Anchormen Jeroen Vlek

1. Data Activation by Jeroen Vlek.
DEVELOP YOUR
DIGITAL CAREER
30-11-2017
Anchormen @ Talent Event

2. WE ANALYZE, DEVELOP,
IMPLEMENT AND OPTIMIZE.
THIS IS MORE THAN A PROCESS, IT’S OUR DNA.
30-11-2017
Anchormen @ Talent Event

3. HOW?
GROW LIVE
30-11-2017
GROW
LIVE
Innovative
minds
Meaningful
resultsSolid
partnership
Anchormen @ Talent Event

4. 30-11-2017
Anchormen @ Talent Event
HOW WE
ACTIVATE DATA

5. BIG DATA SERVICES
Start-to-finish scalable and flexible solutions for all your data:
 Architecture & design
 Implementation & integration
 Platform setup & maintenance
 Consulting & training
30-11-2017
Anchormen @ Talent Event

6. DATA PLATFORM SERVICES
Unified approach towards data management and integration
that can improve scalability and flexibility.
 Consulting on design, installation, and configuration of
complex data platforms.
 Focus on large-scale databases.
 Both greenfield projects and existing situations.
30-11-2017
Anchormen @ Talent Event

7. DATA SCIENCE & A.I.
Creating valuable new insights through prediction models and
A.I.:
 High caliber data consulting
 Recommendations & chatbots & predictive maintenance
 Process & text mining
 Computer vision
30-11-2017
Anchormen @ Talent Event

8. HIGH POTENTIAL PROGRAM
We will find the right people for you and guide them to success.
 Data engineers & scientists
 1-year program
 Training & guidance
30-11-2017
Anchormen @ Talent Event

9. THE ROAD SO FAR
30-11-2017
Anchormen @ Talent Event

10. 30-11-2017
Anchormen @ Talent Event
WHAT IS AI?

11. “THE TRUE SIGN OF INTELLIGENCE IS NOT KNOWLEDGE
BUT IMAGINATION.”
ALBERT EINSTEIN
30-11-2017
Anchormen @ Talent Event

12. ARTIFICIAL INTELLIGENCE
 Perception
 Reasoning
 Learning
earning?
30-11-2017
Anchormen @ Talent Event

13. “LEARNING FROM DATA MEANS INFERRING
WHAT WE DON’T KNOW FROM WHAT WE KNOW.”
CHRIS POOL
LEAD DATA SCIENTIST @ ANCHORMEN
30-11-2017
Anchormen @ Talent Event

14. LEARNING TO PREDICT
For a given object you are asked to make a prediction
 Is tomorrow a good day for playing football?
 Is this tweet positive or negative?
 Does this image contain a car?
Learning is making such predictions after observing data.
MACHINE LEARNING?
30-11-2017
Anchormen @ Talent Event

15. WHAT HAPPENS IN LEARNING
 The learning algorithm observes given examples
 It tries to find common patterns that explain the data: it tries to generalize so that predictions can be made for new examples
 Exactly how this is done depends on what algorithm we are using
30-11-2017
Anchormen @ Talent Event

16. HOW GOOD IS AI?
30-11-2017
Anchormen @ Talent Event

17. COMPUTER VISION
 Humans achieve 5% error rate on object recognition
 Inception-v4 achieves 3.08% (Szegedy et al. 2016)
30-11-2017
Anchormen @ Talent Event

18. NATURAL LANGUAGE
PROCESSING
 Speech Recognition is at 5.1% (Xiong et. al 2017)
 Statistical Machine Translation around 85% human (BLEU
score)
 Neural Machine Translation is the new contender
30-11-2017
Anchormen @ Talent Event

19. “THE BOTTLENECK NOW IS IN MANAGEMENT,
IMPLEMENTATION, AND BUSINESS IMAGINATION.”
BRYNJOLFSSON & MCAFEE
HARVARD BUSINESS REVIEW (JULY 2017)
30-11-2017
Anchormen @ Talent Event

20. 30-11-2017
Anchormen @ Talent Event
LEARNING BY DOING

21. THE CASE (1)
 A large part of any online advertising campaign is determining how much a click and its conversion is worth.
 While bidding for advertising space is typically a blind auction with special sets of rules, optimizing your bid is difficult.
 External influences like accidents, weather conditions, news, PR activities and events, tv subtitles or tv guides, all have impact on
online behaviour and activities
30-11-2017
Anchormen @ Talent Event

22. THE CASE (2)
 Google trends is a good indicator for populair topics
 Can we use Twitter data to explain these peaks?
 Challenges:
 Lots of spam on Twitter
 Keyword != topic
30-11-2017
Anchormen @ Talent Event

23. STRUKTON RAIL
Strukton rail provides solutions for rail infrastructure and
electrical systems in rolling equipment.
 Contracted by ProRail for maintaining (parts of) the Dutch
railroads
 Performance contract
 High competitive market
30-11-2017
Anchormen @ Talent Event

24. VITENS
The largest water company in the Netherlands
 Goal to become data-driven
 A.I. is relatively new
30-11-2017
Anchormen @ Talent Event

25. AI CHAT-BOT
One of the most challenging parts of a chat-bot (and NLP) is
interpreting a sentence because:
 Ambiguity
 People use slang / abbreviations
 Grammar errors
 Need knowledge of the world
30-11-2017
Anchormen @ Talent Event

26. 30-11-2017
Anchormen @ Talent Event
HOW TO
ACTIVATE YOUR DATA?

