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Best practice du Data Product Management

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Aurélie Fliedel Quicksign "Best practice du Data Product Management "
Comment allier Produit et Business pour mettre la data au service de ceux-ci ?
Comment aligner l'organisation pour fluidifier les process de Data Product management ?

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Best practice du Data Product Management

  1. 1. DATA PRODUCT MANAGEMENT 19.02.2019
  2. 2. • Intro • QuickSign: who we are • Data product management: what is it? • Best practices Agenda #1 #2 #3 #4
  3. 3. QuickSign: who we are • The European leader in digital onboarding for financial services • White label B2B2C SAAS platform • 10 years of expertise
  4. 4. Digital onboarding • Upgraded user experience • Improved operational efficiency • Regulatory risks kept under control
  5. 5. Real case c • x • Customers’ issue : Fraud is expensive • Manual fraud detection (often too late!) • Ever-changing fraud behaviours Fraud detection
  6. 6. Real case c • x • Predictive approach • Independant algorithm from already-known fraud patterns Our solution
  7. 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. 8. Data product management DATA PRODUCT MNGT Data Science UX Go to market Pricing Business Objectives Customers’ needs Development
  9. 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. 10. Key Success Factor #2 Agile, iterative process
  11. 11. Key Success Factor #3 Scaling from research to production
  12. 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. 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. 14. c
  15. 15. THANK YOU! Aurélie Fliedel Chief marketing & product officer

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