Developing institutional mechanisms for continued use of data in policymaking requires a multi-pronged approach that involves establishing clear processes, building capacity and fostering a culture of evidence-based decision-making. This article will take policymakers through the scope and benefits of different types of institutional mechanisms – at the local, national and international level. Closely linked to building data partnerships, it provides guidance on what’s worked for different governments in establishing such coordination processes.
Why institutional mechanisms?
Institutional mechanisms provide frameworks for continuous use of data in decisions, allowing organizations to identify trends, evaluate performance, and make informed adjustments promptly. This promotes agility and the ability to adapt strategies and operations based on real-time information. What goes wrong without institutionalising use of data in decision making?
Data silos and fragmentation: In the absence of coordination mechanisms, different departments or units within an organization may operate in isolation, leading to data silos. Data remains scattered across various systems, formats and locations, making it difficult to access and integrate for decision making.
Bias and subjectivity: Institutional mechanisms help establish standardized processes that reduce bias and subjectivity in decision making. Without such mechanisms, decision makers may rely on personal biases, intuitions or anecdotal evidence instead of objective data.
Without institutional coordination mechanisms, duplication of efforts is a common problem that organizations may encounter. Do you see redundant data collection activities being carried out independently by multiple groups in your organization that wastes time, efforts and resources?
Misalignment between data producers and users: Without institutional mechanisms, data producers may not fully understand the specific needs and requirements of data users, resulting in data that may not be relevant or actionable. Similarly, data users may not effectively communicate their needs or provide feedback on the data they receive, making it difficult for data producers to improve the quality or relevance of the data.
How to get started
Remember the challenges faced by all governments globally around collecting data during the COVID-19 pandemic? Most of you may have heard about discrepancies in the reported COVID-19 statistics, such as the number of confirmed cases, deaths, recoveries, testing rates and other related data coming from different sources or authorities within your country. While there were many reasons for this given the scale and uncertainty of the challenge, inter-stakeholder coordination was indeed one of the major issues that significantly impacted the accuracy and consistency of COVID-19 data reporting. The involvement of multiple stakeholders, such as different government agencies, health departments, local authorities, healthcare providers, laboratories and other organizations, in collecting and reporting COVID-19 data can lead to a huge challenge of coordination in almost every country.
Before we go into what a good coordination mechanism may look like in situations like these, let us look at how human capacities are currently being utilized in different existing institutional mechanisms. Do you really need to build another mechanism for coordination? Or can you leverage existing ones? Follow the map to gain a better understanding of existing structures:
Figure 1: Process of designing institutional mechanisms
Figure 1: Process of designing institutional mechanisms
Scope of institutional mechanisms
There’s no one right way of establishing institutional mechanisms. However, some practices have specifically worked well for policymakers trying to coordinate policy decisions at local, national and international levels.
Figure 2: Scope of institutional mechanisms
Figure 2: Scope of institutional mechanisms
Multistakeholder working groups
Multistakeholder working groups on specific themes and subjects bring together individuals with specialized knowledge and expertise to address specific challenges, initiatives or projects. Most recently during the COVID-19 pandemic, many countries established task forces dedicated to pandemic response and management. These task forces included experts from various fields, such as infectious diseases, epidemiology, public health and healthcare delivery. They worked together to develop strategies, guidelines and protocols for testing, contact tracing, vaccine distribution and overall pandemic response. Some of these mechanisms indeed need to be developed ad-hoc with the nature of the issue in hand. However, establishing such working groups/task forces around various issues means more cross-functional collaboration and pre-existing mechanisms that enable rapid response and agility. This article further focuses on things to keep in mind while establishing and utilizing such task forces for data-driven decision-making.
To ensure that data is shared in a transparent, accountable and secure manner in such coordination groups, it's crucial to establish a governance framework. This framework should define roles and responsibilities of each member of the group, data sharing agreements, privacy protections and other key considerations.
