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Data & The City - Sander Klous - ADS UvA KPMG

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Data & The City
Big Data, Open Data, Deep Data,
Data Analytics, Business Intelligence, Dashboards, Monitors

8:30-18:00 uur
maandag 3 oktober 2016
Raadzaal Stadhuis Amstel 1 Amsterdam

#amsboda1 #data #bigdata #opendata #deepdata
@AmsTechCity (Twitter Instagram Facebook)
Amsterdam Tech City (YouTube MeetUp)

PROGRAMMA
08:30 – 09:00 inloop & inschrijving
09:00 – 09:15 Gemeente Amsterdam opening Nuray Gokalp
09:15 – 09:30 Gemeente Amsterdam DataLab Berent Daan
09:30 – 09:45 Gemeente Amsterdam CTO Office Tamas Erkelens
09:45 – 10:00 Gemeente Amsterdam Bedrijfsvoering AMI Lieke van Bers
10:00 – 10:15 Gemeente Amsterdam Ruimte OOV Eric Aart & Kees Rooij
10:15 – 10:30 Gemeente Amsterdam Sociaal GGD Thijs Houtenbos

10:30 – 11:00 pauze
11:00 – 11:15 Gemeente Amsterdam Dienstverlening Mellijn Hartman
11:15 – 11:30 Gemeente Amsterdam Veranderkunde Ingmar Kappers
11:30 – 11:45 Brandweer Amsterdam Amstelland Guido Legemaate
11:45 – 12:00 Vital 10 Roderick
12:00 – 12:15 ADS + UvA + KPMG Sander Klous
12:15 – 12:30 Scyfer Jorgen Sandig

12:30 – 13:30 lunch
13:30 – 13:45 Omnichannel.store Roger Olivieira
13:45 – 14:00 IceMobile Floor Wijnen + Guido Jetten
14:00 – 14:15 Xomnia Martijn Imrich
14:15 – 14:30 Ynformed Martijn Minderhoud
14:30 – 14:45 Open State Foundation Tom Kunzler
14:45 – 15:00 Teamily Maarten Lens-Fitzgerald

15:00 – 15:30 pauze
15:30 – 15:45 ADS + UvA + FD + MIcompany Martin Heijnsbroek
15:45 – 16:00 KLM Leon Gommans
16:00 – 16:15 ING Jos Schenning
16:15 – 16:30 GoDataDriven Rob Dielemans
16:30 – 16:45 Teradata Natalino Busa
16:45 – 17:00 Dell EMC Kenny Pool
17:00 – 17:15 VR Base Launch Daan Kip
17:15 – 18:00 borrel

WAT?
Big Data, Open Data, Deep Data, Data Analytics technologie voor lokale overheid onderzoeken.
Samen met nationale en internationale experts de mogelijkheden en onmogelijkheden verkennen voor de Gemeente Amsterdam.

HOE?
Koppelen van maatschappelijke vraagstukken en oplossingen. Vraagstukken worden gepresenteerd door ambtenaren van de Gemeente Amsterdam. Oplossingen worden gepresenteerd door experts van andere overheidsinstellingen, kennisinstellingen, stichtingen, bedrijfsleven, grote bedrijven, MKB, startups, scaleups en freelancers. Presentaties zijn blokken van 15 minuten, waarvan 10 minuten presenteren aan publiek en 5 minuten interactieve vraag & antwoord.

WIE?
• Ambtenaren van de gemeente Amsterdam en overheidsinstellingen
• Experts op het gebied van big data, open data, deep data, analytics…
• Kennisinstellingen (universiteiten, hogescholen, etc.)
• Stichtingen, NGO’s
• Bedrijfsleven
• Startups & Scaleups
• Freelancers
(50% overheid + 50% “rest”)

CONTACT
Nuray Gokalp
Mobiel +31(0)6-46068985
Email n.gokalp@amsterdam.nl
Twitter & Instagram @NurayG
LinkedIn NurayGokalp
Skype Nuray.Gokalp

Published in: Technology
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Data & The City - Sander Klous - ADS UvA KPMG

  1. 1. Trusted Analytics The future of our information society prof. dr. Sander Klous Big Data Ecosystems in Business and Society University of Amsterdam Managing Director Big Data Analytics KPMG Advisory klous.sander@kpmg.nl @sanderklous http://nl.linkedin.com/in/sanderklous
  2. 2. Extreme expectations https://www.youtube.com/watch?v=2vXyx_qG6mQ 2
  3. 3. 3
  4. 4. Content 1. Technology 2. Organization 3. Reliability 4. Trust 5. Ethics 6. Ecosystems 4
  5. 5. Data lakes & The cloud Data Architecture Deployment ArchitecturePlatform Architecture Data Lake Data Lake Data Lake Prioritize PrioritizeSources MDM DWH Experiment MDM BIResultsSources DataLakes Experiment BI Results 5
  6. 6. Field labsInspiration • What is your current status? • What decisions are suboptimal? • How can they be improved? • Experiment selection Incubation • 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? Implementation • Business value generation • Integration into production environment • Alignment with data initiatives • Privacy and security • Central, distributed or external? Industrialization • Organizational implementation • Primary business functions aligned • Supply / demand process • Capability planning • Recruitment and partnering Current focus of most organizations 6
  7. 7. Content 1. Technology 2. Organization 3. Reliability 4. Trust 5. Ethics 6. Ecosystems 7
  8. 8. 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.be 8
  9. 9. Data driven decisions 1. 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 Partner eco-system Performance Management 1 2 3 4 5 6 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 9
  10. 10. Content 1. Technology 2. Organization 3. Reliability 4. Trust 5. Ethics 6. Ecosystems 10
  11. 11. 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 B P(Hn|E) = P(E|Hn)P(Hn) Sum1 N (P(E|Hn)) 11
  12. 12. 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 SymmetricAsymmetric Blue line: financially healthy clients Red line: clients from Fin. Health Dep. Unknown asymmetric systematically error •Example: Tidal effects in the lake of Geneva The TGV on the train track near CERN •Solution to get a more accurate results: More data from different sources 12
  13. 13. 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 ESKAPADE 13
  14. 14. Content 1. Technology 2. Organization 3. Reliability 4. Trust 5. Ethics 6. Ecosystems 14
  15. 15. Privacy versus Safety / ConveniencePrivacy Safety 15
  16. 16. Smart cities & living labs 16
  17. 17. Content 1. Technology 2. Organization 3. Reliability 4. Trust 5. Ethics 6. Ecosystems 17
  18. 18. Systems determine our behavior 18
  19. 19. Future accountants audit analytics Accountants: 95% 19
  20. 20. Content 1. Technology 2. Organization 3. Reliability 4. Trust 5. Ethics 6. Ecosystems 20
  21. 21. Platform thinking & Edge analytics 10,000 tweets on motorways in Jan. & Feb. 2013 Weather radar Characteristic transition point traffic 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 21
  22. 22. Maybe trust is overrated 22

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