evidoria

← Back to browse

Good practice Imported

AI-Assisted Data Cleansing — Jordan Restores 2 Million Tawjihi Exam Records and Harmonises 2.5 Million Land Records

Jordan · Amman · See the Jordan profile · See the Amman profile

Evidence: Descriptive / self-reported Top 66% 53/100 · Ask Evidence Copilot about this practice

Jordan's Digital Economy Ministry, with Japan's JICA and Amman-based Eqra Tech, used AI to repair national-ID links on 2M+ 1985–2004 exam records and reconcile 2.5M land records across two ministries — a data-quality fix under Jordan's 2023–2027 AI strategy.

2+ million
Tawjihi exam records with restored ID linkages (1985-2004 records, restored by mid-2025)
2.5+ million
Land records harmonised (completed mid-2025)
AI-Assisted Data Cleansing — Jordan Restores 2 Million Tawjihi Exam Records and Harmonises 2.5 Million Land Records

Details

Promoter
Ministry of Digital Economy and Entrepreneurship (Jordan), with JICA and Eqra Tech
Period
2024–2025
Keywords
digital government, data governance, civil records, land administration

Context

Jordan's Ministry of Digital Economy and Entrepreneurship signed an agreement with the Japan International Cooperation Agency (JICA) in December 2024 to apply AI to two long-standing government data-quality problems, with implementation carried out by Amman-based firm Eqra Tech under the project 'Using Artificial Intelligence for Government Data Cleansing in Selected Use Cases.'

Objectives

The project aimed to repair and harmonise two large, long-standing government datasets that conventional methods had failed to fix, as a foundation for more reliable analytics and decision-making rather than a citizen-facing service in its own right.

Activities

The first use case restored national-ID linkages on more than 2 million Tawjihi (General Secondary Education Certificate) exam records spanning 1985-2004, after conventional data-recovery methods had failed to reconnect historical results to citizens' national ID numbers. The second harmonised more than 2.5 million land records shared between the Ministry of Local Administration and the Department of Lands and Survey, unifying inconsistent coding systems between the two agencies' databases.

Results

Both projects were reported completed by mid-2025, restoring over 2 million exam records and harmonising over 2.5 million land records.

Conclusions

No source found discloses a numeric accuracy or match-rate figure, an independent audit, or a downstream measure of how the cleaned data has since improved a specific government service. The project is one of the more concretely completed items in Jordan's 2023-2027 AI Strategy and Implementation Plan, a 68-project roadmap on which most other announced activity remains at the strategy or training stage.

Implementation

Indicative cost
Medium (€50k–€500k) — JICA technical-cooperation agreement with Eqra Tech as implementer; project cost not disclosed in sources.
Time to results
Short (< 1 year) — Agreement signed December 2024; both use cases reported completed by mid-2025.
Staffing & skills
Ministry of Digital Economy and Entrepreneurship (Jordan), Japan International Cooperation Agency (JICA), technical cooperation, Eqra Tech (Amman-based implementer)

Conditions for success

  • Formal inter-agency agreement covering data shared between the Ministry of Local Administration and the Department of Lands and Survey
  • International technical-cooperation partnership (JICA) providing AI expertise
  • Embedding within Jordan's 2023-2027 AI Strategy and Implementation Plan for institutional backing

Common failure modes

  • No numeric accuracy or match-rate figure disclosed
  • No independent audit of the data-cleansing results
  • No downstream measure of how the cleaned data has since improved a specific government service

Where it fits

Governance type
national government with international donor partnership
Scale
national administrative datasets (millions of records across two ministries)
Income level
upper-middle income

Do you run this practice? Claim it — verified implementers get a public contact pathway and can propose corrections.

Data sources

Where this practice's information was retrieved from, and when.

Attachments

Similar practices you may find useful