Slides from Coradix Executive Breakfast on Open Government and Open Data - Ottawa, Aug 16, 2012

1. Open
Government
Open
Data
and
Data
Management

2. • 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

3. The
Open
Data
Landscape
DOB: 2009
Thank-you
Obama

4. Ac9on
Plan
on
Open
Government
Source: Treasury Board of Canada Secretariat

5. Tony
Clement
“Data is
Canada’s new
Natural
Resource”
Winnipeg Free Press, July 12, 2012

6. Canadians Government
Citizen / Industry
Participation
Opportunities / Benefits
Timely Access to Elimination of effort and cost
Quality Data responding to ad-hoc requests
Economic
Innovation
Royalties from commercial
Gov’t Accountability
exploitation of liberated data
Loss of Revenue from Data
Privacy rights compromised
Cost/Capacity of Provisioning Data
Lack of skills to manipulate /
Challenges / Risks
understand (ie non-tech savvy) Lack of consistency of standards-
architecture, meta-data, delivery
Decisions compromised by
relying on erroneous data Quality Issues
Misinterpretation of Data Unable to explain contextual questions
National / Individual
Security

7. 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.

8. 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

9. Source: McKinsey Global Institute, Big Data Next Frontier for Innovation

10. Volume
of
Data

11. Velocity
of
Data

12. “Big
Brother
is
Watching”

13. Variety
of
Data

14. Data
Analysis
Source: AIIM Industry Watch, Big Data

15. Content
Management
Source: AIIM Industry Watch, Big Data

16. Implica9ons
• Governance
• Insight/Analy9cs
• Privacy
• Discoverability
• Security
• IP

18. Does
Big
Data
=
More
Technology?

19. Big
Data
Challenges
• Source:
AIIM
Industry
Watch,
Big
Data
Source: AIIM Industry Watch, Big Data

21. They
set
out
to
buy
this

22. And
this
is
what
they
got

23. Data
Quality
Problems
are
not
cheap

25. The
ERP
Experience

26. Costs

27. Peopleso[
Anyone
?

28. Peopleso[
Anyone
?

29. Tank,
Tanks,
Tankers,
Tanked

30. Legal
Challenges

31. Result

34. This hits
close to
home

35. Professional
Data
Management
is
new

36. Data
Blueprint
–
Na9onal
Cancer
Ins9tute
Re-­‐architec9ng
Data

37. Data
Management
Planning
Online

38. 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

39. Recognize
this
?
Architectural Bubble Chart

40. Enterprise
Architecture
John Zachman’s Seminal
article in 1987 launched
Enterprise Architecture

41. W3C
Linking
Open
Data

42. DBpedia
A community-based
effort structure
Wikipedia
Semantic
techniques extend this
to structured models

43. For
Against
• "Data
belong
to
the
human
race”
• Government
funding
may
not
be
used
to
duplicate
or
• Public
money
was
used
to
fund
the
challenge
the
ac=vi=es
of
the
private
sector
work
and
so
it
should
be
universally
• Governments
have
to
be
accountable
for
the
efficient
available.
use
of
taxpayer's
money:
If
public
funds
are
used
to
• It
was
created
by
or
at
a
government
aggregate
the
data
and
if
the
data
will
bring
commercial
ins=tu=on
(private)
benefits
to
only
a
small
number
of
users,
the
• Facts
cannot
legally
be
copyrighted.
users
should
reimburse
governments
for
the
cost
of
• Sponsors
of
research
do
not
get
full
providing
the
data.
value
unless
the
resul=ng
data
are
• The
government
gives
specific
legi=macy
for
certain
freely
available.
organisa=ons
to
recover
costs
(Stats
Canada)
• Data
are
required
for
the
smooth
• Privacy
concerns
may
require
that
access
to
data
is
process
of
running
communal
limited
to
specific
users
or
to
sub-­‐sets
of
the
data.
human
ac=vi=es
(map
data,
public
• Collec=ng,
'cleaning',
managing
and
dissemina=ng
data
ins=tu=ons).
are
typically
labour-­‐
and/or
cost-­‐intensive
processes
-­‐
• In
scien=fic
research,
the
rate
of
whoever
provides
these
services
should
receive
fair
discovery
is
accelerated
by
beOer
remunera=on
for
providing
those
services.
access
to
data.
• O]en,
targeted
end-­‐users
cannot
use
the
data
without
addi=onal
processing
(analysis,
apps
etc.)

44. Canadians Government
Citizen / Industry
Participation
Opportunities / Benefits
Timely Access to Elimination of effort and cost
Quality Data responding to ad-hoc requests
Economic
Innovation
Royalties from commercial
Gov’t Accountability
exploitation of liberated data
Loss of Revenue from Data
Privacy rights compromised
Cost/Capacity of Provisioning Data
Lack of skills to manipulate /
Challenges / Risks
understand (ie non-tech savvy) Lack of consistency of standards-
architecture, meta-data, delivery
Decisions compromised by
relying on erroneous data Quality Issues
Misinterpretation of Data Unable to explain contextual questions
National / Individual
Security

45. Addressing
the
Challenges,
Realizing
the
Opportunity
Decisions compromised by Quality Issues
relying on erroneous data
Data Quality Management EDM
Privacy rights compromised Governance
Enterprise Data Security
National / Individual Security
Lack of consistency of standards- Master-Data Management Open Data
architecture, meta-data, delivery Delivery
Misinterpretation of Data Meta-Data Management Platform
Can’t address contextual questions
Data Architecture
Timely Access to
Lack of skills to Quality Data
manipulate / understand EDM Competency Center
Loss of Revenue Cost/Capacity of Citizen / Industry
from Data Provisioning Data Participation
Royalties from commercial Elimination of effort and cost Economic Innovation
exploitation of liberated data responding to ad-hoc requests