An operational Data Governance Framework for New Zealand Government
Kevin SweeneyPrincipal Advisor – Data & Analytics ConsultingStats NZWellington, New [email protected]
March 2020
CONTEXT
Data: Then Data: Now• Limited• Specialist• Collateral
• Ubiquitous• Democratised• Currency• Culture
The world has moved on...
...but our approaches to data infrastructure have not
Da
ta G
ove
rna
nce
fra
mew
ork
3March 2020
4March 2020 An operational Data Governance Framework for New Zealand Government
Information Asset Strategy
Frameworks:Governing, Organising, Analysing, Deploying
DalleMule & Davenport; Harvard Business Review; May-June 2017
“The ‘plumbing’ aspects of data management may not be as sexy as the predictive models and colorful dashboards they produce, but they’re vital to
high performance.”
< 50% Structured data used to make decisions
< 1% Unstructured data used at all
> 70% Employees with access to data they shouldn’t
80% Analysts’ time spend finding and prepping data for use
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Awareness has taken hold...
6March 2020
The Economist Intelligence Unit; 2018Disconnect
Reality: Data governance as compliance mechanism
[risk mitigation]
Potential: Data governance as enabler
[business opportunities]
Opportunistic Governance
Protectionist Governance
In general, governance is about controlling, so data governance is about controlling data.
Tallon, et al. 2013
The full potential of data governance not yet realised
7
SOME KEY CONCEPTS
Data and the Organisational Story
Understanding the role of data in the story
• How/where does data fit in?
• How does data define or shape the story?
• Is there a separate “data story”? Or is it the story?
Recognising story has a highly practical value
• Supports a common vision across diverse roles• Helps the organisation articulate its value to its
constituents or customers• Helps individuals understand their place in the organisation• Influences operational processes, decisions
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Data
P
ote
ntial
Data Lifecycle
Raw data
Analysed data
Managed data
organisational boundary
Shared data
Linked data
Enriched data
Realising and growing the inherent potential of data assets
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A whole of data lifecycle approach
Design Source Collect Store Enrich Analyse Publish Dispose
Data Lifecycle
▪ Stewardship
▪ Accountability
▪ Governance
▪ Management
▪ Quality
Dat
a
“by design”
Operationalising through consistently high
levels ofdata mindfulness
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Striking a balance
common
unique
Risk Management
Enterprise
ConsistentCentralised
Authoritative
Direction Setting
Control
ComplianceStandardised
Individualised
Business
Freedom
DistributedAgile
Responsive
Innovation
Federation
• A source of (useful) business “tension”
• Shifts back and forth over time
• Co-exists across different business units within the same organisation
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A more complete view of enterprise
Challenge:siloed data
Solution: Enterprise (traditional)
Project OutputBusiness
Unit
Solution: Enterprise (operational)
Bottom Up
Top Down
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DRIVERS FOR ANEW APPROACH
Deliver
practical implementation
Acknowledge
low data maturity
Leverage
infrastructure as enabler
Embed
data accountability
Align
data management withbusiness process
Promote
stewardship mindset
Sustain
data quality focusbusiness condition
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Facilitate
integrated government
Within the context of my position, I am accountable for my organisation’srelevant data assets, and as such I have a view of where they residethroughout their lifecycle, a measure of their quality, understand and canprotect against associated risks, am informed of relevant constraints, andpossess knowledge of how they contribute value within my organisationand across the wider system.
This enables my organisation to articulate the conditions (open by design)under which we manage our data assets so they can be made available toothers to re-use for their benefit and to help realise the data’s full potential.
