GRF · Governance Nexus

Digital and data governance

Digital and data governance decides who may collect, use, share and act on data and on the systems built from it, and how those choices are answered for. As public services, markets and infrastructure come to depend on data platforms and automated decisions, the risks become governance risks: privacy harm, security failure, concentration, opaque algorithms and lock-in. This page sets out how these failures develop, how digital governance can be read, and which decisions sit with legislators, regulators, boards and public bodies.

02The question

Digital and data governance: how the risk develops.

Technology and information stewardship. The dossier below sets out how the risk develops, what is measured, where the data come from, how it is modelled and who decides.

Mechanism

Accountability blurs in automated decisions

When a model informs or makes a decision, responsibility spreads across data providers, developers, deployers and operators. Without a named owner and a record of how the system was tested, affected people cannot find who answers.

Mechanism

Data outlives its purpose

Data collected for one purpose is reused, combined and retained. Each step is small, but together they create exposure to breach, profiling and misuse that no one approved as a whole.

Mechanism

Dependence concentrates

Public bodies and firms come to rely on a few cloud, identity and software providers. Switching costs and opaque contracts limit oversight, and a single provider outage or policy change reaches many services at once.

Mechanism

Security debt compounds

Unpatched legacy systems, incomplete asset inventories and thin security staffing accumulate risk silently. A breach then exposes not only data but the governance choices that let the debt grow.

03Geography

Connected activity in Asia.

Night lights, population density and flood water over Asia, from NASA GIBS. Each layer carries its own date.

The interactive map needs JavaScript; the layer list and sources are below.

Layers 3

Connected activity, for this page

Latest available

NASA GIBS / EOSDIS · ASTER GDEM (METI, NASA) · Blue Marble (NASA) · © OpenStreetMap contributors (ODbL) · Natural Earth

Loading the latest NASA imagery.

Drag to move. Ctrl or ⌘ and scroll, pinch or the buttons to zoom; hover for the values under the pointer; reset returns to the region.

Connected activity, framed on Asia.Satellite and model products as NASA publishes them, each with its own date. They are indicators, not assessments of any place.

Data: NASA GIBS · ASTER GDEM · Blue Marble · © OpenStreetMap contributors · Natural Earth · NASA EONET · NASA/NOAA VIIRS · U.S. Geological SurveySources and licences

  • We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • VIIRS Black Marble night-time lights, NASA Goddard Space Flight Center; distributed by the NASA Level-1 and Atmosphere Archive and Distribution System (LAADS DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • Center for International Earth Science Information Network (CIESIN), Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). Licensed under CC BY 4.0. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). doi:10.7927/H49C6VHW. CC BY 4.0
  • VIIRS near-real-time products (flood, land surface temperature, snow cover, corrected reflectance), NASA LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • NASA Fire Information for Resource Management System (FIRMS), VIIRS 375 m active fire product, NASA LANCE; vector tiles via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • MODIS land products (vegetation indices, gross primary production, land cover), NASA LP DAAC and LANCE; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • ASTER GDEM is a product of METI and NASA (ASTER Global Digital Elevation Model V003, NASA LP DAAC); imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). Cite as: NASA/METI/AIST/Japan Spacesystems and U.S./Japan ASTER Science Team (2019). ASTER Global Digital Elevation Model V003. doi:10.5067/ASTER/ASTGTM.003. NASA open data
  • Made with Natural Earth. Free vector and raster map data @ naturalearthdata.com. Public domain
  • © OpenStreetMap contributors (ODbL), via NASA GIBS (coastlines, boundaries, roads and place names). Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). ODbL
  • Blue Marble, NASA Earth Observatory; imagery via NASA GIBS. Colours redrawn from the published colour map; values averaged into hexagonal cells where shown (modified). NASA open data
  • Context on the globe and flat map: coastlines, boundaries, roads and place names from NASA GIBS reference layers (OpenStreetMap data, ODbL, © OpenStreetMap contributors); Blue Marble imagery as an optional base; 3D terrain and the elevation read-out from the ASTER GDEM elevation colour index (5 m to 200 m classes, exaggerated for legibility). Boundaries are drawn as published and imply no position on any territory.
  • Live events: NASA Earth Observatory Natural Event Tracker (EONET); events are indicative and keep their original source links.
  • Observed (EO): NASA/NOAA VIIRS (Suomi NPP). We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
  • Live: NASA EONET Events are indicative, not authority warnings.
  • Live: U.S. Geological Survey Events are indicative, not authority warnings.
  • NASA GIBS: We acknowledge the use of imagery provided by services from NASA's Global Imagery Browse Services (GIBS), part of NASA's Earth Science Data and Information System (ESDIS).
  • Optional layers (5 sources in the layer list: live, forecast, observed and context) print their provider credit on the map and here when switched on; forecasts are model output and derived indices are estimates.

