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PART III
Moving toward an
integrated national
data system
9. Creating an integrated national data systemCHA
PT
9
E
R
Creating an integrated
national data system
Main messages
1 By building an integrated national data system, countries can realize the
full value of data for development. The system should provide a framework
for the trustworthy, equitable production, flow, and use of data.
2 An integrated data system is built on an approach to data governance
that is intentional, whole-of-government, and multistakeholder. The steps
needed to implement such a system depend on a country’s data maturity.
What works in one context may not work in another.
3 To be sustainable, an integrated national data system must be continually
improved. This will depend on having highly skilled human resources in
government, civil society, academia, and the private sector.
4 Robust data protection is critical to building an integrated national data
system. As the scope of such a system expands, the economic, social, and
development returns increase, as do the data protection requirements.
Creating an integrated national data system | 303Toward an integrated national national data system and should be recognized in
national development strategies.
data system
T
When the foundation of an integrated national
his chapter describes how to create an inte- data system is in place and a variety of participants
grated national data system designed to real- are included in the data life cycle, it can yield vast ben-
ize the potential of data for development. Such efits for development. In fact, the more integrated the
a system relies on an approach to data governance system and the more participants involved, the higher
that is intentional, whole-of-government, multistake is the potential return. If two participants safely
holder, and collaborative. It explicitly builds data pro- exchange data with each other, data can flow in two
duction, protection, exchange, and use into planning directions. If three participants exchange data, data
and decision-making and integrates participants can flow in six directions, and with four participants,
from civil society and the public and private sectors in 12 directions. If data are reused and repurposed,
into the data life cycle and into the governance struc- these connections can increase exponentially. When
tures of the system. Although such a system is related government agencies, civil society, academia, and the
to a national statistical system, it differs in key areas private sector securely take part in a national data
(box 9.1). system, the potential uses of data expand and so does
An integrated national data system is all about the potential development impact. As the scope of the
people. A well-functioning system requires people system expands, so do the data protection require-
to produce, process, and manage high-quality data; ments and the needs for safeguards against misuse.
people to populate the institutions that safeguard An integrated national data system implies that
and protect the data from misuse; and people to draft, all participants and stakeholders collaborate in a
oversee, and implement data strategies, policies, and system in which data are safely produced, exchanged,
regulations. A well-functioning system also requires and used. It does not mean that all data are stored
people to use data as a factor of production in both in an integrated national database. And while such
the public sector—for policy design and implemen- collaboration requires close coordination and shared
tation—and the private sector—for decision-making governance between the participants, it does not nec-
and innovations in products and services. People are essarily require a centralized governance structure.
also needed to hold the public and private sectors For many countries, a system in which high-
accountable. All this requires robust data literacy.1 quality data flow and are used safely among various
Meanwhile, at the end of the day, it is people who participants remains a distant vision. A low-income
will benefit from an integrated national data system. country suffering from high levels of poverty, fra-
They will see better public policies, programs, and ser- gility, and poor governance may struggle to produce
vice delivery; more business opportunities and jobs; even the most fundamental data, let alone set up a
higher market efficiency; and greater accountability. whole-of-government, multistakeholder approach to
It is vital that the public trusts that data are being data governance. Yet keeping sight of this vision mat-
safely stored, exchanged, and used to create value ters for all countries, even those struggling the most
equitably, while protecting against misuse. Thus the with data, because it can serve as a guide in making
social contract for data is built into a well-functioning decisions on how to develop their data systems.
Box 9.1 Relationship between an integrated national data system and
a national statistical system
A national statistical system is an ensemble of units building an integrated national data system is a national
within a country that jointly collect, protect, process, and data strategy, which is a country’s plan for capturing
disseminate official statistics.a As such, a national statis- greater economic and social value from data in line with
tical system is a core part of the more expansive inte- the principles of a social contract for data. By contrast,
grated national data system. The scope of the integrated the blueprint for building a national statistical system is a
national data system goes beyond official statistics to national statistical development strategy, which focuses
encompass the data produced, exchanged, and used by on official statistics.
participants from civil society and the public and pri- a. See the definition of a national statistical system proposed by the Organ-
vate sectors for a variety of purposes. The blueprint for isation for Economic Co-operation and Development (OECD 2002, 220).
304 | World Development Report 2021After envisioning what a well-functioning governance, if their policies address lack of human
national data system might look like in a frictionless capital, trust, proper incentives, funding, and a cul-
world, this chapter extends the use of the data matu- ture of data use, the potential of data for development
rity model of the last four chapters to discuss how can be better realized.
countries can move closer to realizing this vision,
depending on their context and their level of data
The vision of an integrated
maturity. One size will not fit all. Concrete steps to
move closer to the vision will critically depend on
national data system
local factors, many of which are related to political An integrated national data system serves a num-
economy issues, such as the strength of institutions ber of important functions; it incorporates various
and key decision-makers. Another important aspect participants from government, civil society, and the
is the structure of the government: the system will private sector; and it is built on the pillars discussed
look different in a centralized government structure in part II. These pillars rest on a foundation of human
than in a federal one, for example. But even for coun- capital, trust, funding, incentives, and data demand
tries that remain far from the frontier of good data (figure 9.1).
Figure 9.1 What happens in an integrated national data system?
Data are
produced protected open quality controlled used and reused
By
government civil society academic the private international
entities and individuals institutions sector organizations
Built on the pillars of
infrastructure laws and economic institutions
policies regulations policies
Sustained by the foundation of
human capital trust funding incentives data demand
Source: WDR 2021 team.
