Kasper and Christoph explained how to apply data governance, data discovery and data cataloging for 200+ data sources. Also,m about dealing with a connect vs. collect strategy in a centralized hub of data.

1. IT works
October 17th 2019
Kasper Kisjes
Christoph Balduck
RWS Datahub
- journey so far
1

2. 2
Rijkswaterstaat
Ministry of Infra & Water Highways Fairways Water systems
Sustainable environment Protection against floods Sufficient and clean water Quickly & safely from A to B
Cooperating to promote safety, mobility and the quality of life in
the Netherlands. That is the mission of Rijkswaterstaat.

3. 3
Topics:
• Cor Data: stating the problem
• Discovering the field
• Converging to a solution
• Lessons learned
• Q&A

4. 4
Topics:
• Cor Data: stating the problem
• Discovering the field
• Converging to a solution
• Lessons learned
• Q&A

5. 5
Cor Data

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The dream

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The search
Where to
search for
data?
What do
these this
data mean?
How do I get
access?
Can I trust
these data?

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The prototype
Bravo!
Bravo!

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Next step: production
Organizationalresistance
Expensivepointsolutions
storage
barriers

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What Cor would have liked?

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Schematically…

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Main benefits
• Facilitating cross-domain cooperation
• Much faster from data to insight
• Transparency about quality
• Less IT complexity, better security
• The ‘memory’ of Rijkswaterstaat
Urgent requests:
Fast & comprehensive answers
Highway accidents:
Insight & prevention
Bridges, locks, asphalt:
Just-in-time maintenance

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Topics:
• Cor Data: stating the problem
• Discovering the field
• Converging to a solution
• Lessons learned
• Q&A

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Doing our homework

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Consulting the market

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Consulting the market
About your business case…
• Where is the real pain?
• How to define value?
• When to consider it a success?
• Who are the intended users?
• Critical requirements?
• Which are the biggest risks?
What is more important…
• Easy use or max functionality?
• Proven technology, open source,
and/or state of the art?
• Functionality checkmarks or
agility and roadmap?
• One suite or best-of-breed?

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Consulting the market
In or out of scope…
• Data quality?
• Storage of historical data?
• Master data management?
• Streaming data management?
• Organizational change?
• Data preparation?
By the way…
• Reference visits!
• Start with process, not tools!
• Personas!
• PoC/PoV is still a controlled
environment!
• Machine learning!

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Reference visits
Hosts
• Belastingdienst (Dutch tax authority)
• Municipality of Rotterdam
• ABN AMRO
• ASML
• Elia
• KLM

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Reference visits
Hosts
• Belastingdienst (Dutch tax authority)
• Municipality of Rotterdam
• ABN AMRO
• ASML
• Elia
• KLM
Main findings
• Decoupling of data layer is a trend
• Biggest challenge: culture change
• Seek advice for procurement tricks
• Enterprise wide programs:
‘One…’; ‘Data as a service’
• Extensive communication pays off
• Top-level role models
• 100+ sized team not uncommon

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Topics:
• Cor Data: stating the problem
• Discovering the field
• Converging to a solution
• Lessons learned
• Q&A

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Outlining the processes – together

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Personas
BI ADVISOR
DATA OWNER
DATA STEWARD
PLATFORM
MANAGER
SELF-SERVICE
USER
NON-HUMAN
DATA SCIENTIST

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DATA SCIENTIST

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Visualizing the solution

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Datahubs have layers

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What’s next?
Initial
problem
definition
Getting
familiar
with the
field
Market
consul-
tation
Refine
archi-
tecture
& scope
Setup
personas
&
processes
PoV with
different
scenario's
Select
TO/BE
solution
Implement
first phase
Roll out
broadly
Done Planned
Define Investigate Test & select Implement & roll out

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Topics:
• Cor Data: stating the problem
• Discovering the field
• Converging to a solution
• Lessons learned
• Q&A

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Lessons learned
DO:
• Understand thoroughly the data needs in your organization
• Go out and learn from organizations with similar challenges
• Explore the possibilities of new ML and AI technology to
accelerate your data governance initiatives
DON’T:
• Just explore the one tool that you happened to come across
• Underestimate how rapidly this market is evolving – the
winners of three years ago are today’s dinosaurs
• Forget about the cloud

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Topics:
• Cor Data: stating the problem
• Discovering the field
• Converging to a solution
• Lessons learned
• Q&A

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Q&A