Designing an Effective Data Governance Framework for Plastic Waste Management

Southeast Asia faces a range of data-related challenges that hinder effective decision-making in plastic waste management. Inconsistent data quality and availability across and within countries limit the development of evidence-based strategies. Fragmented data sources, varying standards, and incompatible formats complicate data integration and obscure the full picture of plastic flows. Many countries also lack the technological infrastructure and tools for effective collection, storage, and analysis. Regulatory and legal differences around data use and privacy further complicate cross-border collaboration, while limited stakeholder capacity and nascent open data initiatives contribute to slow progress in data transparency and informed decision-making.

Compounding these data issues are structural barriers in waste management systems, limited recycling capacity, and economic and social constraints. Addressing these challenges requires infrastructure investment, adoption of advanced recycling technologies, public education, and policy reforms to support sustainable waste management practices.

Digital solutions offer promising pathways to strengthen plastic waste data systems and support a transition toward a circular economy. Real-time data collection and monitoring tools, blockchain for transparency, and mobile apps for community engagement can vastly improve plastic waste traceability and accountability. Indonesia and Viet Nam are piloting such technologies, including blockchain platforms and waste management apps such as Grac, mGreen, and VECA. Regional collaboration such as the Global Partnership on Plastic Pollution and Marine Litter Data Hub also supports data sharing and coordination. By leveraging digital innovations and integrated data platforms, Southeast Asia can improve plastic waste governance and achieve circular economy goals.

Data governance is a framework of policies, processes, and roles that guide the management, use, and protection of data within an industry, country, or ecosystem. It establishes oversight and accountability for data-related activities, ensuring data is accurate, secure, and used effectively to achieve strategic objectives. Governance is not an end in itself, but a means to enable better decision-making, compliance, and operational efficiency. Without it, data becomes fragmented, inconsistent, and unreliable that leads to inefficiencies and missed opportunities.

Several key principles set the boundaries and scope of data governance. These help institutions establish a structured approach to managing plastic waste data, ensuring that it is used effectively to achieve the objectives of plastic waste management. These principles are the following:

• Integrity. Stakeholders must act truthfully and transparently in all data-related decisions—crucial for building trust and credibility among stakeholders.
• Transparency. Data-related processes and decisions must be clear to all participants, helping them understand how data is managed and used, which promotes accountability.
• Data accuracy. Ensures data is accurate, valid, and reliable, which are fundamental for making informed decisions and driving operational efficiency.
• Data accessibility. Provides timely, reliable, and secure access to data for authorized users, allowing the right people to have access to the data they need when they need it.
• Data consistency. Maintains uniform data definitions, formats, and values across different systems and processes, reducing errors and inefficiencies.
• Compliance. Ensures data management adherence to regulatory and legal requirements to meet the necessary standards and regulations.
• Accountability. Clearly defines roles and responsibilities for data management to ensure tasks are carried out correctly and efficiently.

Governance, whether applied to data, organizations, or systems, always revolves around two fundamental questions:

• What should be governed? – Identifying the scope, assets, or domains that require oversight. 

• How should it be governed? – Establishing the mechanisms, policies, and structures for oversight and control.

  Figure 2: Data Governance: A Logical Framework

  

  Source: Author.

  By systematically addressing both questions, entities create a structured approach to oversight. Figure 2 presents a logical framework that shows how governance can be both purposeful (focusing on the right areas) and effective (with clear processes and enforcement mechanisms), leading to better decision-making, risk management, and value creation.

Through an Asian Development Bank regional technical assistance project, consulting firms DT Global and Seureca-Veolia developed a data governance operating model for Indonesia and Viet Nam. Titled Data Governance Framework: An Approach for Data Management in Plastic Waste, the model provides a structured framework that outlines who is responsible, what policies and standards must be followed, how decisions are made, and how compliance is monitored and enforced.

The model defines clear roles, responsibilities, and relationships among key stakeholders such as the national and regional government agencies, industry representatives, and communities. It also establishes mechanisms—for setting objectives, managing risks, and monitoring performance—aligned with strategic data management goals.

As an example, consider the proposed data governance operating model for Indonesia (Figure 3), with the different numbered sections discussed in more detail below:

Figure 3: Data Governance Operating Model for Indonesia

ADB = Asian Development Bank, UNEP = United Nations Environment Programme.
Source: Author.

1. Management levels. Data governance is organized across three levels: (i) strategic, for high-level or national decision-making, (ii) tactical, for coordination and alignment, and (iii) operational, for data management activities. Each level has a specific scope, roles, and responsibilities to effectively coordinate governance activities.
2. Decision-making structures. Each level includes representatives from the different stakeholders with clearly defined roles and responsibilities for oversight, conflict resolution, policy enforcement, and monitoring.
3. Scope of governance. Governance in plastic waste management involves overseeing a complex ecosystem of data, stakeholders, and regulatory requirements. The strategic-level Plastic Waste Data Strategy Committee defines the scope for data governance within a specific environment, which are distributed to the decision-making structures and management levels and operationalized through policy implementation and enforcement mechanisms.
4. Feedback, monitoring, and evaluation processes. Effective governance involves bottom-up monitoring and feedback loops. Performance data from operational activities—such as waste collection, segregation and data reporting—enables decision-makers at the strategic level to identify gaps, enforce penalties or corrective actions, and continuously improve the data governance system.

Effective plastic waste management in Southeast Asia depends on strong data governance frameworks that are practical, inclusive, and enforceable. By clearly defining what should be governed—such as data assets, processes, stakeholders, and compliance areas—and how it should be governed—through policies, roles, structures, and monitoring mechanisms—stakeholders can ensure data is accurate, secure, and actionable.

A well-designed and functioning governance operating model connects strategic intent with operational execution, supported by a bottom-up enforcement loop that keeps the system accountable and adaptive.

As the region moves toward digitalization and a circular plastic economy, digital tools and collaborative governance will be essential to unlocking the full potential of data in achieving sustainable outcomes.

Note: Data governance for plastic waste management will be part of the discussions during ADB's Circular Economy Forum, particularly on 18 June (Wednesday), 11:00 a.m.–12:30 p.m., with the session Data Governance Framework, under Track 3: Digitalization of the Plastic Value Chain. For more details, see the program here.