Creating an integrated national data system | 305Functions of an integrated national well in this regard, the quality control processes must
data system have a high degree of political independence.
An integrated national data system enables the pro-
duction of data relevant to development; the equitable Data use and reuse. The frequent and
and safe flow of data among the participants in the widespread use and reuse of data pro-
system; and their ability to use and reuse the data pel a successful national data system.
while safeguarding against misuse. A critical aspect is the routine use of
data in planning and decision-making across govern-
Data production. A well-functioning ment entities and the use and reuse of data beyond
national data system produces data their original purpose, including in business models
relevant to policy planning, decision- of data-driven companies, in academic research, in
making, and the national discourse. The policy making and policy reform, and in informing
data meet the needs of the various participants and media content and coverage.
cover and represent the population of interest. In line
with a multistakeholder approach to data production, Participants in an integrated national
data produced by private sector entities, civil society data system
organizations, and academia, as well as by citizens, The integrated national data system incorporates
are incorporated where appropriate into the national participants from the three development pathways—
data system to fill in gaps and enable synergies with government and international organizations, indi-
government data. viduals and civil society, and the private sector. This
chapter discusses in more detail five groups of par-
Data protection. To ensure rigorous pro- ticipants: government entities, civil society, academia,
tection of data and sensitive informa- the private sector, and international and regional
tion, secure storage and transfer of data organizations. Although all five groups both produce
and safeguards against misuse are in and use data, each plays a different role in the national
place. This arrangement works as a catalyst for trust data system, which merits separate treatment.
and participation in the system. As the types and
volume of data expand, the producers and users of Government entities. Government entities
data increase, and their interoperability improves, are the primary producers of public
data protection becomes increasingly important intent data for policy and government
for safeguarding the integrity of the system. To the functions, such as by collecting admin-
extent possible, robust data protection is achieved by istrative data through censuses and surveys and
legal and technological solutions before restricting through the national statistical system at large. But
access to data, which is a measure of last resort in an the role of government entities in the national data
integrated national data system. system extends beyond producing data for reporting
and monitoring to exchanging data across entities
Data openness and flow. Open data and and with other participants and using data for policy
interoperability foster the flow of data design and decision-making. Government entities
within government and between the also act as data stewards, setting out the rules that
participants in the national data system. govern data use and ensuring data accessibility and
Common standards enable the exchange of data protection. And they act as data managers, laying out
across government agencies to improve planning and and enforcing quality standards and ensuring secure
decision-making as well as to enable cross-border data data transactions.
flows and collaboration.
Civil society and individuals. Civil society
Data quality control. To safeguard the organizations (CSOs), national and
integrity and quality of the data pro- international nongovernmental organi-
duced, sound methodological founda- zations (NGOs), the media, and individ-
tions in data production and stringent uals play a critical role as the producers and users of
standards for quality control are adopted. Such foun- data that hold governments and the private sector
dations also improve the interoperability and com- accountable and highlight issues of public concern.
parability of data from different sources. To function This accountability function also applies to the
306 | World Development Report 2021production of citizen-generated data that act as a middle-income countries, international organiza-
check on official government data if they are in tions also frequently act as donors to support data
doubt, fill gaps in coverage, or otherwise comple- production. Furthermore, given economies of scale,
ment public intent and private intent data. The data some goals for a national data system may best be
produced by civil society are valuable beyond their tackled at the supranational level (see spotlight 8.1).
primary functions, and their value increases through Regional organizations can be effective mechanisms
wider use and reuse by other participants in the for data governance and creating economies of scale
national data system. in data and statistical capacity.
Academia. Academic institutions, think Pillars of an integrated national
tanks, and research organizations both data system
produce and use data in their research An integrated national data system builds on the
to guide and evaluate policy reforms infrastructure policies, laws and regulations, eco-
through impact evaluations and forecasting, for nomic policies, and institutions outlined in part II of
example, and to inform and influence media and the this Report.
public debate. Academic institutions also provide
important education and training for data users and Infrastructure policies. In a well-functioning
producers in government, the private sector, and civil integrated national data system, hard
society, as well as perform data research and develop- and soft infrastructure policies are
ment functions in the national data system. designed to enable the equitable and
trustworthy production, processing, flow, and use
The private sector. Firms in the private of data (see chapter 5). People have access to the
sector are prolific producers of data for internet and can use it properly, consuming an ade-
their business processes, needs, and quate volume of data. Gaps in access—in terms of
decisions. Some of these data are very coverage, usage, and consumption—are addressed.
valuable to those making public policy and to the These policies enable countries to improve access
public interest (see chapter 3). Thus the private sector to international connectivity, favoring competition
is an important contributor to data production in the along the entire infrastructure supply chain. A proper
national data system, whose data are subject to com- competitive environment facilitates development of
mon standards and quality control. In an integrated the more complex elements of data infrastructure.
national data system, businesses gain from a data- The establishment of internet exchange points (IXPs),
driven culture in terms of competitiveness and prof- which requires a competitive market for internet
itability, and the national data system facilitates the providers, helps create a vibrant digital ecosystem.
transition to such a data-driven culture. Businesses Well-functioning IXPs attract content providers
also routinely rely on public intent data to improve locally and from abroad. The consequent growth of
business decisions and processes or to create new data consumption generates investments from colo-
products and services. cation data centers and cloud providers.