Layers and dates

  • Night-time lights, daily: VIIRS Black Marble (VNP46A2), NASA LAADS DAAC · 12 Oct 2026
  • Population density, 2020: Population: CIESIN, Columbia University, Gridded Population of the World v4.11 (2018), NASA SEDAC, CC BY 4.0 · static
  • True colour: VIIRS NOAA-20 corrected reflectance, NASA LANCE · 13 Oct 2026
  • Flood water, 3-day composite: VIIRS Global Flood Product, NASA LANCE · 14 Oct 2026
  • Active fires and thermal anomalies: VIIRS NOAA-20 375 m (FIRMS), NASA LANCE FIRMS · 13 Oct 2026
  • Land cover type (IGBP), yearly: MODIS Terra and Aqua land cover (MCD12Q1), NASA LP DAAC · 2024

04Indicators

What is measured, and how well.

Each indicator carries its unit, its cadence, the kind of source behind it and the evidence label a value would carry. No values are shown: a programme supplies them under its own authority.

IndicatorUnitCadenceSource typeLabel
Comprehensive data protection law in forceyes or no, with independent supervisory authorityper changeUNCTAD data protection legislation trackerPublished statistics
Data protection authority resourcesstaff and budget per authorityannualAuthority annual reportsPublished statistics
Personal data breach notificationscount of notifications by sectorannualSupervisory authority reportsPublished statistics
National cybersecurity commitmentscore by pillar: legal, technical, organisational, capacity, cooperationper editionITU Global Cybersecurity IndexESTIMATED
Public register of algorithmic systemsyes or no; number of systems registeredannualGovernment publicationsPublished statistics
Data governance and availabilityscores by moduleper editionGlobal Data BarometerESTIMATED
Digital government maturityindex score by dimensionper editionOECD Digital Government IndexESTIMATED
05Monitor

Deliberation and decision desk.

The screen a team would use to follow this subject: its measures, states and decisions. Names are examples and values are illustrative.

Deliberation and decision desk

Digital and data governance · Council A · Meeting 20

Declaration pendingGRF · Governance Nexus

Agenda

Risk appetite statementP-817ApproveNoneDiscussing
New member admissionP-539ApproveDeclaredNext
Safeguards annual reviewP-880NoteNoneNext
Data charterP-458ConsultNoneDone

Anti-capture safeguards

in place, of 12

Rotation of chairs9 of 12 requiredDeclared interests12 of 12 requiredIndependent members10 of 10 required

Decision record

  1. M-12DECAppetite statement approved with two dissents recorded
  2. M-18ACTSecretariat to publish reasons within 30 days
  3. M-16REVReview date set
Deliberation and decision desk: the screen's structure, terms and states for Digital and data governance.Example names and illustrative values; a design, not a record or a live system.
06Sources

Where the evidence comes from.

Named public datasets and institutional records, each with its licence class. Sources by agreement are used only under their terms and never republished.

