Fontys Eric van Tol

1. Tja, De Big Data belofte: Data gedreven Service
innovatie!?
Service innovatie: creëren van niet bestaande
verlangens
Data gedreven: tegelijk zoeken naar de vraag en de
data.

4. The Big Data Surfers
Frightful Five: Apple, Amazon, Google,
Microsoft and Facebook
value $2.2 trillion
BAT: Baidu, Alibaba and Tencent
value $600 billion

5. The race against the machine
unemployed knowledge worker?
End of privacy
Is anonymity still possible?
Data Platform power
Can we compete Alibaba or Uber?
Societal anxiety

6. embrace new technology and adapt legacy
share valuable data
damaged reputation
(tackle privacy and trust issues)
The fear to

7. The holy grail of Big Data: Personalized services
- predict behavior – immediate response - location based
• personalized medicine (match your physiology)
• video suggestions (perfectly fit what they want to watch now)
• real time shelter advice (customized indications in case of
emergency)
• precision farming (real-time, geo-spatially data such as seeding
records, fertility applications, weather, soil, and crop health)

8. Eric van Tol
evtol@dataXL.eu

10. freemium
location-based

11. augmented reality

12. multiplayer
online - mobile

13. https://cloudplatform.googleblog.com/2016/09/bringing-Pokemon-GO-to-life-on-Google-Cloud.html

14. Online Gaming
Gamer
Game Advice
Influencing behaviour
Recommendation
Personalization
Real Time & Predictive

15. Online Gaming
Essence that you can:
• Count everything (N = All cases)
• See changes, (or near) real time
• See patterns
• from the past that says something about the future
• detailed anomalies
• trends
Personalization
Real Time & Predictive

16. Game addicts go to unconventional and dangerous
places to find all these creature
developers have reverse engineered the private,
internal Pokémon Go API creating a number of
unofficial APIs and third-party apps. Most popular GPS
spoofing
full access to Google account, all apps therein, and
the personal information each app contained.
16/17 July, DDoS (Denial-of-service) attack took
down Pokemon Go for a few hours.
The success of Pokemon Go has spawned dozens of
copycats like Unilever Ventures
Addiction, Security, Privacy, Intelectual
prpperty issues

17. Een paar computers generen data

18. En toen..
Alles en iedereen
generen data

19. Image
Text
Numeric
More data - better interpretation

20. Graph- netwerk data
Location - Geo data
Sensor data

21. Organiseren is eenduidigheid creëren

22. Het spel op de grens
Wildgroei data
Dataficatie
&
Data analyse

23. Manage KPIs:
ERP, DWH, RDBMS
Business Intelligence
Realtime explore data:
NO SQL, Hadoop, Spark
Hyper Agile, Sandbox
Samenbrengen tooling…

24. Proces
optimalisatie
Personalized services
Samenbrengen doelen…

25. Data driven service innovation
75% Dutch economy are services
and is lagging behind in services productivity
differentiation of companies depends their (big) data strategies.
finding new and innovative data driven services
https://www.rijksoverheid.nl/documenten/rapporten/2016/07/06/werkgroeprapporten-studiegroep-duurzame-groei

26. Nature of ICT systems (1)
the neat causal ICT systems are no more
where the ICT system is too complex to understand.
From neat “if X then Y else Z” rules,
to more organic and probabilistic ICT systems,

27. “…we can only fully figure out the meaning
of new technology in business and
institutions after the fact [drift]; and that we
plainly have to live with such
and state of ignorance.”
impossibility
*C. Ciborra. 2002. The Labyrinths of Information, Oxford University Press, Chapter 5 Dérive: Drift and deviation, p. 85.
Nature of ICT systems (2)
The neat causal ICT systems are no more
And it is not a new insight…
How do we recognize our ignorance?
Can we get a overview or do we live an illusion of control?

28. Nature of Services development
are complex and intangible
Difference between formal description of services and what you see
happening
"The moment of truth 'of the service is difficult to follow
Co-creation, participation and simultaneous production and
consumption of many services
Innovative service which tries establish desires that previously did
not exist.
How do we deal with this complexity?
What is the common language of a multidisciplinary team?
How do we discover new services?

29. “The algorithm did it” is not an acceptable excuse if algorithmic
systems make mistakes or have undesired consequences.
https:www.technologyreview.com/s/602933/how-to-hold-algorithms-accountable/?imm_mid=0eb199&cmp=em-data-na-na-newsltr//_20161130
Richards and King (2013); Schneier (2015).
https://www.oreilly.com/ideas/if-prejudice-lurks-among-us-can-our-analytics-do-any-better
If prejudice lurks among us, can our analytics do any better?
Human and algorithmic bias
Nature of Machine Learning (1)
a barrier to transparency

30. Nature of Machine Learning (2)
a barrier to transparency
“A mismatch between the mathematical optimization in
high-dimensionality characteristic of machine learning and
the demands of human-scale reasoning
Neural networks, especially with the rise of deep learning,
pose perhaps the biggest challenge.
European Union regulations on algorithmic decision-making and a “right to explanation Bryce Goodman & Seth Flaxman 2016 https://arxiv.org/pdf/1606.08813v2.pdf
How do we make Machine Learning trackable or traceable?
How do we explain a algorithmic decision to the user?