27. METHODOLOGY
 Define clear goals
Avoid searching for patterns in the data without a clear goal.
 Use the right tools
Focus on tools that provide quick results.
 Test and validate often
Create a data driven mindset within your organization.
 Fail fast and fail forward
Data Science is highly innovative. Plans have to be changed all
the time.
30-11-2017
Anchormen @ Talent Event

28. 30-11-2017
Anchormen @ Talent Event
CIRCLE OF DATA ACTIVATION

29. A/B TESTING
 Achieve a baseline
 Measure your improvement
30-11-2017
Anchormen @ Talent Event

30. CONTINOUS
INTEGRATION /
DELIVERY
 DS/AI work results in code
 Code should adhere quality standards
 Include quantitative model performance
30-11-2017
Anchormen @ Talent Event

31. GOVERNANCE
 What data was used to train the model?
 What where the model parameters?
 Which customers interacted with it?
30-11-2017
Anchormen @ Talent Event

32. “ORGANIZATIONS NEED TO OPEN UP TO NEW INSIGHTS”
GEOFFREY VAN MEER
HEAD OF DATA INTELLIGENCE @ RTL
30-11-2017
Anchormen @ Talent Event

33. ROADMAP TO
DATA ACTIVATION
30-11-2017
Anchormen @ Talent Event

34. COME MAKE MUSIC
WITH US!
anchormen.nl/careers
30-11-2017
Anchormen @ Talent Event

35. THANK YOU
FOR YOUR
ATTENTION
Jeroen Vlek, CTO @ Anchormen
Pedro de Medinalaan 11,
1086 XK Amsterdam
020 - 773 1972
www.anchormen.nl

36. CLASSIFICATION
Predict a class using examples, for example the traffic light.
Other examples:
 Sentiment analysis
 Predictive maintenance
30-11-2017
Anchormen @ Talent Event

37. CLUSTERING
Group observations in “meaningful” groups
 Group a set of documents into groups about topic
 Group similar observations.
30-11-2017
Anchormen @ Talent Event

38. REINFORCEMENT
LEARNING
Coupling actions to state
 Deduce the next best action
 Maximize the total reward
30-11-2017
Anchormen @ Talent Event

39. FINANCIAL FORECASTING
Client: International Travel Agency
I
•Initial Business Question
 What factors determine the right price for a house? (e.g. WiFi, Swimming pool, distance to beach)
II
•Technical solutions
 Time series analysis (Season, Trend & Cyclical decomposition)
 Regression and Trigger Models (Explaining residuals with non-frequent events)
 Transfer Learning (Combining models from different regions)
III
•Primary data sources
 Financial ledger (SAP)
 Social media data (Twitter Firehose)
 Web traffic data (Google Analytics)
IV
•Business impact
 Insight in how investing in one region effects sales in neighboring regions
 Presenting C-level managers with improved financial predictions
 Revision of online marketing strategies
V
•Spin-off
 Photo analysis, what makes a good photo? Clear guidelines for photographers?
 Consumer segmentation, website stability, search
 Competitor analysis, pricing models, AdWords and keywords
Anchormen @ Talent Event

40. MARKETING SPEND ANALYSIS
Client: Global Consumer Goods
I
•Initial Business Question
 What is the best moment to buy ads? How to align TV campaign with online events?
II
•Technical solutions
 Time series analysis (Season, Trend & Cyclical decomposition)
 Regression and Trigger Models (Explaining residuals with rare events)
 Transfer Learning (Combining models from different regions)
III
•Primary data sources
 Marketing spend (SAP)
 Social Media (Brand24)
 Weather data (Weather Underground)
IV
•Business impact
 Higher return on TV campaigns
 Insight in what triggers people to buy flu related products
V
•Spin-off
 Mosquito index
Anchormen @ Talent Event

41. LOGISTICS & STOCK OPTIMIZING
Client: Logistical Organisation
I
•Initial Business Question
 How can we achieve better usage of machines?
II
•Technical solutions
 Process mining
 Simple reports deliberately targeted across intra-organizational boundaries
 Programmed simulations to analyze complex interaction of simple processes
III
•Primary data sources
 Maintenance logs (relational database)
 Logs and standing data of supplies and procurements (ERP system)
 Machine logs
IV
•Business impact
 Maintenance planners now have access to historical data while planning
 Impact on planning is now calculated real-time during Long-Term Planning meetings
 List of parts were identified where higher supply levels will increase availability
V
•Spin-off
 Promotion of a data-driven culture
Anchormen @ Talent Event
source: ministry of Defence