Best practices and recommendations
Considercarefullywho to assign to multistakeholder groups
Figure 3: Composition of multistakeholder groups
Figure 3: Composition of multistakeholder groups
Establish clear roles and responsibilities
Members are typically assigned specific roles and responsibilities based on their expertise, experience and the objectives of the task force. Common responsibilities include a) leading overall direction of the task force (chairperson), b) sharing subject matter expertise and data, c) planning initiatives and next steps, d) stakeholder liaison and e) communication and outreach. It’s good to establish a meeting frequency and other terms of reference while keeping in mind that some operational flexibility is needed.
The Bangladesh Bureau of Statistics (BBS), the national statistical agency, collaborates with various stakeholders to design and conduct national household surveys. These surveys aim to gather data on various socio-economic indicators, such as poverty, employment, health, education, and living conditions. BBS engages with government ministries, development partners, research institutions, and most importantly civil society organizations to identify the questionnaire design and sampling methodologies to ensure inclusive and comprehensive data collection at the grassroots, more remote areas.
Establish data sharing agreements, standards & protocols
Specifically for data, establish data sharing agreementsthat outline the terms and conditions of data sharing. This includes identifying what data will be shared, who will have access to the data and how the data will be used.
To ensure consistency and accuracy in data exchange between different stakeholders, it's important to establish data standards and protocols that specify what data should be shared and how. This includes defining data formats, data dictionaries and data quality standards.
Citizen Generated Data (CGD) has been offering an important complement to official data produced by national statistical offices (NSOs) in driving forward a data revolution for sustainable development. In Kenya, the NSO is providing data stewardship to guide production and use of CGD. Clear guidelines on governing the process of data production and compliance have been developed such that CGD can be integrated into official statistical production.
Institutionalize monitoring and evaluation mechanisms
Monitor, evaluate and institutionalize these groups (make them an integral part of your functioning) such that they’re a part of the data collection and management process. Consider practices shared in "Monitor policy implementation" and "Evaluate policy impact".
Build data centres
Establish dedicated data centres or similar units within the government organization. These centres can serve as hubs for data-related expertise, research and collaboration, providing consultancy services and support for data-driven initiatives across the government. They can invite, as needed, subject matter experts while dealing with specific issues.
Rwanda's Gender Data Lab, implemented by PARIS21 in collaboration with the UNDP Chief Digital Office and GIZ, accelerates gender data availability and uptake in Rwanda. The lab operates within the National Institute of Statistics of Rwanda, consolidating existing gender data from relevant government entities and enhancing its availability and accessibility to key stakeholders. The lab plays a crucial role in informing decision-making by analysing gender data, identifying insights and integrating them into policy discussions. By embedding within the National Statistical Office (NSO), the lab fosters gender-responsive statistical practice and promotes awareness, innovation and the dissemination of gender-specific insights. The lab's team consists of regular NSO employees, interns from higher learning institutions of the country and international experts who provide technical assistance in capacity development, data analysis and dissemination. With its dedicated space for collaboration, knowledge sharing and research, the Gender Data Lab serves as a catalyst for advancing gender data practices, promoting innovation and driving progress towards gender equality in Rwanda.
Institutional mechanisms at the national level
At the national level, institutional mechanisms for data-driven decision-making typically involve the establishment of dedicated entities, frameworks and processes that facilitate exchange of data and more uptake in decisions. This involves various federal agencies, non-government partners that can come together to create systems that enable open data sharing. Examples include having data labs and intelligence units as a part of the government machinery. These are in many cases very impactful at the national level as it helps align national priorities, streamline data collection efforts and promote data integration across different sectors. Note that when it comes to the overarching vision of promoting more data integration, the national statistical offices are often an important starting point given the amount of data they host.
Singapore employs Inter-Ministerial Committees to address cross-cutting policy issues. For example, the Inter-Ministerial Committee on Ageing coordinates policies related to the elderly. Similarly, the Inter-Ministerial Committee on Climate Change (IMCCC) enhances Whole-of-Government coordination on climate change policies to ensure that Singapore is prepared for the impacts of climate change. IMCs, an institutional mechanism in Singapore, play a crucial role in facilitating inter-ministerial collaboration, aligning policy objectives, and driving effective implementation. Within this framework, data plays a crucial role in understanding the challenges, assessing the impact of policies, and identifying potential solutions.
