Unifying Indonesia’s Cross-Sector Data Systems

In the era of digital governance, public sector transformation depends not merely on technology adoption and solid regulation standing but on the ability of institutions to share, standardise, and operationalise data across organisational boundaries.

Corinium spoke with Febriana Misdianti, the senior data manager at INA Digital Edu to understand the fragmentation between ministries and agencies in Indonesia, which remains a major obstacle to policy coherence and service delivery.

These challenges are further compounded by the country’s vast geography and social diversity, as Indonesia continues to face significant geographical, societal, and systemic barriers that hinder the delivery of equitable, high‑quality education. Disparity in resources, curriculum implementation, infrastructure readiness, and learning outcomes persist across provinces and districts. These structural conditions make coordination not only desirable, but essential.

Indonesia’s Ministry of Primary and Secondary Education has taken a significant step toward addressing this complexity through the development of Rumah Pendidikan, a national education super-app designed to unify digital education services within a single interoperable ecosystem. Although designed as an education platform, Rumah Pendidikan offers a practical blueprint for how cross-sector data interoperability can enable coordinated, cross-ministries collaboration.

A Unified Vision: Beyond App Consolidation

What distinguishes Rumah Pendidikan is not merely service consolidation but also its architectural alignment. Rather than functioning as a single monolithic application, the platform is structured into eight integrated modules (“ruang”), each serving a distinct stakeholder group.

Building a Unified Digital Ecosystem

Before the introduction of Rumah Pendidikan, Indonesia’s education landscape was supported by hundreds of disconnected digital systems, ranging from student data repositories to teacher certification tools and school administration platforms. This fragmentation limited visibility, slowed coordination, and increased duplication across ministries and regional authorities.

Rumah Pendidikan was not built to replace these systems outright, but to federate them under a harmonised structure. By integrating previously siloed systems into structured modules (or “ruang”), the platform establishes common access points and harmonised workflows while preserving role-based governance.

Crucially, this integration is underpinned by:

1. Standardised data taxonomies: ensuring data definition for students, teachers, institutions, credentials, and performance metrics are consistent across agencies.
2. API-driven architecture: enabling secure and efficient data exchange.
3. Clear governance framework: defining data ownership, access rights, and compliance mechanisms to preserve trust and accountability.

This architecture enables collaboration not only within the education ministry, but also across civil service administration bodies, regional governments, planning and budgeting agencies, and even social protection institutions where education data intersects with welfare eligibility criteria. In doing so, it is intended to strengthen policy alignment, enhance responsiveness in decision-making, and promote more coherent program delivery at scale.

Building AI-Ready Government Through Data Quality Management

Interoperability alone, however, is insufficient for the next stage of public sector transformation. As governments accelerate the adoption of artificial intelligence and advanced analytics, the quality of underlying data becomes mission-critical.

To safeguard downstream systems, each data entity unit must achieve full compliance with predefined Data Quality Management (DQM) metrics before being exchanged with other services, both internally within the ministry and externally across government institutions. This ensures that interoperability does not merely move data faster, but moves only verified and compliant data across institutional boundaries. Rumah Pendidikan’s ecosystem illustrates the importance of building AI-ready infrastructure through:

• Validity: validated and continuously updated records reduce systemic errors.
  Example scenario: Teachers’ national identification numbers are automatically validated against a record from the National Civil Registration and Population Authority to ensure the identity exists and matches official demographic data before payroll or certification processing is approved. 
• Completeness: comprehensive datasets allow for more equitable and robust analysis.
  Example scenario: Mandatory personal fields, such as employment status, certification level, school assignment, and regional placement, cannot be left blank in the system. Incomplete records are flagged before submission to prevent gaps in workforce planning or budget allocation models. 
• Consistency: harmonised formats prevent model fragmentation and semantic conflicts.
  Example scenario: Reference IDs (such as school ID, teacher ID, or regional codes) must match across tables and systems, ensuring datasets can be reliably joined for cross-analysis without reconciliation errors or mismatched entities.
  
• Timeliness: near-real-time data flows support responsive interventions.
  Example scenario: Through a Change Data Capture mechanism, every modification in core datasets, such as teacher status, school enrollment, or funding records, automatically triggers validation checks against predefined quality metrics. 
• Uniqueness: strong entity resolution and deduplication mechanisms ensure that individuals, institutions, and records are uniquely identified, reducing redundancy, preventing double counting, and safeguarding analytical integrity.
  Example scenario: A single teacher registered in multiple systems is identified through unique identifiers and deduplication rules, preventing double counting in national staffing statistics or benefit eligibility assessments.
• Accountability: formalised data ownership and governance structures ensure that data quality is monitored, corrected, and ethically managed.
  Example scenario: Each indicator, such as teacher certification rates, student enrollment figures, or school funding utilisation, has a clearly designated PIC responsible for validation, periodic review, and corrective action when anomalies are detected.

By embedding data standards and governance mechanisms into the core architecture, interoperability becomes not just a technical connector, but a strategic enabler of intelligent public services that strengthen public trust.

A Continuing Journey

Rumah Pendidikan continues to evolve as an initiative to institutionalise integration, discipline, and cross-sector collaboration. The transition toward intelligent governance requires continuous refinement and strategic clarity regarding long-term goals. As the platform matures, the success of this digital infrastructure will be measured by its tangible impact on policy coordination, resource allocation, and the improvement of educational outcomes.

Since its launch in early 2025, the platform has attracted more than 5 million users nationwide and earned both national and international recognition, including acknowledgment at the International Customer Experience Awards 2025 and recognition among the Top 10 Global EdTech Prize recipients at the World Schools Summit 2025.

Early progress also indicates encouraging momentum in institutional adoption. More than 1,300 dissemination activities have reached approximately 29,000 education units nationwide, while institutionalised Data Quality Management practices have produced overall data quality scores of around 98 percent for the school budgeting system (ARKAS), 95 percent for the school procurement platform (SIPLah), and about 97 percent for core educational datasets covering schools, education personnel, and students.

In the end, interoperability builds connectivity. Data quality builds intelligence. Together, they build the future of public sector collaboration

CDAO Indonesia is happening on 12 May. Join us for more insights on data, analytics, and AI!