International and regional organizations. Laws and regulations. In an integrated
These groups are de facto players in national data system, laws and regu-
the national data system. Many inter- lations guiding data openness, usage,
national organizations require their and protection are in place (see chapter
members to engage in various types of reporting, 6). Open data laws and access to information legis-
such as progress in meeting the United Nations’ lation complement one another by requiring public
Sustainable Development Goals (SDGs), which institutions to disclose data by default while granting
affects national data production.2 International orga- individuals the right to compel disclosure. The rights
nizations develop methods and tools that cannot be of individuals on the use of their personal data are
produced efficiently at the national level. They also recognized and reserved, and an independent data
commonly act as standard setters. Best practice protection authority safeguards those rights. Data
standards and methods are central to the interna- controllers and processors are held accountable to
tional comparability of the data produced in the ensure cybersecurity. Governments play a steward-
national data system (see spotlight 2.2). In low- and ship role by incorporating soft law around data use
Creating an integrated national data system | 307that reflects societal values, including standards, Human capital. Human capital underpins
terms and conditions of use, norms, codes of conduct, a well-functioning national data system.
and other voluntary frameworks. Both state and non- Data producers have the skills needed to
state participants adhere to this body of soft law (see produce high-quality data that measure
chapter 6). up to best practices and international standards for
processing, storing, and ensuring the interoperability
Economic policies. Executive-level decision- of data. The institutions that safeguard data, perform
makers in both the public and private quality checks, and ensure that data flow among par-
sectors view data as foundational for ticipants are staffed with skilled workers, as are the
creating value and are committed to institutions that lay out the policies, laws, and regula-
implementing policies to maximize the value of data tions governing data flows. Individuals have the data
while ensuring that the proper safeguards are in literacy needed to ensure that data can be used effec-
place. In the whole-of-government strategy for data tively and equitably, to be empowered, and to hold
governance, policies set out norms, objectives, and governments accountable. Data literacy should be
tools. Antitrust tools are adapted to data-driven mar- understood in a broad sense to include understanding
kets, and antitrust authorities tackle anticompetitive basic statistical and numerical concepts; understand-
behavior by data-driven firms. Data can flow securely ing how to analyze, interpret, and communicate data
across borders and facilitate cross-border services using digital tools; understanding the place of data in
transactions, such as in the financial services or decision-making; and understanding data rights and
telecommunications sectors. Tax loopholes for data- data governance essentials. Finally, the next genera-
driven businesses are addressed (see chapter 7). tion of data users is trained in data literacy through
educational curricula, and the next generation of data
Institutions. The institutions required scientists and statisticians is trained through higher
to effectively govern data are in place education, ensuring the sustainability of the national
(see chapter 8). They include those that data system.
enact overarching strategic and policy
objectives, such as an executive-level cross-functional Trust. For data to flow securely within
group of key stakeholders that makes policy deci- the national data system, participants
sions, provides strategic direction, and mobilizes in the system trust that the data will be
the necessary resources. To execute the strategy and protected, that the information inherent
manage the national data system, a repurposed exist- in the data will not be misused, and that the value
ing institution or a newly created data governance created from the data will be shared equitably. People
office is fully operational. A national statistical office trust the ability of government, academia, and the
with sufficient financing, independence, and capac- private sector to collect, protect, and safely share data
ity to fulfill its role is in place. Institutions that mon- gathered from them. Firms trust that their data will
itor compliance, such as a data protection authority be used properly when those data are shared with
and an antitrust authority, are operating. Institutions third parties. And, in general, participants trust that
that monitor and evaluate the system as a whole are the public sector enforcement systems are robust and
created—such as oversight agencies that effectively that appropriate measures will be taken in the event
monitor the accountability of data producers and of data misuse.
users and nongovernment watchdogs that monitor
public and private sector compliance with rules and Funding. A well-functioning national
standards. data system is sufficiently funded. Gov-
ernment agencies have the resources
Foundations of an integrated national to hire and pay highly skilled data sci-
data system entists, statisticians, and data collectors at compet-
Putting these pillars in place for the national data itive levels, as well as the resources to purchase the
system is not easy. They need to be anchored in a technical infrastructure needed to collect, process,
solid foundation of human capital, trust, funding, and manage data. Likewise, government agencies
incentives, and data demand. Trust in particular have the funding needed to achieve the goals set out
plays a critical role in facilitating the integration of in the national data strategy and to sufficiently staff
participants and their data. It is essential to binding the safeguarding and enabling institutions. Academia
the national data system to the social contract on data has funding to create, access, and analyze data. Civil
(see chapter 8). society and individuals have the financial resources
308 | World Development Report 2021needed to acquire the technology often needed to move closer to the system envisioned here, focus-
monitor government data, produce data themselves, ing on how to integrate in the national data system
and use data from other participants. the various participants: government agencies, civil
society, academia, the private sector, and interna-
Incentives. The right incentives and tional and regional organizations.
power balances conducive to the equi- Integrating these participants depends not only
table production, exchange, and use on a country’s data maturity level, but also on other
of data are in place. To overcome the context-specific factors such as the relative strengths
reluctance of government entities to share data and weaknesses of the current institutions and
openly because it could expose poor performance, actors. Where relevant, this discussion explores how
risk data protection breaches with little return, or local contexts might affect progress toward attaining
shrink their power, data exchanges are mandated the vision.
