THEORY OF CHANGE NARRATIVE

THEORY OF CHANGE

NARRATIVE

The Global Partnership for

Sustainable Development Data

October 2019

GPSDD | Theory of Change Narraive | 2

Introducion

This document delineates the Global Partnership

for Sustainable Development Data’s (GPSDD)

Theory of Change (ToC) Narrative. It represents

the GPSDD in 2019, building on lessons learned

since GPSDD’s launch in 2015 and is designed to

relect the GPSDD’s Five Year Strategy for 2019

– 2023. The intenion is to re-visit this narraive

periodically as the GPSDD coninues to grow and

evolve and update it as necessary.

GPSDD’s theory of change explains how GPSDD’s

aciviies are intended to produce a series of results

that contribute to achieving the inal intended

impacts. GPSDD is a partnership and network and

by design aims to achieve change by bringing to-

gether stakeholders and organizaions to address

ideniied and emerging data for development

issues. The ToC is not intended to provide a def-

iniive intervenion logic for all the work GPSDD

supports – this is not possible, given the entre-

preneurial brokering role which GPSDD plays,

meaning that it is not possible to deine in advance

exactly what intervenions will be designed and

supported, and how partners within the network

may engage in pracice.

Instead, the ToC provides a conceptual framework

within which this work is understood to take place;

seing out the most important elements (i.e. levers

of change, contribuions/inputs from the network)

and how these will be acivated by GPSDD’s work,

together with the key assumpions associated

between diferent levels of the intervenion logic

(e.g. output – outcome) and between diferent

elements within the same level (e.g. between levers

of change).

The ToC explains how GPSDD expects to drive

beter decisions and beter lives. In summary,

GPSDD’s causal logic is that some combinaion

of outputs:

• Partnerships (i.e: Ghana mobile data; ARDC)

• increased visibility for data for development

leaders (i.e.: Data for Development Fesival;

UNGA side events),

• engagement mechanisms (i.e.:

Interoperability Collaboraive; listservs) , and

• knowledge building and sharing (i.e.: CGD

Guide; peer-to-peer exchange)

will contribute to:

1. Scaling new technologies and data sources

2. The mobilizaion of a global movement of

poliical, business, and civil society leaders

promoing responsible data use

3. The embedding of standards of data

interoperability into global frameworks on

data and staisics;

These three combined will contribute to more

and beter data being used to achieve the SDGS.

Alongside these expected changes, some

combinaion of the four outputs will also contribute

to the use of imely and robust data being scaled,

which will contribute to more and beter data being

used to monitor the SDGs.

GPSDD | Theory of Change Narraive | 3

GPSDD Context

As noted in the A World That Counts report,

there is broad recogniion that the Sustainable

Development Goals (SDGs) will require integrated

acion on social, environmental and economic

challenges, with a focus on inclusive, paricipatory

development that leaves no one behind. The

report further sipulates that “this in turn will

require another signiicant increase in the data

and informaion that is available to individuals,

governments, civil society, companies and

internaional organizaions to plan, monitor and be

held accountable for their acions. A huge increase

in the capacity of many governments, insituions

and individuals will be needed to deliver and use

this data.

”

This report, commissioned by the UN Secretary-

General recommended that a novel partnership be

forged among diverse stakeholders to ignite “a data

revoluion for the SDGs.” In order to ill the ideniied

gap a wide-ranging group of governments,

companies, and civil society organizaions came

together to launch the Global Partnership for

Sustainable Development Data (GPSDD). What

started with informal conversaions, networking,

and shared ideals, has now grown to more than

200 partners working together to spur new acion

and partnerships to drive data innovaion for

the SDGs. These include the private sector, civil

society, internaional organizaions, academia,

foundaions, and governments.