SourceWhat it givesLicence class
UNCTAD Data Protection and Privacy Legislation WorldwideStatus of data protection and privacy legislation by countryFree, attribution and UN terms
ITU Global Cybersecurity IndexAssessment of national commitments to cybersecurity across five pillarsFree, attribution and ITU terms
Global Data BarometerExpert assessments of data governance, capability, availability and useFree and open, CC BY 4.0
OECD.AI Policy ObservatoryDatabase of national AI policies, strategies and governance initiativesFree, attribution (OECD terms)
OECD Digital Government IndexComparative assessment of digital government maturityFree and open, attribution (OECD terms)
Data protection authority annual reportsComplaints, breach notifications, investigations and sanctionsPublic records, terms per authority
07Policy scenarios

Compare instruments: rules, incentives and information.

Three families of instruments side by side against the baseline, each with its own lag and adoption speed, under the coverage and compliance you set; effect sizes are teaching values to replace with evidence. Scenario: named assumptions about the future, not a forecast. The map on this page shows Asia; the model itself is not tied to a place.

Controls

Baseline trend of the outcome
2.0% a year
Coverage of the instrument
70%
Compliance
75%
Effect size, against the teaching value
100%
Years ahead
20 years

Readouts

Largest reduction at the horizon
Regulation
Its reduction against the baseline
21%
Numbers, methods and sources
YearBaselineRegulationEconomic incentiveInformation and capacity
0100.099.598.899.1
1102.0101.2100.199.8
2104.0102.5101.199.9
3106.1103.5101.6100.0
4108.2103.7101.8100.7
5110.4103.2101.7102.1
6112.6102.0101.6103.9
7114.9100.4101.6105.9
8117.299.2102.1108.0
9119.598.6102.9110.1
10121.998.9104.2112.3
11124.399.7105.7114.5
12126.8101.1107.4116.8
13129.4102.7109.4119.2
14131.9104.5111.4121.6
15134.6106.5113.5124.0
16137.3108.5115.7126.5
17140.0110.7118.0129.0
18142.8112.9120.4131.6
19145.7115.1122.8134.2
20148.6117.4125.2136.9

Methods

  1. Tools of government Hood (1983), The Tools of Government, Macmillan
  2. Carrots, sticks and sermons Bemelmans-Videc, Rist and Vedung (1998), Transaction
  3. Diffusion of innovations Rogers (2003), Diffusion of Innovations, Free Press
  4. Uncertainty fan (parameter Monte Carlo) Spiegelhalter, Pearson and Short (2011), Visualising uncertainty about the future, Science 333
  5. One-at-a-time sensitivity (tornado) Saltelli and others (2008), Global Sensitivity Analysis: The Primer, Wiley

Data

The model runs on the teaching values set by the controls. To calibrate it for a place, read it against these admitted layers and series, which this page's map can draw:

  • Borders Natural Earth
  • Cities Natural Earth
Compare instruments: rules, incentives and information. Illustrative teaching model: it runs on the values set by the controls, not on measured data. Methods: Tools of government (Hood (1983), The Tools of Government, Macmillan); Carrots, sticks and sermons (Bemelmans-Videc, Rist and Vedung (1998), Transaction); Diffusion of innovations (Rogers (2003), Diffusion of Innovations, Free Press).
08Decisions

Who decides, and what the evidence serves.

Governance Nexus prepares evidence. The decisions below are taken by the authorities named, under their own rules, and never by the platform.

Who decidesThe questionEvidence this page serves
LegislatorsHow to assign duties for data protection, AI and critical digital services, and to whomComparative regimes, accountability maps, case evidence
Data protection, cyber and competition authoritiesWhere to focus supervision and enforcementBreach and complaint records, sector cases, authority resources
Boards and chief executivesHow data, cyber and AI risks are owned, reported and testedBoard oversight guidance, decision-rights maps, comparable incidents
Public bodies deploying automated systemsWhether a system may be used, and with what safeguards, records and reviewAccountability maps, impact assessment practice, algorithm register models

Outputs this topic produces

  • Accountability maps for automated decision systems, naming each role from data to deployment
  • Case studies of data breaches, algorithmic failures and platform outages and their governance lessons
  • Guidance on board oversight of data, cyber and AI risk
  • Comparative notes on data protection and AI governance regimes
09Implementation

From mandate to evaluation, by pillar.

