Trusted Analytics

The future of our information society

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

Extreme expectations

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

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3

Content

1. Technology2. Organization3. Reliability4. Trust5. Ethics6. Ecosystems

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Data lakes & The cloud

Data Architecture Deployment ArchitecturePlatform Architecture

Data Lake Data Lake Data Lake

Prioritize

PrioritizeSources MDM DWH

Experiment MDM

BIResultsSources Data

Lak

es

Experiment BI Results

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Field labsIn

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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Content

1. Technology2. Organization3. Reliability4. Trust5. Ethics6. Ecosystems

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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.be8

Data driven decisions1. Organisation & Governance— Scalable organisation of multidisciplinary data teams,

aligned with related domains— Roles and responsibilities of data-related business and

IT functions— Data management and reporting governance— Data privacy, -security and -quality management

2. Services & processes— Agile processes to grow from idea to

provisioning— Continual model validation and

improvement processes— Structured ideation and prioritization of

business use cases

5. Performance— Support investment decisions using

transparent reporting of effectiveness— Continual improvement through KPI-

based measurement framework— Drive innovation through employee

rewards and incentives

6. People & Skills— Skills & capability planning for data scientists

and business analysts— Training programs and analytical capability

development— Agile skills and culture— Platform & deployment management skills

3. Technology— Process and governance supporting tools— Architecture and life-cycle management tools— Collaboration and planning tools

Organisation &

Governance

Technology

Services & processes

People & Skills

Partnereco-system

Performance Management

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2

3

4

5

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4. Partner eco-system— Collaborative approach to partners— Evolution to incentive based contracts— Sourcing of external models, algorithms and

data sources— Longer term / optional: Joint ventures with

market partners (SPVs)

Analytics Operating

Model

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Content

1. Technology2. Organization3. Reliability4. Trust5. Ethics6. Ecosystems

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Correlation or causality

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 12

Decision support framework

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

ESKAPADE13

Content

1. Technology2. Organization3. Reliability4. Trust5. Ethics6. Ecosystems

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Privacy versus Safety / ConveniencePr

ivac

y

Safety15

Smart cities & living labs

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Content

1. Technology2. Organization3. Reliability4. Trust5. Ethics6. Ecosystems

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Systems determine our behavior

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Future accountants audit analytics

Accountants: 95%

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Content

1. Technology2. Organization3. Reliability4. Trust5. Ethics6. Ecosystems

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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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Maybe trust is overrated

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