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Data as a catalyst for a new approach to innovation by Giovanna Miritello

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https://www.bigdataspain.org/2016/program/thu-data-catalyst-new-approach-innovation.html

https://www.youtube.com/watch?v=QFYj7ohiSvQ&t=32s&index=17&list=PL6O3g23-p8Tr5eqnIIPdBD_8eE5JBDBik

Published in: Technology
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Data as a catalyst for a new approach to innovation by Giovanna Miritello

  1. 1. Data as a catalyst for a new approach to innovation Giovanna Miritello @gmiritello
  2. 2. 26 countries 50 partner networks 430+ M connections 107k employees
  3. 3. Data overview
  4. 4. Enterprise & External data External data is usually wide and shallow. Enterprise data is narrow and deep. data-driven home search transportation socio economics -unemployment-
  5. 5. so data
  6. 6. so data but data is not numbers, it’s people.
  7. 7. How to build a product suitable for every different customer?
  8. 8. Let’s the data speak. How to build a product suitable for every different customer?
  9. 9. Data-driven customer centric products
  10. 10. Data-driven customer centric products
  11. 11. Data-driven customer centric products
  12. 12. Personalised customer experience
  13. 13. Personalised customer experience
  14. 14. Why personalisation is important? VS
  15. 15. Personalisation at Vodafone
  16. 16. slower 4Gfaster 4G Why do we need more data & advanced models? The world is often simple to predict.
  17. 17. mobility needs apps/type of content device healthnetwork performance in the area Or not?
  18. 18. Network experience Network experience might be related to: • home/work location • mobility patterns • needs and interests • device type/quality
  19. 19. Price-plan 2. We are social animals 1. Black or white and Gb £ data plan (Gb) # contacts %customers
  20. 20. Understanding reasons to leave and motivations to stay customer experience OOB spend spend on roaming 4G at home
  21. 21. Homophily and influence effects can be measured and predicted! Don’t forget the social factor! Jun Ding et al. Alone in the Game: Dynamic Spread of Churn Behavior in a Large Social Network a Longitudinal Study in MMORPG
  22. 22. Data is not numbers, it’s people. What you can’t predict, you must at least see! This customer feedback can go unnoticed if only structured feedback and scores get looked at.
  23. 23. This does not happen in one day.
  24. 24. Within the data science community No organisation is perfect, but some good practices help [1/2]
  25. 25. Invest in standardisation Standardisation enables sharing and collaboration: lowering barriers, increasing expectations) data format platform tools frameworkmethodology
  26. 26. Sharing knowledge Data stories and visualisations as a daily practice
  27. 27. Avoid “data vomit” D.J. Patil, Data Jujitsu: The Art of Turning Data into Product VS
  28. 28. Solid software development practices: know your code! http://thecuriouscan.com/learn-from-the-costliest-mistakes-in-history/ June 4, 1996 Ariane 5 rocket launched by the European Space Agency exploded just 37 seconds after its lift-off 7 billion dollars development of the rocket The cost 500 million dollars estimated value of the destroyed rocket and its cargo
  29. 29. Solid software development practices: know your code! http://thecuriouscan.com/learn-from-the-costliest-mistakes-in-history/ A software programming error! A 64 bit floating point number relating to the horizontal velocity of the rocket with respect to the platform was converted to a 16 bit signed integer. The number was larger than 32.767, the largest integer storable in a 16 bit signed integer, and thus the conversion failed. The reason for the blast?
  30. 30. Model accuracy is important, but it’s not the only thing Prefer interpretability at the beginning, then upgrade models.
  31. 31. Start simple, then iterate • think it (reduces the risk) • build it (as fast as possible) • ship it (gradually roll out to all users) • tweak it (continuously improve) Prefer high precision of one product instead than many sophisticated products. The real risk is building solutions that no one needs: D.J. Patil, Data Jujitsu: The Art of Turning Data into Product
  32. 32. No organisation is perfect, but some good practices help [2/2] With the rest of the organisation
  33. 33. • Engage with stakeholders from day 1 It is a bidirectional direction thing: - a data science team must know the business priorities - the whole organisation needs to understand and engage with data driven results, stories and their value
  34. 34. • Engage with stakeholders from day 1 It is a bidirectional direction thing: - a data science team must know the business priorities - the whole organisation needs to understand and engage with data driven results, stories and their value • Remove barriers & enable data connectivity
  35. 35. • Engage with stakeholders from day 1 It is a bidirectional direction thing: - a data science team must know the business priorities - the whole organisation needs to understand and engage with data driven results, stories and their value • Remove barriers & enable data connectivity • Agree on implementation and performance metrics
  36. 36. Thank you! Giovanna Miritello @gmiritello

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