisions.
- Identify supply-chain and retail data relationships and useful performance indicators.
- Interpret selected queries, refreshed datasets and AI-assisted demonstration outputs.
- Distinguish forecasting concepts from proven predictive performance.
- Evaluate governance, privacy, accuracy and consumption considerations.
- Prioritise a bounded first use case with ownership and measurable evaluation criteria.
Prerequisites
- Working familiarity with Snowflake and business reporting.
- Experience in business operations, retail or supply-chain management.
- Basic cloud-data and AI awareness is helpful; no developer workshop is assumed.
5 modules
01Snowflake data foundations5 topics
- Snowflake architecture at a glance
- Databases, schemas, tables and virtual warehouses
- Separation of storage and compute
- Structured, semi-structured and unstructured data
- From fragmented systems to a unified data foundation
02Supply-chain analysis and predictive opportunities5 topics
- Supply-chain data sources and integration
- Orders and sales
- Inventory and warehouse data
- Suppliers and procurement
- Transportation and fulfilment
- Creating a consolidated operational view
- SQL-based supply-chain analysis
- Demand, stock and fulfilment indicators
- Snowflake demonstration: operational data exploration
Refreshed datasets and forecasting concepts
- Demand forecasting concepts
- Inventory availability and candidate replenishment signals for evaluation
- Slow-moving and at-risk stock
- Logistics performance and disruption indicators
- Dynamic Tables and target-lag/refresh considerations for analytical datasets
- Streams for change tracking and Tasks for scheduled or triggered processing
- Snowflake demonstration: inventory and demand intelligence
03Retail and customer intelligence7 topics
- Connecting customer, transaction and product data
- Customer 360 concepts
- Shopping behaviour and purchasing patterns
- Segmentation and personalisation
- Customer sentiment and feedback analysis
- Loyalty, retention and customer lifetime value
- Snowflake demonstration: retail customer insights
04Cortex AI and governed business definitions6 topics
- Snowflake Cortex AI capabilities
- AI Functions for text and unstructured data
- Sentiment, classification and summarisation
- Cortex Analyst and natural-language questions over supported semantic definitions
- Semantic Views for governed metrics, dimensions and relationships
- Snowflake demonstration: AI-assisted customer and operational analysis
Feature, model, region and access availability vary; demonstrations use a prepared supported environment.
05Governance and a credible first use case7 topics
- Role-based access control
- Data privacy and sensitive customer information
- Secure data sharing
- AI accuracy and human oversight
- Compute consumption and cost awareness
- Selecting a credible first use case
- Executive ownership and measurable outcomes
Compare controlled data sharing, access policies and consumption with the proposed use case. Verify predictions and generated analysis before operational decisions.
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