The steps an instrument takes from mandate to evaluation, each with the pillar that runs it, and the decisions that stay with the authority. Teaching model with stated illustrative values, not a prediction. The map on this page shows Asia; the model itself is not tied to a place.

  1. Mandate and problem framing Competent authority (outside the programme)
    A competent authority frames the problem and the mandate under its own rules. Howlett, Ramesh and Perl (2009)
  2. Evidence and system model Foundry
    The system is modelled with its feedbacks, data and stated limits. OECD (2014), principle 2
  3. Options appraised with scenarios Foundry
    Instruments are compared under scenarios, with uncertainty shown. Hood (1983)
  4. Exercise with stakeholders Universe
    The options are tested in an exercise with those who would implement and bear them. OECD (2014), principle 3
  5. Standard or guidance drafted Nexus Standards
    What works is codified as a standard or guidance, open to comment. Nexus Standards process
  6. Decision by the authority Competent authority (outside the programme)
    The authority decides, outside the programme, with the evidence on record. Howlett, Ramesh and Perl (2009)
  7. Capacity and training Risk Academy
    Those who implement are trained on the decided instrument and its data. Sendai Framework (2015), priority 2
  8. Monitoring and public report Reports
    Indicators are published with sources, limits and corrections. OECD (2014), principle 5
  9. Evaluation and revision Nexus Standards
    Results return to the standard and the model: the cycle closes. Howlett, Ramesh and Perl (2009)

Methods

  1. Policy cycle Howlett, Ramesh and Perl (2009), Studying Public Policy, Oxford University Press
  2. Governance of critical risks OECD (2014), Recommendation of the Council on the Governance of Critical Risks
  3. Layered drawing of directed graphs Sugiyama, Tagawa and Toda (1981), IEEE SMC 11
From mandate to evaluation, by pillar: the steps as recorded, each with its pillar; sources as cited on each step.
10Core questions

The questions experts ask.

  • Who decides how data are collected, used and shared, and on what legal basis?
  • How are algorithmic and AI systems in public and critical services overseen?
  • How do institutions keep accountability when they depend on vendors and platforms?
  • How are privacy, security and openness balanced?
  • How are digital systems governed across borders?
11Twin and cascade simulator

Exercise a cascade in digital and data governance with the people who decide.

Assembled from the Nexus Standards area profile Political and governance risk standards: the hazards it names, the systems and communities that carry the loss, the controls and institutions that dampen it and the data that observe it. Pick an initiating event and step through the cascade to see where mandates meet and which decisions fall to whom. The Map view places the run on an example geography with open data. Every weight is a stated assumption. An exercise for dialogue, not a forecast, an assessment or a position. To model a place of your own choosing, open the full System Builder.

Controls

  • NS-0 Constitution and principles
  • NS-9 Contextual UNIs (deployments, instances, programme instances)
  • NS-10 Policies and clauses (machine-readable obligations: definition, binding, evaluation and enforcement)
  • NS-11 Verification and monitoring (evidence, attestations, zero-trust conformance observation)
  • NRS-H112 Hazard family: Governance and institutional failure (NHS-1 soc.governance)
  • NRS-U9 Universe platform profile: Financial Regulation Nexus
  • NRS-H59 Hazard family: Economic shocks and governance failure (NHS-1 soc.economic)
  • NRS-H118 Hazard family: Organised crime and illicit economies (NHS-1 soc.crime)
  • NRS-A5 Built-in licensing and rights
  • NRS-U24 Universe platform profile: Policy Nexus

Observation

  • Worldwide Governance Indicators (listed for reference)
  • V-Dem dataset (listed for reference)
  • Global SDG Indicators Database (listed for reference)
  • Proportion of population satisfied with their last experience of public services
  • Proportion of population who believe decision-making is inclusive and responsive
  • Number of countries that adopt and implement national DRR strategies in line with the Sendai Framework
12Mechanisms

Stewardship, oversight and dependency.