By 2023, we could be well on the way towards a more

open, collaboraive and equal data system at global

undatarevoluion.org/wp-content/uploads/2014/12/A-World-That-Counts2.pdf, pg. 4

and naional level, where the knowledge that we

produce every minute of the day is used in the best

way to solve the problems we face. Governments,

civil society organizaions and companies could

have access to reliable and up to date informaion

from many sources, to inform decisions and track

progress. People could have conidence that data

would be used to help and not harm them, and

be able to disinguish between data that tells the

truth and data that is used to misinform. And, at

the halfway point of the Sustainable Development

Goals, we could be able to track progress, idenify

what is working, and make sure that all resources

are used to the best efect to take us to the inal

goal of 2030 and a sustainable future for us all.

Thanks to the data revoluion, there is more

knowledge available to humanity than ever

before. Advances in technology are creaing data

at unprecedented levels of detail and speed, and,

together with our long-established data systems

and methods are turning the stories of people’s

lives into numbers every minute of every day,

across the globe.

But too oten, narrow insituional interests, lack

of resources and knowledge, lack of trust or lack

of leadership inhibit the efecive use and sharing

of knowledge and the applicaion of the right infor-

maion at the right ime, to solve global problems.

GPSDD’s aciviies are designed to address these

issues. The following secions provide a descripion

of how GPSDD expects to contribute to achieving

the change described above.

GPSDD | Theory of Change Narraive | 4

GOAL

Drive beter decisions and beter lives for all by facilitaing the producion,

sharing and use of beter data

OBJECTIVE 1

More and beter data is used

to achieve the SDGs

LEVERS OF CHANGE

IR 1.1

New technologies and

data sources have

been brought to scale

IR 1.3

Standards of interoper-

ability are embedded

into frameworks on

data and staisics

IR 1.2

A global movement

on data has been

mobilized

IR 2.1

Use of imely and robust

data for SDG monitoring

is insituionalized

OBJECTIVE 2

More and beter data is used

to monitor the SDGs

Developing

learnings

Creaing

incenives

Supporing

change

makers

PARTNER

CONTRIBUTIONS

Resources

Knowledge

Data

Skills

OUTPUT 3

Mechanisms for

engagement

created

OUTPUT 2

Increased

visibility to

leaders in

the ield

OUTPUT 4

Knowledge

and experise is

catalysed, shared,

aggregated and

ampliied

OUTPUT 1

Partnerships

established and

supported

S, R

Theory of Change Diagram

GPSDD | Theory of Change Narraive | 5

Hypotheses of Change

GPSDD’s theory of change is based on the

fundamental logic that change is dynamic and

non-linear. Given the facilitaive nature of GPSDD’s

work, the change pathways from outputs to

outcomes are dependent on a combinaion of

mutually reinforcing and someimes overlapping

aciviies categorized as levers of change

(supporing changemakers, creaing incenives,

and developing learnings) and contribuions from

partners (skills, data, knowledge, resources).

The iniiaives or intervenions which GPSDD

supports will contribute to change at the outcome

(intermediate result) level by supporing aciviies

within one or more outputs that leverage one

or more of the levers of change and crowd in (or

acivate) contribuions or inputs from the partners.

Levers of Change

• Supporing Changemakers: We establish and

support partnerships that help individuals

and organizaions achieve their objecives

in strengthening enabling policies and data

ecosystems.

• Creaing Incenives: We use our

communicaions and advocacy experise to

provide visibility to leaders in the ield, create

mechanisms for engagement, and build

coaliions for change, to promote innovaion

and investment in data.

• Developing Learnings: We share, aggregate,

and amplify our network’s knowledge and

experise, so all partners can learn and show

what can be done and how to do it.

Contribuions from Partners

The efeciveness of the network is deined by

the partners and their engagement. As a facil-

itator, GPSDD connects demand and supply of

skills, data, knowledge and resources to strengthen

the data ecosystem.

• Data refers to a variety of types of data

(mobile, satellite, etc.) as well as datasets.

• Skills refers to technical experise on tools,

methodologies, and systems that builds

capacity.

• Knowledge refers to informaion in a variety

of forms (papers, webinars, discussions,

etc.) that support individual and collecive

learning.

• Resources refers primarily to inancial

investments, but also includes ime and

personnel investments to a deined data

objecive.

The three levers of change are mutually reinforcing

sets of aciviies, that are made possible by and

build on partner contribuions to produce each

of the four outputs, and in various permutaions

contribute to outcomes level changes.

