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DATA PRODUCT MANAGEMENT
19.02.2019
• Intro
• QuickSign: who we are
• Data product management: what is it?
• Best practices
Agenda
#1
#2
#3
#4
QuickSign: who we are
• The European leader in digital onboarding for
financial services
• White label B2B2C SAAS platform
• 10 years of expertise
Digital onboarding
• Upgraded user experience
• Improved operational efficiency
• Regulatory risks kept under control
Real case
c
• x
• Customers’ issue : Fraud is expensive
• Manual fraud detection (often too late!)
• Ever-changing fraud behaviours
Fraud detection
Real case
c
• x
• Predictive approach
• Independant algorithm from already-known
fraud patterns
Our solution
The Data Revolution has begun
1. Passive use of data
2. Data management and reporting
3. Process and product innovation with data
4. Value generated directly with AI/DL
#1
#2
#3
#4
Data product management
DATA
PRODUCT
MNGT
Data
Science
UX
Go to market
Pricing
Business
Objectives
Customers’ needs
Development
Key Success Factor #1
Business first
c
• Data speaks for itself… BUT before digging
into data…
• Indentify the customers’ issue and use cases
• Be clear about what the product does
• Define the KPIs to follow
Key Success Factor #2
Agile, iterative process
Key Success Factor #3
Scaling from research to production
Key Success Factor #4
Focused, aligned organization
Data
Product
Manager
Product
Owner
Data
Scientist
Data
Engineer
Dev
Multi-disciplinary feature team
Product
team
Dev back
/ front
DL team Devops
Dev ops
Key Success Factor #5
Monitor everything
• Acceptance criteria before going live: key
metrics depending on business goals to
determine if the AI product is accurate
enough
• Sampling to compare human decision and
AI decision all along the product lifecycle
c
c
THANK YOU!
Aurélie Fliedel
Chief marketing & product officer

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Best practice du Data Product Management

  • 2. • Intro • QuickSign: who we are • Data product management: what is it? • Best practices Agenda #1 #2 #3 #4
  • 3. QuickSign: who we are • The European leader in digital onboarding for financial services • White label B2B2C SAAS platform • 10 years of expertise
  • 4. Digital onboarding • Upgraded user experience • Improved operational efficiency • Regulatory risks kept under control
  • 5. Real case c • x • Customers’ issue : Fraud is expensive • Manual fraud detection (often too late!) • Ever-changing fraud behaviours Fraud detection
  • 6. Real case c • x • Predictive approach • Independant algorithm from already-known fraud patterns Our solution
  • 7. The Data Revolution has begun 1. Passive use of data 2. Data management and reporting 3. Process and product innovation with data 4. Value generated directly with AI/DL #1 #2 #3 #4
  • 8. Data product management DATA PRODUCT MNGT Data Science UX Go to market Pricing Business Objectives Customers’ needs Development
  • 9. Key Success Factor #1 Business first c • Data speaks for itself… BUT before digging into data… • Indentify the customers’ issue and use cases • Be clear about what the product does • Define the KPIs to follow
  • 10. Key Success Factor #2 Agile, iterative process
  • 11. Key Success Factor #3 Scaling from research to production
  • 12. Key Success Factor #4 Focused, aligned organization Data Product Manager Product Owner Data Scientist Data Engineer Dev Multi-disciplinary feature team Product team Dev back / front DL team Devops Dev ops
  • 13. Key Success Factor #5 Monitor everything • Acceptance criteria before going live: key metrics depending on business goals to determine if the AI product is accurate enough • Sampling to compare human decision and AI decision all along the product lifecycle c
  • 14. c
  • 15. THANK YOU! Aurélie Fliedel Chief marketing & product officer