or encouraged through incentives, where relevant. The data maturity model is used as an organizing
Incentives are similarly in place for the private sec- framework to help determine the strengths and weak-
tor, encouraging, and where relevant mandating, nesses of the existing data system and identify the
businesses to exchange data. In the private sector, sequential steps that can be taken to establish an inte-
such incentives deal appropriately with situations grated national data system. The model differentiates
in which corporations may have invested capital in three stages. At low levels of data maturity, countries
systems to accumulate data and wish to earn a return should prioritize establishing the fundamentals of a
on the investment, keep data out of the hands of com- national data system. Once the fundamentals are in
petitors, or both. place, countries should seek to initiate data flows. At
advanced levels of data maturity, the goal is to opti-
Data demand. An integrated national mize the system (figure 9.2).
data system has a high demand for data
Figure
and a culture of valuing 9.2 (1 column plus margin 21p7)
and prioritizing
data use. Data are viewed as founda- Figure 9.2 A data maturity model for a hypothetical
tional for creating public value through improved national data system
policy making, particularly by high-level manage-
ment in both the public and private sectors. In the Gove
rnm
public sector, central analytical units and technical Optimizing ent
en
staff in ministries gather and analyze data tailored to the system tit
ie
the needs of decision-makers. In the private sector,
s
Initiating
companies view data as a valuable asset. For individ- data flows
uals and civil society at large, data are viewed as a
tool for empowerment and for holding governments Establishing
fundamentals
Civil
national organizations
accountable. Programs to improve the data literacy
of individuals, journalists, and other stakeholders society and individuals
are in place, ensuring future demand and the long-
term sustainability of the national data system.
Fact-checking is also well established to challenge
the misuse of data.
Inter
Realizing the vision
For a country suffering from fragility, poverty, and
poor governance, this vision of an integrated national Pr n
ti o
s
iva it u
data system may seem unattainable. As discussed te s
e c to ci n st
in chapter 2, for many governments, just producing r e mi
Ac a d
high-quality data is a challenge. Thus data exchanges
and integration among various partners may not Source: WDR 2021 team.
seem feasible. Yet any country can take steps toward Note: The figure shows steps in a data maturity model for a hypothetical national data system. The
fulfilling the vision of an integrated national data inner circle is the first stage of maturity, the second the middle stage, and so forth. Darker colors
indicate steps accomplished; lighter colors indicate steps not accomplished. Thus for each participant,
system. Using the data maturity model, this section segments may be dark or light. In this way, the figure illustrates that countries may be at different data
describes the concrete steps countries can take to maturity stages at the same time and that some participants may be more integrated than others.
Creating an integrated national data system | 309In practice, deviations from these steps are likely Integrating government
to occur as countries adapt them to their specific Government entities play a central role in the
circumstances and exigencies. Early steps will likely national data system as the main producers of pub-
need to be revisited, refined, and adjusted at later lic intent data. To contribute to and sustain a strong
stages. Some countries will be more advanced in national data system, these entities must meet sev-
some domains but lacking in others—that is, ele- eral objectives. They must address shortcomings in
ments identified as fundamentals may not be present the coverage, quality, and usability of public intent
in some mostly advanced systems. In a few circum- data. They need to ensure the effective coordination
stances, it may be appropriate for a country to change of public sector data producers and data exchanges
the sequencing of steps in certain domains. and the interoperability of data from various sources.
Some countries may not have an intentional And they must make data available and accessible to
whole-of-government approach to data governance stakeholders across the system to promote use, reuse,
but still be advanced in data maturity. Although and repurposing (see figure 9.3).
these countries can have much to gain from taking Data strategy formulation. Recognizing the impor-
an intentional whole-of-government approach, an tance of an integrated national data system in a
integrated national data system does not call for dis- high-level strategic document, such as a national data
carding what has been established, but rather build- strategy, is central to accomplishing these objectives
ing on its strengths. Regardless of where a country’s and to garnering the necessary political commit-
current data maturity stands, building an integrated ment and resources. The formulation of a national
national data system will not happen overnight. It is a data strategy should be a transparent, collaborative
long-term process of ongoing steps, refinements, and process and include stakeholders from across the
improvements. government, civil society, academia, and the private
Figure 9.3 Steps to integrating the public sector into the national data system
Establishing fundamentals
Initiating data flows
• Policies: Recognize the
importance of an integrated Optimizing the system
national data system in a • Data demand: Establish a
high-level strategic document. culture of data use in
ministries and among policy • Institutions: Charge an
• Laws and regulations: Put in makers and legislators. existing or new government
place robust data protection unit with responsibility for
regulations. • Incentives: Prioritize open overseeing and reporting on
data for development and implementation of a national
• Human capital and funding: use of common standards
Strengthen the technical data strategy.
throughout the data life cycle.
capacity and financing of • Incentives: Empower the
NSOs. • Infrastructure policies: NSO to take an active role in
Establish a secure, integrated the national data system.
• Institutions: Create technical digital platform for storing
units in ministries to and providing access to • Policies: Define clear
strengthen administrative deidentified public intent institutional mandates for
data and coordinate data data. the various government
institutions.
• Incentives: Ensure that data
scientists and statisticians
in the public sector are
appropriately remunerated.
Source: WDR 2021 team.
Note: Categories overlap and are meant to be illustrative. NSO = national statistical office.