The DSPP in Brazil is a data lab that aims to promote evidence-based decision-making in the public sector. It brings together data scientists, policymakers, and government agencies to analyse data, develop predictive models, and generate insights to guide policy formulation and evaluation. In the city of Rio Grande do Sul, the first mission of the lab is to provide five city departments with information that will guide the decisions of the Economic Development, Health, Education, Security and Mobility departments. The plan involves holding regular meetings between researchers and public officials to access and analyse data.
Establishing institutional mechanisms for data-driven decision-making isn’t a one-size-fits-all approach. It can vary depending on the context, objectives and specific needs of an organization or government. The process of establishing institutional mechanisms can be fluid and adaptable to suit the unique circumstances. For example, as an immediate response to the onset of the COVID-19 pandemic, many coordination mechanisms were established at the local, national and international levels – all having a strong element of data-informed decision-making. Various approaches are taken:
Figure 4: Approaches to establishing institutional mechanisms
Figure 4: Approaches to establishing institutional mechanisms
While different approaches may be suitable depending on the issue at hand, remember - establishing institutional mechanisms for data-driven decision-making is an iterative process that involves learning from experiences, feedback and adjustments. It allows for continuous improvement and adaptation based on lessons learned and changing needs. For instance, an organization may start with a small working group focused on specific data projects, learn from their successes and challenges and gradually expand efforts into a more comprehensive institutional mechanism.
In Moldova, the Intelligence Unit for Policy Development (IUPD) framework offers instructions for the establishment and operation of an intelligence unit under the auspices of the Prime Minister's Office of Moldova. The motivation for this arrangement is to ensure both visibility and influence to connect to all possible data sources (public and private), and to progressively cultivate capabilities to address policy challenges and opportunities, based on defined priorities. The primary goal of the IUPD is to substantially enhance the quality and effectiveness of government policies – backed in evidence. The unit strives towards the following objectives:
Cultivate culture and competencies for evidence-based decision-making across the government
Establish a framework for inventive data collection and usage for policy formulation
Create the organizational and technical prerequisites for integrating external data (such as from social media, forums and other discussion platforms) into policy development and decision-making processes
Foster data collaborations among the private sector, public sector and academia, both domestically and internationally
Adopt, cultivate and experiment with tools and methodologies that will be utilized by various institutions and entities to incorporate data into policy-making workflows
Implement specific use-cases based on the priorities determined by the government
Open data portals as institutional mechanisms
Open data portals are also an important institutional mechanism for promoting data-driven decision-making and fostering transparency and accountability. They serve as centralized platforms for governments, organizations and institutions to publish and share their data with the public but also with each other. Some common benefits of having such standardized portals:
Accessibility: Open data portals make data more accessible to a wide range of users, including policymakers, researchers, businesses and the general public. By providing a user-friendly interface and standardized formats, open data portals facilitate easy discovery and retrieval of datasets, enabling users to explore and analyse the data to derive valuable insights.
Transparency and Accountability: Open data portals promote transparency by making government data available to the public. This helps enhance public trust, as citizens can access and scrutinize data related to public spending, service delivery, and performance indicators.
Open data portals encourage collaboration between different stakeholders. Researchers, entrepreneurs and developers can utilize the available data to develop innovative applications, tools and services that address societal challenges.
Empowering Citizens: Open data portals empower citizens by giving them access to information that affects their lives. It enables individuals and communities to engage in informed discussions, participate in public debates and contribute to the decision-making process.
Data.gov.uk is the UK government's open data portal. It hosts a wide range of datasets from government departments, local authorities, and other public organizations. The portal includes datasets on demographics, crime, environment, economics, and other areas, with tools for data exploration and visualization.
The ASEAN Open Data Portal is an initiative that promotes open data sharing among ASEAN member states. It serves as a central repository for datasets from various sectors, including economy, society, and environment, contributed by the participating countries. The portal aims to improve data accessibility, transparency, and usability for informed decision making within the ASEAN region.