31. Nature of social media
daily life is published
Consumers are publishing everything of their
daily life's. To control this is not doable and most
of the time undesirable.
Social Media players share less and less data…
How do we get access to social media data?
How do we do that without losing the trust of a consumer or citizen?

32. Nature of autonomously acting algorithms
unforeseen dynamics
Autonomously acting algorithms that exist on the Internet
• vast, global, connected, always on, silent, unseen
• self-discovery, self-organising, self-healing, self-learning
Generates side effects, unexpected consequences, unforeseen dynamics
high-frequency trading, mobile add auction, and general IoT as example
How do we deal with unforeseen dynamics?
Do we trust a self acting military drone?

33. Nature of the prosumer (1)
risk naive
“People aren’t stupid. The problem is that our educational
system has an amazing blind spot concerning risk literacy.
We teach our children the mathematics of certainty
but not the mathematics of uncertainty, statistical
thinking. And we teach our children biology
but not the psychology that shapes their fears and desires.
Do we know enough? What is sufficient to cope?
Gerd Grigerenzer, auteur of ‘Risk Savvy’

34. Nature of the prosumer (2)
knowing is not yet doing
From the behavioral sciences it has been shown
that the ability of people to weigh information
and to take rational choices are limited
https://www.wrr.nl/publicaties/rapporten/2017/04/24/weten-is-nog-geen-doen
So, how feasible is a good risk assessment?

35. Nature of the prosumer (3)
unconscious part of the service
A prosumer is a person who consumes and produces media.
Self-service origin
From ATMs to e-commerce to mobile payments,
lower costs and more convenience
https://en.wikipedia.org/wiki/Prosumer
http://www.itif.org/files/2010-self-service-economy.pdf
How to balance convenience and privacy?
How do we ensure that the consumer is in control?

36. Nature of regulation
GDPR (General Data Protection Regulation) May 2018,
Implementation by the Personal Data Authority in the Netherlands
To much restriction of service development?
PSD2 (Payment Services Directive2) January 2018
Opportunity for fintech companies? Threat to banks?
Do we oversee the consequences?
Can we comply?

37. A Big Data project is an experiment with a continuous interaction between
poor requirement articulation and naive exploration of data sets.
A continuous iteration between knowing the problem and having the data
Data gets value during use
37
Nature of a Big Data project (1)
messy experiments
What is a good Big Data project?
Is there a 'best practice’ or even a ‘good practice’?

38. Data agility separates winners and losers
Big data projects are more research projects than production
projects
Conventional project management combine wisdom with data
agility (light, fast and accurate)
Well, and how do we do that?
Does it work for large ICT infrastructural projects?
Nature of a Big Data project (2)
messy experiments

39. Nature of ICT systems
Acknowledge your ignorance! When are you in the ‘factory’ and when in ‘chaos’? Sense - Analyse or Act - probe.
Nature of Services development
Trust operational people in ‘the moment of truth’ of the service – and digital service design
Nature of Machine Learning
Alternative traceable algorithms - AI assisted human decisions in sensitive situations
Nature of autonomously acting algorithms
Depending application
Nature of social media
Do not use all insights
Nature of innovation
Be a smart improviser in search of surprises. What is the use of a map if you do not know the terrain?
Nature of the prosumer
Educate ‘learn to learn’. risk savvy and consumer is conscious steering part the service creation.
Nature of regulation
GDPR as opportunity to get more customer focus. PDP2 to get innovation in finance.
Nature of a Big Data project
dare to fail, share your failures – more than saying that you do
Nature of data driven digital service innovation

41. without prejudice How to avoid unfair conclusions even if they are true?
without guesswork How to answer questions with a guaranteed level of accuracy?
ensures confidentiality How to answer questions without revealing secrets?
provides transparency How to clarify answers such that they become indisputable?
Data driven service innovation is FACTual?
FACT (fair, accurate, confidential, transparent) algorithms
Wil van der Aalst http://www.vsnu.nl/digital-society-introduction-researchers/big-data.html
Data driven service innovation is FAIR?
FAIR (findable, accessible, interoperable, reusable) data
https://wetenschapsagenda.nl/nwo-honoreert-aanvragen-startimpuls-nationale-wetenschapsagenda/
Route Waardecreatie door verantwoorde toegang en gebruik van big data
Verantwoorde Waardecreatie met Big Data

42. From data science back to behavioural
sciences?
Human nature

43. Capabilities
individual or groupHedgehog
Fox

44. Manage KPIs:
Realtime explore data:
Attempts to bring together both worlds…
Devops? Dataops?
Growth Hacking?

45. The nature of
Data Driven Service Innovation