Notes from Conferences

Data Innovation Summit 2019, Stockholm

- slides (dropbox)
- videos (youtube)


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

- _AI makes prediction cheap_
- data becomes more valuable
- need to have clear data mngmt & 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

- presentation
- 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

- presentation
- 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

- slides
- centralized data team (without separate backlog) with members embedded in product teams
- focusing on high.prob.of succ/value, starting with weight on success
- all data in one environment
- easy deployment of a model together with preprocessing
- easily accessible data quality reports (dashboards even for managers)

14:30 M1 Algorithmic attribution modelling

- presentation
- approximate shapley values to model attributions

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

- presentation
- 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

- presentation
- 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


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

- presentation
- more than 60% of time spent on data preparation
- massive datasets => ability to analyse only 10% of valuable data in reasonable time
- why not to use the power of GPU to crunch the data for us?

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