310 | World Development Report 2021sector to encourage broad-based buy-in. To address for their production, processing, management, and
shortcomings in public intent data, the national data protection. In addition, ensuring the interoperability
strategy should reflect the priorities discussed in and accessibility of administrative data must be a
previous chapters: robust data protection, political priority of administrative data systems. For exam-
commitments to the independence of data produc- ple, Argentina connects data registers through its
ers, adequate and sustained financing, investments data interoperability platform for the public sector
in human capital, and efforts to strengthen the data (INTEROPER.AR).5 Statistical units at ministries
literacy of the general populace, policy makers, leg- should centralize cataloging and storage of data-
islators, and civil society. This process should also sets, including of existing datasets. This will require
establish a common framework for accountability continually modernizing the technological and data
in and independent oversight of the national data infrastructure for the production, management, safe
system. To achieve these priorities, the national data exchange, and secure storage of data.
strategy should include concrete policy steps, such as Remuneration of data scientists. To ensure a func-
the ones that follow, and it should be reflected in the tional integration of government institutions in the
national development plans. For example, in Colom- national data system, civil servants need the incen-
bia, National Development Plan 2014–18 was used tives and capabilities to produce, safeguard, and use
as a vehicle to formally assign its National Statistical data. To this end, governments could pursue civil
Administrative Department (DANE) the role of coordi- service reforms to ensure that data scientists and
nator and regulator of the national statistical system.3 statisticians in the public sector are appropriately
Data protection regulations. Putting in place robust remunerated. These steps are needed to attract and
data and privacy protection regulations is an early retain the human capital required to build and sustain
priority in establishing an integrated national data a successful national data system. Lack of competitive
system. These regulations should be backed by salaries and career opportunities is a frequently cited
independent oversight of compliance with them, a barrier to greater institutional performance and the
function that a data protection authority may serve capacity of data producers, for instance in El Salvador,
(see chapter 8). An example of independent over- Guatemala, and Peru.6
sight is the United Kingdom’s Information Commis Culture of data use. To initiate data flows, it is
sioner’s Office, a nondepartmental body tasked with vital to establish a culture of data use in ministries
upholding “information rights in the public interest.” and among policy makers and legislators. Institu-
Reporting directly to Parliament, it oversees the Data tionalizing data-intensive management practices can
Protection Act, Freedom of Information Act, Privacy jump-start this process (see chapter 2). This effort
and Electronic Communications Regulations (PECR), should be accompanied by ongoing investments in
Environmental Information Regulations, INSPIRE the data literacy of policy makers and legislators.
Regulations, and Re-use of Public Sector Informa- Technical units should be required to periodically
tion (RPSI) regulations.4 Although these regulatory deliver knowledge products based on administrative
steps need to be taken early, they remain relevant data and disseminated in accordance with a public
for advanced data systems. Legal and institutional release calendar. Such products should become an
arrangements will require adjustment as the data and integral part of monitoring, evaluation, and citizen
policy landscapes change. engagement efforts.
NSO capacity. Because the NSO fulfills the core Common standards. Open access to public intent
function of producing official statistics, it is funda- data is central to realizing the broad benefits of
mental that the office be integrated into the national widespread data use, reuse, and repurposing. On the
data system. This requires strengthening the technical political front, it is critical that governments prior-
capacity and financing of NSOs to fill data gaps and itize open data for development and use common
produce high-quality official statistics (see chapter 2). standards throughout the data life cycle. Government
Technical data units. Within government minis- entities should view data stewardship as a strategic
tries and agencies, the foundations of administrative function needed for the effective management and
data should be strengthened. Creating and staffing use of internal data assets, as well as for seamless data
technical units dedicated to the production and man- exchanges among entities. To undertake this func-
agement of administrative data are vital for the par- tion, each entity should receive the required finan-
ticipation of ministries in the national data system. cial and human resources and should use common
Administrative data should be based on common standards for the production, management, quality
standards promoted across the national data system assurance, and interoperability of public intent data.
Creating an integrated national data system | 311Integrated digital platforms. On the technical front, In New Zealand, the NSO is branded as a data agency,
establishing a secure, integrated digital platform for and the head of the NSO was appointed Government
storing and providing access to deidentified public Chief Data Steward by the State Services Commis-
intent data deposited on the platform by producers sioner in July 2017. A cabinet mandate empowers the
from across the public sector can initiate data flows Chief Data Steward to facilitate and enable “an inclu-
and spur further demands for data. The creation of a sive, joined-up approach across government to set
unified platform should be conditional on putting in standards and establish common capabilities, includ-
place common technological, legal, and institutional ing developing data policy, infrastructure, strategy,
standards for safeguarding confidential and sensitive and planning.”9
information. An example is Open Data Philippines Estonia has opted for another institutional home
(ODPH). ODPH was launched in January 2014 as part for the unit overseeing implementation of the national
of the multilateral Open Government Partnership data agenda. The steering body for implementing
initiative, which also includes Brazil, Indonesia, the government’s digital strategy, which includes
Mexico, Norway, South Africa, the United Kingdom, overseeing its national data system, is the e-Estonia
and the United States.7 The ODPH repository works as Council.10 The council is chaired by the prime min-
a core government program ensuring citizens’ rights ister and organized by the Strategy Unit of Govern-
of transparency and access to information. The plat- ment Office, a public entity charged with assisting
form collects more than 1,237 datasets from 99 gov- the government in designing and implementing
ernment agencies and organizations, which allows policy.11 Estonia’s national data system centers around
the disclosure of specific data from different sectors. X-Road, an open-source data exchange layer solution
ODPH acts as an intermediary between the national that allows linked public and private databases to
government and its constituents. It also removes bar- automatically exchange information, ensuring con-
riers limiting data sharing between agencies. fidentiality, integrity, and interoperability among
Data strategy oversight unit. A national data strategy the parties exchanging data.12 X-Road’s cryptography
is key to optimizing the national data system. Central protocols enhance transparency because they log
to this process is charging an existing or new govern- entries into the system and give individuals detailed
ment unit with overseeing and reporting on imple- insights into who is sharing their data and for what
mentation of the national data strategy. The unit will purposes.
