data & the city - sander klous - ads uva kpmg
TRANSCRIPT
Trusted Analytics
The future of our information society
prof. dr. Sander KlousBig Data Ecosystems in Business and SocietyUniversity of AmsterdamManaging Director Big Data AnalyticsKPMG [email protected]@sanderkloushttp://nl.linkedin.com/in/sanderklous
Extreme expectations
https://www.youtube.com/watch?v=2vXyx_qG6mQ
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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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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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