Saku Vaittinen keynote at AWS Community Day Nordics 2018

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Data Driven Public
Transport
Saku Vaittinen, VR Group | 21.3.2018

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VR Group: Moving people and other things

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VR Group’s track to AWS
• Cost effectiveness + elasticity + development speed
• First implementations 2016, core infra ready 2017 (CON206)
• Terraform
• Direct Connect
• Shift from on-prem to cloud required lots from organization
• Comprehensive mindset change with DevOps through new projects
• Lift’n’shift avoided
• Focus on architecture!

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Data on driver’s seat
• Incentive: Free competition on local railway passenger traffic by
2021 and on long-haul by 2024
• From Excel to Data science
• Learning from data

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Analytics project
• Started in January 2018
• 6 members, everyone new
• Lots of legacy burden
• Almost production-ready
• Infrastructure, Data platform, Data model
• First data sources
• Power BI dashboards and reports
• Recommendation API for new ticket system
• Data science work has been started

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OPERATIVE INTELLIGENCE CUSTOMER INTELLIGENCE
RESOURCE
PLANNING
PERSONALIZED CONTENT
PRODUCT, PRICING
STRATEGY
CUSTOMER SERVICE
ROBOTIZATION
CORPORATE
TRAVEL
CUSTOMER
VALUE & CHURN
SHIFT PLANNING
OTHER OPERATION
OPTIMIZATION
LOYALTY
PROGRAM
DEVELOPMENT
The Problem

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Diverse data ecosystem
Data sources
• DBs: on-premise, cloud
• Apps: mobile, web, ticket machines
• Systems: seat reservation, train planning, ERP
• Communications: email, surveys, social media
Data users
• Dashboards & analytics applications
• Personalized content
• Operative intelligence
• Customer service

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Data analytics stack
Cloud
• AWS
Open source tools
• Apache NiFi, Jupyter, DbSchema, Python …
Business analytics applications
• Power BI
Warehouse
• Snowflake

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AWS Architecture
Connectivity
• VPC Privatelink
• Direct in-account connection to Snowflake in same region
Data pipeline
• Data Migration Service
• Daily batches, Continuous migration by CDC files
Analytics API
• ECS, Lambda, API GW, ElastiCache

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Snowflake characteristics
• AWS-native
• Pay-per-use pricing model
• Separated storage & compute
• Virtual Data Warehouses
• Variable patterns both in data import & usage
• Batch, stream
• BI users, Data science, API

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Next hot things
• Raw data from ALL sources
• Machine Learning for e.g.:
• Time series forecast algorithms
• Ticket pricing
• Work shift lists
• Train configuration planning
• Immediate reaction to changes