Cross-country multistakeholder institutional mechanisms to streamline processes and methodologies in data collection
Collaborative platforms or frameworks bring together multiple countries and stakeholders to promote the use of data for decision-making processes at an international level. These mechanisms aim to enhance data sharing, knowledge exchange and coordination among participating countries and stakeholders to support evidence-based policies and actions. Below are some of the elements being actively considered in such cross-country mechanisms.
Data Sharing and Harmonization: Cross-country multistakeholder institutional mechanisms facilitate the sharing and harmonization of data across borders. They encourage participating countries to exchange relevant datasets, methodologies and best practices, enabling them to compare and benchmark the data against global standards and indicators. This promotes consistency and coherence in data collection, analysis and reporting.
Standards and Guidelines: Cross-country multistakeholder institutional mechanisms play a role in developing and promoting data standards, guidelines and frameworks at an international level. They help to establish common methodologies, terminology and quality control measures for data collection, ensuring comparability and interoperability across countries. These standards facilitate data integration, sharing and aggregation, enabling meaningful cross-country analyses and benchmarking.
Capacity Building and Technical Assistance: These mechanisms often include capacity building programs and technical assistance to support countries in strengthening their data systems and analytical capabilities. Workshops, training sessions and knowledge-sharing platforms are organized to enhance skills in data collection, management, analysis and interpretation. This helps countries improve their capacity to generate, analyse and utilize data for decision-making purposes.
Policy Dialogue and Advocacy: These mechanisms provide a platform for policy dialogue and advocacy on the importance of data-informed decision-making. Participating countries and stakeholders engage in discussions to highlight the benefits of evidence-based policies and advocate for increased investment in data systems, infrastructure and capacity building. Policy recommendations and best practices are shared to promote effective data use and decision-making processes.
The Intergovernmental Authority on Development (IGAD) Member States Djibouti, Eritrea, Ethiopia, Kenya, Somalia, South Sudan, Sudan and Uganda collectively hosted over four million refugees and nearly 13 million internally displaced people in 2022, functioning simultaneously as countries of origin, transit and destination for migrants and refugees. Accurate and comparable migration and displacement statistics are crucial for understanding migration patterns, formulating evidence-based policies and promoting cooperation.
IGAD aligns its efforts with global frameworks such as the Global Compact on Migration (GCM), which emphasizes the collection and utilization of accurate and disaggregated data to inform evidence-based policies, thus recognizing the significance of migration data in policymaking. To facilitate the availability of quality statistics for evidence-based policymaking, IGAD formulated the IGAD Regional Strategy for the Development of Statistics (IRSDS). This strategy ensures the availability of high-quality data and indicators, comparability of regional statistics, effectiveness of the regional statistical system, raised statistics profile and enhanced statistical capacity in the IGAD region. Jointly implemented by GIZ and IGAD, the 'Improving Migration and Displacement Policies in the IGAD Region (SIMPI II)' project, for example, enhances the effective implementation of IGAD's political objectives in migration and displacement with strengthening migration data for evidence-based policymaking being one of the key areas of intervention.
An increasing number of local and regional governments are using the framework provided by the Sustainable Development Goals to elaborate strategic plans, rethink public services and bring together the action of different city departments. The 17 SDGs come with a set of 169 specific targets that can be measured with local indicators. This provides a new “strategic plan” that many territories have never had. Local and regional governments are reporting SDG implementation through a mechanism known as the “Voluntary Local Review” or VLRs, that establish a diagnosis about the priorities of the territory and links them to each of the goals and targets. It is a good source of data gathering as baselines need to be established to show progress on the implementation of the agenda towards year 2030. For example, the Barcelona SDG data has been adopted as an overreaching source of information for the status of implementation of SDGs in the city, disaggregated by target.
How do I know the implementation was successful?
To ensure you’re on track for implementing institutional mechanisms for data-informed decision-making, consider the following indicators:
How can institutional change be initiated?
Initiating institutional change requires a comprehensive and strategic approach, along with sustained commitment and support from leadership and stakeholders. Strategies around building data culture, improving data capacities and facilitating collaboration and partnership all contribute towards this institutional change. To learn more, see the “foster culture change” section.
Related Use Cases
Gender
Breaking Barriers: Reinforcing Gender Data Analysis and Use with the Gender Data Lab Initiative
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