Individual statement (consistent)
Sample organisational statement (varies)
stew
ard
ship
min
dse
t
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Addressing the challenges of a roles-based approach
Avoiding definition inconsistency
Eliminating the Custodian’s burden
Expanding good practice accountability Supporting agile
businessevo
lvin
g d
ata
mat
uri
ty
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The states of data
Increasing value potential
gas
solid
liquid
“bow-tie”
ad hoc centralised dispersed
chaotic controlled managed
acknowledge consolidate channel
data discovery data access data flow
evo
lvin
g d
ata
mat
uri
ty
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from: Global Data Strategy, Ltd
Data Governance
(da
ta-d
rive
n)
Bu
sin
ess
Ou
tco
mes
Alignment
infr
astr
uct
ure
as
enab
ler
infrastructure
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OPERATING ENVIRONMENT
Quality framework
Compliance requirements
Policy
Change management
Procedures
Security protocols
Organisational competencies
Strategy
operationalData Governance
Framework
Analytics and insights
Assurance
Audit
Relationship management
AccountabilityBusiness asset management
infr
astr
uct
ure
as
enab
ler
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Acknowledging different perspectives
Stewardshipkaitiakitanga
Asset ManagementTrust/Social License
GovernmentMāori
BusinessConstituents
infr
astr
uct
ure
as
enab
ler
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Increasing enablement potential
null
ad hoc centralised dispersed
isolated enterprise/top-down distributed/bottom-up
individuals champions everyone
random designated roles capabilities
traditional modeloperational Data Governance Framework
Data Custodian
Data Steward
emb
edd
ing
dat
a ac
cou
nta
bili
ty
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alig
nin
g d
ata
man
agem
ent
wit
h b
usi
nes
s p
roce
ss
Data management
“content”
Business process
“context”
Mindful data management
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A NEW APPROACH
ideas
approach
framework
output
new ways of thinking about
data
holistic data governance
operational Data Governance Framework
steady state data flow
map
A path to new thinking
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An operational Data Governance Framework for Data Stewardship
Data System Stewardship
Beneficial outcomes
Government Priorities
“Nexus”Operational context
operational framework
Accountability
Transparency
Data quality
IntegrationAgile operating
modelsTrust/Social license
Open data
Assurance
Data securityAutomation
Data valuation
Asset knowledge
Asset management
Asset care
Data flowBest practice data
management
Steady states model Core capabilities
Business decision point
framework
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27March 2020 An operational Data Governance Framework for New Zealand Government
Data Governance Capabilities
Individual
Accountability Designation
Line of Business
operational Data Governance
political Governance
Organisation
Data Flow Mapping
Enterprise Structures
Gap: People and Information capabilities
System
System Structures
Data Governance Board
Top
-d
ow
n
Bo
tto
m -
up
NZ Data Advisory GroupNZ Data
Office
LDS
CDO
GCDS
GCDO
A continuum of governance for data
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Route: asset management
Bridging the gaps
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3030
Practice
Direction
Direction
Perspective/
Perspective/
from: ISO-IEC 38505-1-2017 and
ISO-IEC TR 38505-2-2018
Strategy
Data-specific aspects of governance:• Value• Risk• Constraints
“Ca
scad
ing
scoreca
rd”
Holistic data governanceapproach
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ASSET CARE:DATA GOVERNANCE CAPABILITIES
Steward
Custodian
Steward
CustodianData Governance
Capabilities
and Accountability
ceded ceded
Data Governance
Capabilities
and Accountability
Traditional Data Governance Model Proposed Data Governance ModelD
esig
na
ted
Go
vern
an
ce R
ole
sLi
ne
Sta
ff
embeddedembedded
custodianshipstewardship
Evolving with organisational data maturity
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Core data governance capabilities
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Capabilities at theBusiness Decision Point Nexus
Best practice data management
Data ethics
Human resource frameworks
ngā mātāpono o te tiriti/Treaty partner
obligations
Business Decision
Point
Steady State
Data+ Delivery+ Process+ Accountability
[asset][infrastructure][business][agent]
nexus
March 2020 An operational Data Governance Framework for New Zealand Government
ASSET KNOWLEDGE:STEADY STATES DATA FLOW
Why data flow?
❑ Pervasive gap
❑ Easily addressed
❑ Intuitive output
❑ Holistic view
❑ Wide range of benefits (high ROI)
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Steady States: evolved from a production environment value chain context…
+ Value
Data = Quality
“A stable business process output (data + metadata) that satisfies a predefined level of quality.”