For example (shown by the

purple pathway in the ToC

diagram), an iniiaive design-

ed to support the coninued

development and uptake of

the interoperability guide

might involve: a) establishing

and supporing a partnership

to work on and develop such

a guide (Output 1) and at

the same ime engaging

with change makers at the

poliical level to socialize

the guide (Lever of Change

1), b) promoing the guide

and increasing its visibility

(Output 2) and inluencing the

development of incenives that

encourage uptake (Lever of Change 2), and c) supporing the use of the guide in target

countries, sharing experiences and encouraging uptake elsewhere (Output 3) through

developing learning and sharing this throughout the partnership (Lever of Change 3).

Ulimately, through working on the iniiaive in this way, the GPSDD expects to posiively

contribute to Intermediate Result 1.3: Standards of interoperability are embedded.

Throughout the process described, partners contribute to the various aciviies by providing

skills, data, resources, and knowledge.

Change is also achieved through mutually reinforcing and iteraive aciviies harnessing

muliple levers of change that lead to more than one outcome. For example, aciviies

designed to support use of the Africa Regional Data Cube might involve supporing

changemakers develop insituional and governance structures (Output 1 and Lever of

Change 1) while sharing knowledge of the data cube capabiliies (Output 4) and building

poliical buy-in to adopt non-tradiional data sources (Lever of Change 2). However, in

order to maintain and increase buy-in and policy relevant use, GPSDD may support ongoing

and addiional trainings (Output 4) and opportuniies to showcase outputs (Output 2 and

Lever of Change 2) that incenivize more stakeholders to use the data cube and contribute to

Intermediate Result 1.1: New technologies and data sources being scale and Intermediate

Results 2.1: The use of timely and robust data for SDGs monitoring being scaled.

GPSDD | Theory of Change Narraive | 6

GOAL

Drive beter decisions and beter lives for all by facilitaing the producion,

sharing and use of beter data

OBJECTIVE 1

More and beter data is used

to achieve the SDGs

LEVERS OF CHANGE

IR 1.1

New technologies and

data sources have

been brought to scale

IR 1.3

Standards of interoper-

ability are embedded

into frameworks on

data and staisics

IR 1.2

A global movement

on data has been

mobilized

IR 2.1

Use of imely and robust

data for SDG monitoring

is insituionalized

OBJECTIVE 2

More and beter data is used

to monitor the SDGs

Developing

learnings

Creaing

incenives

Supporing

change

makers

PARTNER

CONTRIBUTIONS

Resources

Knowledge

Data

Skills

OUTPUT 3

Mechanisms for

engagement

created

OUTPUT 2

Increased

visibility to

leaders in

the ield

OUTPUT 4

Knowledge

and experise is

catalysed, shared,

aggregated and

ampliied

OUTPUT 1

Partnerships

established and

supported

S, R

Theory of Change Diagram (partial)

GPSDD | Theory of Change Narraive | 7

GOAL

Drive beter decisions and beter lives for all by facilitaing the producion,

sharing and use of beter data

OBJECTIVE 1

More and beter data is used

to achieve the SDGs

LEVERS OF CHANGE

IR 1.1

New technologies and

data sources have

been brought to scale

IR 1.3

Standards of interoper-

ability are embedded

into frameworks on

data and staisics

IR 1.2

A global movement

on data has been

mobilized

IR 2.1

Use of imely and robust

data for SDG monitoring

is insituionalized

OBJECTIVE 2

More and beter data is used

to monitor the SDGs

Developing

learnings

Creaing

incenives

Supporing

change

makers

PARTNER

CONTRIBUTIONS

Resources

Knowledge

Data

Skills

OUTPUT 3

Mechanisms for

engagement

created

OUTPUT 2

Increased

visibility to

leaders in

the ield

OUTPUT 4

Knowledge

and experise is

catalysed, shared,

aggregated and

ampliied

OUTPUT 1

Partnerships

established and

supported

S, R

Theory of Change Diagram (partial)

GPSDD’s theory of change is that

the outputs will contribute to each

of the outcomes through a dynamic

process that involves one or more

of the levers of change and one

some combinaion of ideniied

contribuions from partners. The

outcomes achieved through the

dynamic change process described

above, contribute to two objecives:

1. More and beter data is used to

achieve the SDGs

2. More and beter data is used to

monitor the SDGs.

