Trusted Analytics

The future of our information society

prof. dr. Sander KlousBig Data Ecosystems in Business and SocietyUniversity of AmsterdamPartner in charge of Data & AnalyticsKPMG Advisoryklous.sander@kpmg.nl@sanderkloushttp://nl.linkedin.com/in/sanderklous

Extreme expectations

https://www.youtube.com/watch?v=2vXyx_qG6mQ

2

3

Content

1. Society2. Technology3. Organization4. Ecosystems5. Conclusions

4

Data & Society

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Tipping points

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Privacy versus Safety/ConveniencePr

ivac

y

Safety/Convenience7

Big Brother? That’s us!

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Content

1. Society2. Technology3. Organization4. Ecosystems5. Conclusions

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Data Scientists

Apples and Pears

■ Jar A contains 10 apples and 30 pears■ Jar B contains 20 of each

Fred picks a jar, without further evidence there is a 50% chance this is jar A (or B).

Fred pulls out a pear. The new probability that Fred picked bowl A is 0.75 x 0.5 / ( 0.75 x 0.5 + 0.5 x 0.5 ) = 0.6

Jar A Jar BP(Hn|E) =

P(E|Hn)P(Hn)

Sum1N (P(E|Hn))

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Quantity over Quality

Known symmetric statistical error• Example:

Typical Gaussian distributed measurement errors• Solution to get a more accurate mean value:

More data from the same source

Statistical Systematically

Sym

met

ricAs

ymm

etric

Blue line: financially healthy clients

Red line: clients from Fin. Health

Dep.

Unknown asymmetric systematically error•Example:Tidal effects in the lake of GenevaThe TGV on the train track near CERN

•Solution to get a more accurate results:More data from different sources 11

Trust includes Open Source

Combining data

Modelling & learning

Presenting / Dashboarding

Validation of individual decisions

Automated decision making process

Provide feedback for(non-)supervised learning

Issue 1 Issue 2

Answer

Decision

Answer

Decision

ESKAPADE

Data Architecture Deployment ArchitecturePlatform Architecture

Data Lake Data Lake Data Lake

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Content

1. Society2. Technology3. Organization4. Ecosystems5. Conclusions

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Start smallIn

spira

tion • What is your

current status?• What decisions

are suboptimal?• How can they be

improved?• Experiment

selection

Incu

batio

n • Organized as a startup

• Failure is acceptable

• Efficiency is not (very) important

• Training and knowledge development

• Initial technical platform setup

• What efforts do we need?

Impl

emen

tatio

n • Business value generation

• Integration into production environment

• Alignment with data initiatives

• Privacy and security

• Central, distributed or external?

Indu

stria

lizat

ion • Organizational

implementation• Primary business

functions aligned

• Supply / demand process

• Capability planning

• Recruitment and partnering

Current focus ofmost organizations

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Agile organizations

Spotify:

ING:

https://www.youtube.com/watch?v=Mpsn3WaI_4k (1 of 2)https://www.youtube.com/watch?v=X3rGdmoTjDc (2 of 2)

https://m.youtube.com/watch?v=NcB0ZKWAPA0&feature=youtu.be15

Content

1. Society2. Technology3. Organization4. Ecosystems5. Conclusions

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The new deal on data?

Privacy: the new deal on data

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Platform thinking & Edge analytics

10,000 tweets on motorways in Jan. & Feb. 2013

Weather radar

Characteristic transition pointtraffic jams

Vehicle intensity vs density in 2013:dry vs wet road

Predicted vehicle intensity

Platform thinking in Harvard Business Review:https://hbr.org/2013/01/three-elements-of-a-successful-platform

http://artofgears.com/2015/09/08/this-one-trick-in-carmel-indiana-lowered-traffic-injury-accidents-by-80

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Shared value with Big Data

http://www.visaeurope.com/en/newsroom/news/articles/2010/validsoft_fraud_solution.aspx

http://www.confused.com/car-insurance/black-box

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Content

1. Society2. Technology3. Organization4. Ecosystems5. Conclusions

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Future accountants audit analytics

Accountants: 95%

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Maybe trust is overrated

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