an operational data governance framework for new zealand

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


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


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

10March 2020

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


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


A more complete view of enterprise

Challenge:siloed data

Solution: Enterprise (traditional)

Project OutputBusiness

Unit

Solution: Enterprise (operational)

Bottom Up

Top Down


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


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


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

17March 2020

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


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

19March 2020

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

20March 2020

Acknowledging different perspectives

Stewardshipkaitiakitanga

Asset ManagementTrust/Social License

GovernmentMāori

BusinessConstituents

infr

astr

uct

ure

as

enab

ler


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


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


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


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

26March 2020

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


Route: asset management

Bridging the gaps


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


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

32March 2020

Core data governance capabilities


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)


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

37March 2020

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


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


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


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


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


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


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


Facilitating line of business data accountability

SteadyState n

?

Data inquiry

49

LOB3

?

Information Asset Register

LOB2

?

LOB5

?

Accountable staff Accountable staff Accountable staff

49March 2020

Steady states data flow model

Informing data asset valuation

50March 2020

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


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

52March 2020

IMPLEMENTATION

Piloting operational Data Governance and Steady State data flow mapping

• Central Government

• Local Government

• Māori/iwi


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

57March 2020

For more information...

• [email protected]

• 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

mailto:[email protected]

http://www.data.govt.nz/

mailto:[email protected]

an operational data governance framework for new zealand

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