- Stats Netherlands
Steady State
Steady State
…to a data-mindful business decision point
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Steady State
measures
criteria
Tolerance• Qualitative (yes / no)• Quantitative: (%)
Steady state decision critera
data quality dimensions
1. Relevance2. Accuracy3. Timeliness4. Accessibility5. Consistency6. Interpretability
enterprise
business unit
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A business decision point flow map
Map
o
bje
ct
lib
rary
SteadyState
n
input output
SteadyStaten+1
data flow
information flow
Verify
Integrate
Aggregate
Analyse/Model
Report
Store
organisational boundary
Output states reflect:• Logical data processes• Physical movements• Format changes
process
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Steady state map settings
Granularity Density Depth Frequency1 2 3 4
2 SSn SSn SSnSSn SSn SSn
4 SSnSSn SSn SSn
1 SSn SSn SSn SSn SSnSSnSSn SSnvs.
3 SSnSSnSSn criteria criteria criteria
Granularity Density Depth Frequency
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Steady state identification
SteadyStaten+1
SteadyState
n
SteadyStaten+2
. . .
• Sequential – downstream direction
• Initiates with steady state zero (SS0)
• Unique ID for each steady state
• Logical
downstream
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Steady state zero (SS0)
SteadyState0
• Definition: that point in the data flow where the data comes into existence, from the perspective of the relevant business unit
• Reflects the point at which data is either:
o collected by the host business unit, oro provided to them by an external entity
• Inherent relationship with the boundary of the business unit’s sphere of influence
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The
stea
dy
stat
e d
ata
flo
w m
ap
output
The
stea
dy
stat
e d
ata
flo
w m
ap
output
output
The
stea
dy
stat
e d
ata
flo
w m
ap
Manual
Satisfaction
Platform
Schedule
BENEFITS
Realise Data Potential
oDGF
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An infrastructure of data flow monitoring stations
Steady
State
Business process/Data lifecycle
entry points:
embedding best practice,
data asset management,
accountability
Business process/Data lifecycle
access points:
maintaining data quality,
monitoring workflow,
transparency
criteria
criteria
measures
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Facilitating line of business data accountability
SteadyState n
?
Data inquiry
49
LOB3
?
Information Asset Register
LOB2
?
LOB5
?
Accountable staff Accountable staff Accountable staff
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Steady states data flow model
Informing data asset valuation
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Addressing the high cost of data sourcing/processing
0
2
4
6
8
10
12
14
16
18
Evaluate SpecifyNeeds
Design Build Collect Process Analyse Disseminate
Data Lifecycle Costs
Labour Other
Collect and Process:45% of total lifecycle costs
GSB
PM
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Capturing the dynamic nature of data qualityLi
ne
of
Bu
sin
ess
-1
Lin
e o
f B
usi
nes
s -
2
Critical data quality dimensions
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IMPLEMENTATION
Piloting operational Data Governance and Steady State data flow mapping
• Central Government
• Local Government
• Māori/iwi
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Explore
Socialise
Investigate
Commit
Incr
easi
ng
in
vest
men
t
Increa
sing
system p
articip
atio
n
Self Service:data.govt.nz
Engagement: internal & community
Characterisedata culture
Targetedaction plan:
Stats NZData & Analytics
Consulting Service
Implementation pathways
55March 2020
data.govt.nz
Published
Document Library Map: operational Data Governance Framework
56March 2020
DataStewardship FrameworkHolistic Data
Governancedata & analytics consulting service
Direction
Perspective/
Perspective/
Direction
operational Data Governance Framework
DataCapability
Hub
Stewardship “toolkit”
System Data Governance;
Māori Data Governance
Govt ChiefData Steward data.govt.nz
Value Proposition Assessment tool (VPAt)
Stewardship support for the data system
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For more information...
• www.data.govt.nz
• Stats NZ website: www.stats.govt.nz
• Sweeney, K.J. (2018) Re-Imagining Data Governance. In Handbook of Research on Knowledge Management for Contemporary Business Environments (pp. 251-278). IGI Global.
58March 2020