Objecive 2 is in theory a subset of objecive 1 given that monitoring the SDGs is a component of successfully

implemening and achieving the SDGs. However, for the sake of clarity and ease of communicaion, it is

presented as a disinct objecive. Each of the outcomes contribute to change at the objecive level in the

following ways:

Outcomes to Objecives

• IR 1.1: Scaling new technologies and data sources → Decision-makers have the data they need available

to idenify development needs and inform policy decisions → More and beter data being used to achieve

the SDGs

• IR 1.2: A global movement of poliical, business, and civil society leaders → Increased interest in, publicity

for, and commitment to data for development among inluenial leaders → Promoing responsible data

use and build public trust → More and beter data being used to achieve the SDGs

• IR 1.3: Embedding standards of interoperability into global frameworks on data and staisics → Enabling

environment including guidelines and examples on the processes and beneits of joining up data

systems → Prioriizing eiciencies across data systems is the norm → More and beter data being used

to achieve the SDGs

• IR 2.1: Scaling the use of imely and robust data → Data users have access to good quality and current

data relevant to the SDGs → Provide accurate and imely updates on progress to decision-makers →

More and beter data being used to monitor the SDGs

Objecives to Goal

The use of more and beter data will contribute to beter decisions because decisions will be based on data

that is more reliable, comprehensive, and imely. In addiion, more and beter data will contribute to beter

lives for all through beter decisions being made in relaion to monitoring and achieving the SDGs. The

SDGS represent the internaional consensus on beter lives for all. As noted above, monitoring the SDGS

(objecive 2) directly contributes to achieving the SDGs (objecive 1) through imely, robust data on the SDG

indicators. A feedback loop between the two objecives ensures that the development of naional, regional,

and global level data ecosystem enabling environments, including poliical commitment, contributes to the

producion of imely SDG indicator data that further supports achievement of the SDGs.

GPSDD | Theory of Change Narraive | 8

Assumpions

Assumpions Between Outputs and Outcomes

• It is necessary to have both technical and poliical components to advance data for development and

aciviies must target both simultaneously and iteraively to achieve change at the outcome level. In

order to build awareness of, access to, and capacity to use more and beter data, it is necessary to have

an enabling poliical environment that supports changemakers’ ability to invest ime and resources.

Showcasing examples of technical products leads to poliical leaders being incenivized to engage and

support investments in data.

• Partner engagement and contribuions of data, skills, knowledge, and resources are necessary

to operaionalize the three levers of change and achieve each of the outputs and contribute to the

outcomes. The following are a set of assumpions about partners and their engagement with each other

and the partnership as a whole that contributes to achieving the outcomes. Depending on the nature

of partners and the speciic engagement, a combinaion of some or all of these elements needs to be in

place to achieve outcomes:

• Partners perceive that it is possible to form mutually beneicial partnerships among diferent sets

of stakeholders that contribute to producing and using more and beter data

• Governments, donors, and other stakeholders can be persuaded to invest inancially in data for

development

• Partners produce and share data that is relevant to stakeholders

• Partners are willing to learn from each other and can show what can be done and how to do it to

advance data for development

• Focusing on a systems approach is important because simply building new tools or platforms, or

releasing new data, does not lead to sustained change, unless they are embedded within a system

that is incenivized and organized to promote data producion, sharing and use. A system, and most

paricularly a government system, where people and insituions can clearly understand what data

they need, can receive it in the right way, and are then commited to using insights to shape policy

and decisions is the key factor in making the best use of data to solve problems. This means bringing

together technical soluions with poliical and insituional change.

• Scaling is important across space and ime. Inluencing systems is a slow and labor-intensive process.

Scaling successful technologies, products, and processes enables impact beyond partner countries and

organizaions at a faster pace and contributes to achieving the global vision of the SDGs.

