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

2. Data as a catalyst
for a new approach to
innovation
Giovanna Miritello
@gmiritello

3. 26
countries
50
partner networks
430+ M
connections
107k
employees

4. Data overview

5. Enterprise & External data
External data is usually wide and shallow. Enterprise data is narrow and deep.
data-driven home search
transportation
socio economics -unemployment-

6. so data

7. so data
but data is not numbers, it’s people.

12. How to build a product suitable for every different customer?

13. Let’s the data speak.
How to build a product suitable for every different customer?

14. Data-driven customer centric products

15. Data-driven customer centric products

16. Data-driven customer centric products

17. Personalised customer experience

18. Personalised customer experience

19. Why personalisation is important?
VS

20. Personalisation at Vodafone

21. slower 4Gfaster 4G
Why do we need more data & advanced models?
The world is often simple to predict.

22. mobility needs apps/type of content
device healthnetwork performance
in the area
Or not?

23. Network experience
Network experience might be related to:
• home/work location
• mobility patterns
• needs and interests
• device type/quality

24. Price-plan
2. We are social animals
1. Black or white
and
Gb
£
data plan (Gb)
# contacts
%customers

25. Understanding reasons to leave and motivations to stay
customer
experience
OOB spend
spend on
roaming
4G at home

26. 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

27. 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.

28. This does not happen in one day.

29. Within the data science community
No organisation is perfect,
but some good practices help [1/2]

30. Invest in standardisation
Standardisation enables sharing and collaboration: lowering barriers, increasing
expectations)
data format
platform
tools
frameworkmethodology

31. Sharing knowledge
Data stories and visualisations as a daily practice

32. Avoid “data vomit”
D.J. Patil, Data Jujitsu: The Art of Turning Data into Product
VS

33. 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

34. 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?

35. Model accuracy is important, but it’s not the only thing
Prefer interpretability at the beginning, then upgrade models.

36. 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

37. No organisation is perfect,
but some good practices help [2/2]
With the rest of the organisation

38. • 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

39. • 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

40. • 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

41. Thank you!
Giovanna Miritello
@gmiritello