play an important coordinating and integrating role Similar to Estonia, Argentina has adopted a feder-
in optimizing data flows among participants in the ated data sharing model that connects data registers
national data system. This role will include acting as and has enabled the development of public services
liaison with the technical data production teams in through its data interoperability platform for the
ministries and the NSO to support the development public sector. This system includes the Smart Judicial
and use of common standards for activities across the Investigation tool and the National Tax and Social
data life cycle. Of particular importance is ensuring Identification System (SINTyS), which coordinates
a common and robust approach to the protection of database exchanges and single data requests at the
personal data and sensitive information across the national, provincial, and municipal levels to support
system. The institutional home and reporting lines better targeting and monitoring of social programs.13
for such a unit will likely differ, depending on the NSO empowerment. Regardless of whether the NSO
country context. To be effective, the unit should have houses the oversight unit, as the data system matures
both the political power to oversee the data agenda of the NSO should be empowered to take on an active
other government institutions and the technical and role (see chapter 2). In Estonia, although the NSO
legal know-how to understand the complexities of does not oversee the national data system, it has been
data governance. elevated to national data agency status—“a center
Many NSOs have the most extensive experience of excellence for public sector data administration
of government agencies in dealing with important and research, which would support the making of
data issues. To the extent that a strong and capable data-based decisions nationwide, integrating data
NSO exists or that reforms can be readily undertaken administration and analytics.”14 In line with this rec-
to shore up its independence, financial resources, ommendation, NSOs could take on a bigger role in
and technical capabilities in line with the recommen- many ways, including conducting data literacy train-
dations put forth in chapter 2, the NSO may be well ing for stakeholders in government, CSOs, the media,
placed to co-lead formulation of the national data academia, and the private sector, as well as by provid-
strategy and possibly to oversee its implementation.8 ing specialized technical assistance to government
312 | World Development Report 2021departments aimed at improving methods for the data and as data producers in their own right, whether
production, processing, management, deidentifica- citizen-generated or collected by CSOs (figure 9.4).
tion, and dissemination of public intent datasets. As A key function of CSOs, national and international
part of an expanded role in training, NSOs could also NGOs, individual citizens, and journalists and the
develop (or hire) staff to engage in research on new media is to hold the government and private sector
methods for data collection and test the validity of accountable. But the national data system also stands
experimental statistics. In addition, NSOs could offer to gain from the systematic incorporation of citizen-
independent quality assurance of the administrative generated data for use by other participants. This
data products and related official statistics produced effort requires collaboration and shared governance
outside of the NSO. arrangements between government and civil society.
Institutional mandates. National data strategies Because of the importance of civil society’s account-
should be revisited periodically as the national data ability function, these governance arrangements
system matures in light of evolving data needs and must be set up to reinforce the unconditional inde-
technological improvements. The process of formulat- pendence of civil society data producers and users.
ing a national data strategy is an opportunity to define For civil society and individuals to be an integral
clear institutional mandates for the NSO, ministries, part of a national data system, several prerequisites
and specialized government agencies for the pro- must be met.
duction, quality assurance, exchange, and protection Legal rights to data production. Laws and regulatory
of public intent data. Such mandates can minimize frameworks are needed to protect people’s rights to
overlapping and duplicated data production, thereby produce, use, and disseminate data. Laws should be
making the whole system more efficient. Defining amended to support individuals’ role in, as well as
clear institutional mandates also helps identify the their accountability function for, the data system. At
comparative advantages and expected contributions the same time, laws and regulatory frameworks need
of each institution and simplifies the task of securing to credibly protect data and sensitive information on
financial and human resources commensurate with people so they will trust they can safely participate
the mandates of each institution. in the national data system. Instituting civil society
watchdogs to monitor public and private sector com-
Integrating civil society and individuals pliance with the rules and regulations of the data
Civil society and individuals should be empowered system can act as additional safeguards for indepen-
to participate in the national data system as users of dence and data protection.
Figure 9.4 Steps to integrating civil society into the national data system
Establishing fundamentals
Initiating data flows
• Laws and regulations:
Institute laws protecting Optimizing the system
people’s rights to produce, • Human capital: Improve data
use, and disseminate data. literacy through joint projects,
training, and secondments • Institutions: Institutionalize
• Incentives: Create civil and through the education collaboration for the
society watchdogs to monitor system. systematic use of citizen-
compliance with the rules of generated data in
the system. • Infrastructure policies: decision-making.
Ensure adoption of common
• Funding: Ensure that civil standards that improve data • Incentives: Include civil
quality and interoperability. society in planning, high-level
and resources for data decisions, and strategy setting
production and use. • Trust: Promote trust for the national data system.
through establishment of an
independent scientific quality
control unit.
Source: WDR 2021 team.
Note: Categories overlap and are meant to be illustrative.
