Data Innovation Summit 2019

Data Innovation Summit 2019

Thursday

9:20 P Keynote: Building a win-win data environment for analytics success

  • AI makes prediction cheap
  • data becomes more valuable
    • need to have clear data management & strategy
    • central data repository (unified warehouse)
    • ability to scale is crucial
    • not accidental architecture/ecosystem
    • study your data

10:30 M1 How to get mind-blowing ROI from your Analytics and Data Science teams

  • problems:
    • focus on big & actionable problems
  • infrastructure
    • start by MVP
  • people
    • have strategy for targeting top talent
    • engaged people & shared ownership

12:00 M2 Agile Analytics – Building a data driven culture

  • pull analytics instead of push
  • every role should have analytics support
  • Laloux culture model

13:30 M2 Doing data science at iZettle - What worked and what didn’t

14:30 M1 Algorithmic attribution modelling

16:30 M3 Handling personally identifiable information in a modern data platform

  • anonymize data on ingestion and create mapping table (containing also 'removed' metadata)
  • multiple mapping tables (prod, dev, diff access roles)
  • adding fuzzy analytical fields

17:00 M1 How to introduce Data Science in big companies

  • to introduce data science to a company, you need:
    • business problem to solve
    • meet with like-minded people regularly (mastermind method)
    • define success measure
    • taste your food while cooking (MVP)
    • prepare model
    • keep stakeholders engaged by showing data insight regularly, keeping up the momentum
    • serve insight while hot
  • process:
    • build > show > use > feedback
    • secret ingredient: excite people

Friday

9:45 P Keynote: Responsibility in the Data Age

10:15 M6 Accelerating Analytics in a New Era of Data

  • what makes competition competitive? The data.
  • massive amounts:
      • 60% time spent preparing
      • 10% data gets analyzed
      • => shallow reports
      • systems are not designed for todays needs
  • solutions: MPP DBs, MR
  • alternative: GPU-accelerated DBs

10:45 M6 What problems should be solved with AI in Banking

  • think different - don't be afraid to rethink instead of incremental evolution
  • start small and learn fast
  • we don't have time to do incremental changes
  • work on BIG stuff (not just improving but creating the new)
  • always think 10x

11:15 M2 Customer centric data science

  • book Customer Centricity
  • have strong customer identity
  • store all interaction
  • consent to use
  • avoid personal data
  • examples:
    • collaborative filtering: recommending
    • segmentation: long term behaviour (customer churn rate, lifetime…), e.g. prob. of alive customer given his recency vs frequency

11:45 M1 Model Industrialization in ING Bank

  • analytics > innovation > new business models
  • customer interaction based on analytics
  • engage more data scientists instead of data analytics (use models instead of just manual analysis)
  • empower analysts wit hthe tools od data scientists (see also auto-sklearn, mlflow.org etc.)

12:15 P Chairman's Closing Remarks

  • learn by doing
  • balanced innovation
  • empower people with tech
  • have in idea
  • be stubborn
  • don't be afraid to speak to people
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