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Open Data
Challenges
Paul Stone, NZ Open Government Data Programme,
Data Leadership and Capability, Stats NZ.
ODC Implementation Working Group, November 2019
Quality
Some data is better than
no data…
1. Good context
• Purpose of collection
• Method of collection
• Known strengths and
weaknesses
2. Opportunity for
feedback
3. Creative Commons
Licence
Image: Quality by Nick Youngson CC BY-SA 3.0 Alpha Stock Images
Accurac
y
• Context – inform how data is
collected and for what purpose.
• Be upfront about limitations in
the data or risk of human error
• Create a feedback loop - invite
data users to contribute and help
lift the accuracy of the data
Creative Commons
Licence – not liable for
quality
THE LICENSOR OFFERS THE LICENSED MATERIAL AS-IS AND
AS-AVAILABLE, AND MAKES NO REPRESENTATIONS OR
WARRANTIES OF ANY KIND CONCERNING THE LICENSED
MATERIAL… THIS INCLUDES… FITNESS FOR A PARTICULAR
PURPOSE…
Ignorant misuse
Context, context, context.
• Describe the collection
method and purpose
• Describe well the
variables in the data and
what they mean
• Don’t assume everyone is
ignorant.
Malicious
misuse Don’t let one bad
potential use prevent
the opportunity for
many good uses
Further clause on
liability in CC licence.
Creative Commons
Licence – not liable for
reuse (misuse)
…IN NO EVENT WILL THE LICENSOR BE LIABLE TO YOU ON
…DAMAGES ARISING OUT OF THIS PUBLIC LICENSE OR USE
OF THE LICENSED MATERIAL…
Reputation
• Transparency builds trust
• Good context reduces
misunderstanding
• An opportunity to contribute to
data quality is an opportunity to
engage and build relationships
All good reasons to release the
data – with good context.
Lack of resources
• Open by design
• Prioritise
o Alignment to organisational
strategy
o demand
• Don’t re-invent the wheel
• Start small
Lack of technology
• Keep it simple
• Start small
• Every agency has a website
• Most agencies have API
capability but just don’t use it
Privacy
Critical for maintaining
trust.
Toolbox:
• Privacy Impact Assessment
• Confidentialisation
• Reference Scenarios
(coming)
Culture
Probably the biggest challenge.
Shifting the mindset of the whole
organisation to open by default.
Challenges are manageable, not
insurmountable.
Key messages – like “we are
custodians of a public data asset”
Doesn’t AI make too
much data
dangerous?
Data considerations
Provenance
and
context
Indigenous
Data
Sovereignty
Data EthicsData Bias
Licensing
attribution
Don’t spray and walk away…
Twitter: @opendatanz
opendata@stats.govt.nz
Paul.stone@stats.govt.nz
Data.govt.nz
Contact

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