Creating an integrated national data system | 313Funding and resources. Civil society also needs suf- suggest that integrating data literacy into school cur-
ficient funding and resources for data production ricula may ultimately be more effective than targeted
and use. Interviews with representatives of NGOs adult education.22 Because of the relatively young
in Argentina, Kenya, and Nepal revealed that lack of populations of lower- and middle-income countries,
funding can constrain citizen-generated data.15 Both incorporating data literacy programs in school curric-
international donors and national funders could ula could reap valuable returns. For example, Rwanda
improve funding by including specific budgets for has supported initiatives to build digital skills through
citizen data production alongside funding for insti- its multistakeholder Digital Ambassador Program.23
tutional data collection.16 General funds for citizen In addition to general programs, targeted data liter-
data collection akin to those for funding of scientific acy efforts to reach traditionally marginalized groups
research could also be created. When civil society has such as women and indigenous communities may be
limited access to data collection resources, such as needed to reduce the digital divide.
smartphones for computer-assisted interviewing or Common standards. Data flows between civil soci-
collection of satellite-based global positioning system ety and other actors can also be promoted by adopt-
(GPS) data, such resources could be directly distrib- ing common standards that improve data quality
uted by funders or loaned through organizations ded- and interoperability. The efforts to promote adoption
icated to providing communities with technological of these standards should be augmented by efforts
tools and training.17 Open-source software for data aimed at strengthening analytical capacity to ensure
collection, such as ODK, as well as free-of-charge soft- their proper implementation. For example, the col-
ware for data collection, such as Survey Solutions and laboration between Twaweza and public institutions
CSPro, could be supported.18 CSOs also need technical helped ensure that data on literacy and numeracy
support in adopting and operating such resources were collected in accordance with official educational
and software to ensure they have the capability to standards.24 Similarly, in Mozambique the standard-
produce high-quality data. ization of community scorecards used by several
Data literacy. Lack of data literacy in civil society NGOs to assess school and health care services at the
is a major barrier to the demand for high-quality, local level allowed the data to be aggregated to the
accessible data, and it limits the accountability role national level, where they were used in research and
that civil society can play. It also leads to low levels advocacy campaigns.25
of trust in citizen-generated data by other partici- Quality control unit. For citizen-generated data to
pants, which, in turn, hinders data flows from civil be reused—such as to inform policy decisions—their
society to the national data system. Improving data quality and representativeness need to be guaran-
literacy through project partnerships, training, and teed. One specific concern is advocacy bias in cases
secondments can help address these skill gaps and in which the primary purpose of the data produced
trust deficits. For example, the Ugandan Bureau of by civil society is to advocate for certain issues. To
Statistics and Ministry of Education supported the this end, the relevant government agencies and
CSO Twaweza in survey and sampling design for a CSOs could together establish an independent
numeracy and literacy survey. Twaweza then inde- scientific quality control unit to assess the method-
pendently carried out the data collection and pro- ological soundness and representativeness of citizen-
cessing, improving the quality of and trust in citizen- generated data for possible use in national data por-
generated data. The data were later used by the tals and in SDG reporting.26 Closer collaboration and
Ministry of Education.19 In addition to training, joint the adoption of common standards do not mean that
projects, fellowships, and secondments of staff from civil society ceases to play its critical role of holding
CSOs to data-driven institutions can increase the other actors accountable by challenging their data,
technical capacities of CSOs.20 One private sector–led views, or priorities. Methodological rigor and com-
initiative to increase digital literacy in civil society, mon standards may in fact empower civil society to
StoryLab Academy, uses online webinars and face- play an accountability role by increasing the credibil-
to-face training to improve digital literacy among ity and interoperability of citizen-generated and CSO
African journalists.21 The academy is a joint initiative data. At the same time, not all CSOs may opt for their
of Code for Africa, the World Bank’s Global Media data to be used for policy purposes or SDG reporting
Development Program, and Google News Lab. if advocacy is their primary concern. In any case, data
Data literacy should also be reinforced across soci- should flow to civil society to empower and inform
ety more broadly. One aspect is incorporating data decisions of communities and individuals and ensure
literacy in primary and secondary education curric- that benefits from an integrated data system are
ula (chapter 2). Empirical studies on financial literacy broadly shared.
314 | World Development Report 2021Institutionalized collaboration. To optimize data council.29 Once included in multistakeholder gover-
flows, the relationship between civil society and nance forums, civil society can progress from being
other participants should evolve from a stage of ad a standard-taker to a standard-maker. Including
hoc collaboration to institutionalized collaboration citizens in the planning, production, and use of data
for the production and use of data. From the per- can also help empower individuals and enhance trust
spective of the government, this means that citizen between citizens and their governments.30
data production and use are by default integrated
into policy-making and administrative processes.27 Integrating academia
An example is the framework developed by South In a well-functioning national data system, academia
Africa’s Department of Planning, Monitoring and (including universities, think tanks, and research
Evaluation on how to include citizen-based monitor- organizations) generates data and insights, advances
ing in planning, budgeting, and evaluation systems.28 the methodological frontier, and trains other partici-
Institutionalized collaboration allows the government pants in data production and use (figure 9.5).
to leverage civil society’s unique perspectives, local For academia to realize this potential, several con-
expertise, and motivation, whereas civil society can ditions must be in place.
influence policies and services, highlighting problems Technological infrastructure. Academics must have
that may otherwise go unnoticed or be ignored. With adequate financial support and access to key informa-
institutionalized collaboration, civil society could tion and communication technology (ICT) infrastruc-
have a designated role of collecting data that would ture. Higher education funding should prioritize and
otherwise be too expensive or difficult to collect, include designated budgets for data infrastructure.
such as wildlife counts. Institutionalized collabora- Where limited, access to ICT infrastructure required
tion on data would also serve as an incentive for rely- for any work involving large datasets should be
ing on data for policy making more broadly, and thus expanded.