GPSDD | Theory of Change Narraive | 9

Assumpions About the Levers of Change

• Unless tools, platforms, and data are embedd-

ed within an ecosystem that incenivizes and

acively enables data producion, sharing,

analysis and use, they will not lead to

sustained change in either public or private

sector. As such, it is important to support

changemakers within exising systems, both

public and private, because evidence suggests

that invesing in data ecosystems is the key to

beter data.

• To safeguard public interests and drive

progress at scale, governments are in the

lead, through their role in producing, sharing

and using data within the naional staisical

system and through the legal and regulatory

frameworks they create to guide the

producion, sharing and use of data by others

in the system. Working together, naional

governments set the global norms, rules and

frameworks which ensure that data works for

all. As such, it is important to create incenives

for change at the naional level.

• Making the data revoluion a force for good

will require partners from inside and outside

governments to work together in new ways,

in the process building the global system that

works for all. Partnership is vital. Bringing

together data producers and data users from

diferent sectors will help them to understand

each other, work together to solve common

problems, and, in the end, produce a more

robust and equitable data ecosystem for the

beneit of all. As such, it is important to build

knowledge to increase the basis for mutually

beneicial partnerships based on shared

understanding of the evidence.

What is a Data Ecosystem?

A data ecosystem consists of actors and processes that are interrelated and interdependent.

Actors include state and non-state stakeholders such as local and naional governments,

private sector, civil society, and academia, encompassing both data producers and

users. Systems and processes such as regulatory frameworks, governance systems,

communicaions mechanisms, and informaion management systems are criical to the

data ecosystem but are self-contained systems that have funcions and implicaions beyond

the ecosystem. The interdependent nature of the ecosystem means that the funcioning

of each of these actors and processes is not only important in and of itself, but criical to

the strengthening of the ecosystem as a whole to ensure that data is efecively produced,

shared, and consumed. Within the context of opimizing the intersecion of data demand

and supply, GPSDD considers governance and leadership, capacity and resources, principles

and standards, and technology, innovaion and analysis to be the four key components of a

naional level data ecosystem.

GPSDD | Theory of Change Narraive | 10

Stakeholder Groups

• Governments indicate their recepiveness to

be inluenced by data and base decisions on

evidence and data-use through their interest

in engaging with GPSDD. Governments are

important stakeholders given their regulatory

power and as eniies responsible for all their

ciizens and delivering on the SDGs.

• Private Sector eniies indicate commitment

to a double or triple botom line through their

interest in engaging with GPSDD. Private

sector stakeholders collect and hold data that

the public does not have access to but can

provide more and beter SDG relevant insights.

• Civil Society organizaions express cii-

zen interests through independent data

producion and advocacy for issue-speciic

data generaion and use addressing the SDGs.

• Academia/Research Organizaions generate

and own data for development knowledge.

In paricular, they foster innovaion and have

the capacity to test robust methodologies and

tools that can be applied to monitoring and

achieving the SDGs.

• Donors/Foundaions have resources to sup-

port the operaionalizaion of data for develop-

ment soluions and through their engagement

with GPSDD indicate their interest in idenify-

ing aciviies and iniiaives to support and

inluence achievement of the SDGs.

Geographic Levels of Change

• Naional – aciviies address sector speciic

data gaps and needs that contribute to

strengthening of the naional level data

ecosystem. Data aciviies use whole of

government and muli-stakeholder ap-

proaches and are aligned to naional prioriies.

• Regional – aciviies address insituional-

izaion and knowledge sharing across peers

• Global – aciviies address inluencing

models of engagement and priority areas for

knowledge building, coaliion building, and

inluencer atenion

GPSDD Network

For the network to be successful, it must be

• Broad – comprising insituions from dif-

erent sectors and regions, and a mix of data

producers and users, large and small players

• Engaged – inding value in their GPSDD

membership and prepared to invest ime and

resources in it to achieve common objecives.

• Open – willing to engage with each other and

overcome challenges to drive progress.

Photo by Ulet Ifansasti/CIFOR