Data governance defines roles and rules for data across their life cycle: who owns or stewards them, for what purposes they may be used, how quality is ensured and how individuals' rights are protected. Data protection law sets minimum standards in many jurisdictions, but governance goes further, covering data sharing for public interest, open data and the stewardship of data held on behalf of communities.

AI and algorithmic systems raise specific challenges: opacity, bias inherited from training data, difficulty of contesting automated decisions and rapid change. Oversight approaches include impact assessment, human review of consequential decisions, audit and transparency registers. Institutions that procure technology may lose the expertise to oversee it, creating dependency on vendors whose systems they cannot inspect.

Concentration of digital infrastructure in a few providers creates systemic risk, as outages or security incidents can affect many institutions at once.

Public trust depends on these arrangements. When people cannot understand or contest how data about them are used, or when systems fail without clear accountability, trust in the institutions that deploy them declines, with consequences that reach well beyond the technology itself.

Why now: institutions depend on a small number of digital providers and data flows, and oversight of that dependence is new for many boards. Governance Nexus compares how institutions govern digital dependence and can propose common records to the Nexus Standards.

13Instruments and commitments

How digital and data governance is governed: instruments, mandates and commitments.

The instruments, mandates, actors and accountability arrangements a policymaker, diplomat or development agency works with, citing the agreements that set them. Structures follow the frameworks named. Any state or progress drawn is illustrative, not a record.

Policy instrument matrix

The kinds of instrument available on digital and data governance, by level of government and cooperation.

Instrument typeLocalNationalRegionalGlobal
RegulatoryZoning, building codesLaws, standards, licensingHarmonised rulesConventions and treaties
EconomicLocal levies, grantsBudget lines, taxes, insurance schemesRisk pools, regional fundsFinancing mechanisms
InformationLocal warningsDisclosure, early warning, statisticsData sharing arrangementsGlobal monitoring and reporting
InstitutionalLocal committeesLead agencies, coordination councilsRegional bodies and secretariatsMultilateral mandates
CooperativeCommunity agreementsPartnership compactsMemoranda and joint programmesAgreements and joint commitments
A typology after the OECD Recommendation on the Governance of Critical Risks; examples of instrument types, not an inventory of any country's measures.
Sendai Framework for Disaster Risk Reduction · UNDRRParis Agreement · UNFCCC2030 Agenda and the Sustainable Development Goals · United NationsInternational Health Regulations · WHO
14Methods and data

Methods and their limits.

Assessment

Impact assessment

Data protection and algorithmic impact assessments; quality varies and they can become formalities.

Audit

Algorithm audit

Testing systems for bias and performance; access to systems is often restricted.

Comparison

Comparative law and policy

Comparing regulatory approaches across jurisdictions.

Limits: technology changes faster than governance research, and evidence on what oversight approaches work is still developing.

15Methods

How it is studied, and what the methods cannot say.

  1. Systems engineeringDecision-rights and interface mapping · Assurance and control designApplied here
  2. Decision analysis under uncertaintyRisk appetite and tolerance setting · Multi-criteria appraisal of institutional optionsApplied here
  3. Policy, institutional and decision scienceInstitutional role mapping · Comparative governance case studiesApplied here
16Questions

Questions and answers.

What is the difference between data protection and data governance?
Data protection concerns the rights of people whose personal data is processed and the duties of those who process it. Data governance is the wider system of rules, roles and decisions for all the data an organisation or society holds and uses.
Who is accountable for an automated decision?
The organisation that deploys the system remains accountable for its use, under law that varies by jurisdiction. Good governance names an owner for the system, records how it was tested and gives affected people a route to review.
Why does a rising breach count not mean things got worse?
Because counts reflect reporting duties, detection capacity and willingness to notify as well as actual incidents. The page reads breach records with the reporting regime beside them.
How does this differ from AI and Digital Governance?
AI and Digital Governance is GRF's platform for technology governance specifically. This topic examines digital and data governance as part of institutional governance generally.
Does the platform assess AI systems?
No. It convenes comparative study of governance approaches.
Is data governance only about privacy?
No. It also covers quality, sharing, stewardship, security and accountability.
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