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PART III

       Moving toward an
       integrated national
       data system
       9. Creating an integrated national data system
CHA
   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   |   303
Toward 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 2021
After 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            |   305
Functions 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 2021
production 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   |   307
that 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 2021
needed 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                        |      309
In 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 2021
sector 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     |   311
Integrated 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 2021
departments 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     |    313
Funding 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 2021
Institutionalized 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             |   315
role 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

316   |   World Development Report 2021
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