would bolster demand for data. Data awareness. Any form of cooperation and data
Joint planning and strategy setting. Finally, govern- exchange between academia and other participants in
ments should take steps to include civil society in the national data system requires awareness of which
planning, high-level decisions, and strategy setting databases are maintained by participants. A survey
for the national data system. In Chile, where civil of policy makers in 126 low- and middle-income
society participation is mandated by the national Law countries found that they learn about domestic data
on Associations and Citizen Participation in Public sources primarily through consultations and infor-
Management, the NSO has put in place a civil society mal communications, highlighting the important
Figure 9.5 Steps to integrating academia into the national data system
Establishing fundamentals
Initiating data flows
• Infrastructure policies:
Ensure that academics have Optimizing the system
adequate financial support • Data demand: Set up
and access to key ICT data sharing agreements,
infrastructure. commission data production • Infrastructure policies:
by academics, and support Institutionalize access to data
• Data demand: Promote the repurposing of academic through data portals and data
awareness of the datasets data. enclaves.
of participants by building
relationships among partners • Human capital: Promote data • Data demand: Support and
in academia and government. literacy through quantitative adopt data innovations by
degree programs, workshops, academia.
and secondments. • Incentives: Support initiatives
that provide legal access to
scientific journals.
Source: WDR 2021 team.
Note: Categories overlap and are meant to be illustrative. ICT = information and communication technology.
Creating an integrated national data system | 315role of personal interactions and social capital.31 At of data use among Nepalis.34 The program is now
low data maturity levels, promoting awareness of partnering with the Kathmandu University School
the datasets of participants by building relationships of Management to incorporate data literacy toolkits
between partners in government and academia, such into university programs and develop a data-driven
as through workshops, is therefore a priority. As in course that will be free to other institutions and thou-
civil society, laws and regulations need to protect aca- sands of students as a result.
demics’ rights to collect and share data. Embedding a team of researchers in public insti-
Data exchange agreements. To initiate data flows, tutions is another effective way of transferring skills.
researchers can leverage relationships with other In Peru, IPA and J-PAL partnered with the Ministry
participants in the national data system and set up of Education to embed a team of researchers in the
project-based data exchange agreements—for exam- ministry. They then worked with public officials to
ple, to access administrative data collected by the conduct several impact evaluations using administra-
government. Other participants may take advantage tive data. The ministry subsequently scaled up three
of academia’s expertise and commission the gener- programs based on the evaluation results, and the
ation of data for their needs. Data generated by aca- unit is now government-run.35
demics as part of their research could be repurposed Data portals and enclaves. To optimize data flows
by other participants in the national data system, to academia, academia’s access to public intent data
from government policy makers to students pursuing could be institutionalized by establishing data por-
higher education. An example of how researchers can tals and data enclaves. The latter enable researchers
make their data available for repurposing is the Data- to use confidential microdata from surveys and
hub for Field Experiments in Economics and Public government censuses behind secure firewalls. For
Policy, a public searchable database that researchers example, Mexico’s National Institute of Statistics and
affiliated with Innovations for Poverty Action (IPA) Geography (INEGI) offers researchers data access
and the Abdul Latif Jameel Poverty Action Lab (J-PAL) through its Microdata Laboratory, which is located
use to publish their data from impact evaluations.32 in secure enclaves on its premises, provided they
It is critical to ensure that the data made available undergo an application and training process.36 Sim-
for downstream use are sufficiently documented, ilar institutions have been set up in other countries,
not only to confirm replicability of past findings but such as DataFirst in South Africa37 and the Scientific
also to properly inform future use. Research Center at the Palestinian Central Bureau of
Data literacy. Academia also plays an important Statistics in the West Bank and Gaza.38 Implementing
role in the flow of human capital and the promotion these models requires a secure infrastructure, skills
of data literacy. Tertiary education programs should in the deidentification of data, trust, and sanctions for
be geared toward training professionals skilled in misuse and attempts to reidentify individuals. When
using data who are prepared to join institutions in public intent data are made accessible to researchers,
the data system. At lower data maturity levels, any data producers should require that insights gained
programs providing skills in quantitative fields, such from the data flow back to them such as in the form of
as statistics, economics, or computer science, will be technical briefs or open-access journal articles. This
useful. Beyond formal tertiary education, academia requirement helps ensure that data exchanges serve
can train participants from government agencies, the broader development objectives. Microdata access
private sector, or civil society by means of training should also extend to metadata and syntax files to
courses, workshops, and seminars. For example, the increase ease of use and transparency.
Data Literacy pillar of the Data-Pop Alliance—an alli- Data innovations. Finally, innovations emanating
ance created by the Harvard Humanitarian Initiative, from academia should be supported and, where rele-
MIT Connection Science, and Overseas Development vant, adopted. Academia can play a role in transferring
Institute—has developed a framework and tools to and applying global knowledge to local contexts. For
establish core competencies toward becoming data lit- example, randomized experiments in international
erate.33 The Nepal Data Literacy Program, established development research were originally pioneered by
in 2019 through a partnership between the Nepalese academics at elite universities, but since then they
government, the World Bank, and the United King- have proliferated and been adopted as a decision-
dom’s Department for International Development making tool by many governments, including those
(since incorporated into the Foreign, Commonwealth in low- and middle-income countries.39 A prominent
and Development Office), comprises a 100-hour mod- example is Mexico’s National Council for the Evalua-
ular, customizable pedagogy to support both build- tion of Social Development Policy (CONEVAL), which
ing technical skills and efforts to enhance a culture was set up in 2004 with the mandate to coordinate
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