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Slides from Coradix Executive Breakfast on Open Government and Open Data - Ottawa, Aug 16, 2012
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Open Government Open Data and Data Management
• We acknowledge that people all around the world are demanding more openness in government;
• We accept responsibility for seizing this moment to strengthen our commitments to promote transparency;
• We accept responsibility to harness the power of new technologies;
• We uphold the value of openness in our engagement with ci9zens
The Open Data Landscape
DOB: 2009 Thank-you Obama
Ac9on Plan on Open Government
Source: Treasury Board of Canada Secretariat
Tony Clement
“Data is Canada’s new Natural Resource” Winnipeg Free Press, July 12, 2012
Canadians Government
Opp
ortu
nitie
s / B
enef
its
Cha
lleng
es /
Ris
ks
Gov’t Accountability
Citizen / Industry Participation
Economic Innovation
Loss of Revenue from Data
Quality Issues
Unable to explain contextual questions
Cost/Capacity of Provisioning Data
Decisions compromised by relying on erroneous data
Misinterpretation of Data
Privacy rights compromised
Elimination of effort and cost responding to ad-hoc requests
Timely Access to Quality Data
Royalties from commercial exploitation of liberated data
Lack of consistency of standards- architecture, meta-data, delivery
National / Individual Security
Lack of skills to manipulate / understand (ie non-tech savvy)
Neighborhood Knowledge Los Angeles (http://nkla.ucla.edu) (NKLA) is a website dedicated to providing public access to vital data and information for neighborhood improvement in Los Angeles.
Digital Economy
“The total size of digital economy is es=mated at $20.4 trillion, equivalent to roughly 13.8% of all sales flowing through the world economy.” Source: The New Digital Economy How it will transform business, Oxford Economics
Source: McKinsey Global Institute, Big Data Next Frontier for Innovation
Volume of Data
Velocity of Data
“Big Brother is Watching”
Variety of Data
Data Analysis
Source: AIIM Industry Watch, Big Data
Content Management
Source: AIIM Industry Watch, Big Data
Implica9ons
• Governance • Insight/Analy9cs • Privacy • Discoverability • Security • IP
Does Big Data = More Technology?
Big Data Challenges
• Source: AIIM Industry Watch, Big Data
Source: AIIM Industry Watch, Big Data
They set out to buy this
And this is what they got
Data Quality Problems are not
cheap
The ERP Experience
Costs
Peopleso[ Anyone ?
Peopleso[ Anyone ?
Tank, Tanks, Tankers, Tanked
Legal Challenges
Result
This hits close to home
Professional Data Management is new
Data Blueprint – Na9onal Cancer Ins9tute Re-‐architec9ng Data
Data Management Planning Online
State of Colorado • How Data Management Improved
– The EDM program helped facilitate much greater communica=on between business and IT
– A robust Governance process and commiOee structure was established – A set of Data Principles were developed and accepted – Specific ini=a=ves were undertaken in the areas of Master Data, Architecture
and Meta-‐data • How the Business Issue was addressed
– Colorado Unique Personal Iden=fier (CUPID) MDM program generated benefits in quality, sharing, understanding, security and stewardship
– Educa=on Longitudinal Data System Architecture ini=a=ve reduced the gaps in school readiness and academic achievement between popula=ons of children
– Improved client-‐service through access to integrated health informa=on – Improved policy making through a more informed process
Recognize this ?
Architectural Bubble Chart
Enterprise Architecture
John Zachman’s Seminal article in 1987 launched Enterprise Architecture
W3C Linking Open Data
DBpedia A community-based
effort structure Wikipedia
Semantic techniques extend this
to structured models
For Against
• "Data belong to the human race” • Public money was used to fund the
work and so it should be universally available.
• It was created by or at a government ins=tu=on
• Facts cannot legally be copyrighted. • Sponsors of research do not get full
value unless the resul=ng data are freely available.
• Data are required for the smooth process of running communal human ac=vi=es (map data, public ins=tu=ons).
• In scien=fic research, the rate of discovery is accelerated by beOer access to data.
• Government funding may not be used to duplicate or challenge the ac=vi=es of the private sector
• Governments have to be accountable for the efficient use of taxpayer's money: If public funds are used to aggregate the data and if the data will bring commercial (private) benefits to only a small number of users, the users should reimburse governments for the cost of providing the data.
• The government gives specific legi=macy for certain organisa=ons to recover costs (Stats Canada)
• Privacy concerns may require that access to data is limited to specific users or to sub-‐sets of the data.
• Collec=ng, 'cleaning', managing and dissemina=ng data are typically labour-‐ and/or cost-‐intensive processes -‐ whoever provides these services should receive fair remunera=on for providing those services.
• O]en, targeted end-‐users cannot use the data without addi=onal processing (analysis, apps etc.)
Canadians Government
Opp
ortu
nitie
s / B
enef
its
Cha
lleng
es /
Ris
ks
Gov’t Accountability
Citizen / Industry Participation
Economic Innovation
Loss of Revenue from Data
Quality Issues
Unable to explain contextual questions
Cost/Capacity of Provisioning Data
Decisions compromised by relying on erroneous data
Misinterpretation of Data
Privacy rights compromised
Elimination of effort and cost responding to ad-hoc requests
Timely Access to Quality Data
Royalties from commercial exploitation of liberated data
Lack of consistency of standards- architecture, meta-data, delivery
National / Individual Security
Lack of skills to manipulate / understand (ie non-tech savvy)
Addressing the Challenges, Realizing the Opportunity Quality Issues
Can’t address contextual questions
Cost/Capacity of Provisioning Data
Decisions compromised by relying on erroneous data
Misinterpretation of Data
Elimination of effort and cost responding to ad-hoc requests
Timely Access to Quality Data
Lack of consistency of standards- architecture, meta-data, delivery
Privacy rights compromised National / Individual Security
Lack of skills to manipulate / understand
Loss of Revenue from Data
Enterprise Data Security
EDM Competency Center
Open Data Delivery Platform
Data Quality Management
Royalties from commercial exploitation of liberated data
Meta-Data Management
Master-Data Management
Citizen / Industry Participation
Economic Innovation
Data Architecture
EDM Governance