By Jithendra Antonio
Amidst the sweeping wave of digital transformation, Sri Lanka stands at a consequential inflection point. While government decisions often rely on experience and tradition, the power of data can unlock solutions grounded in real-time evidence, directly addressing the needs of the people. Data-driven governance has proven transformative globally streamlining services, tackling corruption, and ensuring that policies have tangible benefits. Sri Lanka can harness data for evidence-based policymaking, offering specific steps and successful international examples to guide a brighter future.
Understanding the Stakes: Why Evidence-Based Policymaking Matters
Countries like Estonia, Singapore, and Rwanda have shown that data-driven governance can reduce inefficiencies, improve public services, and attract international investment. For Sri Lanka, adopting evidence-based policymaking could address pressing issues like poverty reduction, healthcare accessibility, environmental management, and economic growth. With a robust data ecosystem, Sri Lanka’s policies could be targeted, timely, and based on quantifiable impacts rather than assumptions.
Case Study: Singapore’s COVID-19 Response
During the COVID-19 pandemic, Singapore used data analytics to manage healthcare resources, track virus hotspots, and guide policy responses. As a result, the government made swift, informed decisions that minimized economic disruptions while protecting public health. This case underscores the potential of data-driven policymaking to safeguard both lives and livelihoods.
Data Collection - Building the Foundation
Effective data-driven policy begins with reliable, comprehensive data collection. Sri Lanka needs a centralized framework that can collect, store, and analyse data across sectors—from healthcare and education to the environment and finance.
- Strengthening Digital Infrastructure – Investment in digital infrastructure is essential for data collection and integration. Public Wi-Fi zones, data hubs, and high-speed internet access should be prioritized to ensure data can flow from rural regions and underserved communities into centralized systems.
- Cross-Agency Data Sharing – For data-driven policy to work, ministries and public agencies must share data openly. Establishing secure protocols for data sharing and setting up a central repository could eliminate duplication and allow data to be cross-referenced for broader insights.
- Data Collection at Local Levels – Just as Rwanda has effectively used local data collectors, Sri Lanka could empower local government offices to gather data at the grassroots level. Training programs could ensure that local offices gather standardized, high-quality data that reflects community realities.
Analytics and AI - Turning Data into Insights
Collecting data is only the first step. Advanced analytics tools, including artificial intelligence (AI) and machine learning (ML), can reveal patterns, project trends, and evaluate potential policy impacts before they’re implemented.
Case Study: Estonia’s AI-Driven Governance
Estonia uses AI extensively in its public services, from judicial decision-making to welfare distribution. By analyzing trends and predicting outcomes, the government tailors services to real needs, cuts waste, and improves citizen satisfaction. A similar approach could help Sri Lanka prioritize resources in areas like healthcare, agriculture, and disaster management.
- Establishing a National Data Analytics Center – This center would house AI and data analytics experts who could interpret data for different ministries, provide training, and develop models to evaluate potential policies.
- Predictive Analytics for Social Programs – Using predictive analytics, Sri Lanka can design policies that address issues like poverty before they worsen. For example, by identifying at-risk communities, the government could proactively allocate resources to prevent malnutrition or educational disparities.
- Evaluating Policy Effectiveness with Real-Time Data – AI could track and evaluate policy impacts in real-time. For instance, in agricultural policies, machine learning algorithms could monitor crop yield data to assess whether policies supporting farmers are effective, allowing for rapid adjustments.
Implementing Evidence-Based Policymaking
Transforming policy into evidence-based processes will require initial investments in technology, training, and cross-sector collaboration. However, the payoff—more efficient policies and more satisfied citizens—makes this a worthwhile endeavour.
- Pilot Programs with Data-Driven Policies – Pilot programs in sectors such as healthcare, education, and agriculture would allow the government to test data-driven policies, refine the approach, and demonstrate proof of concept. For instance, Sri Lanka could start by using data to allocate healthcare resources to underserved areas.
- Public-Private Partnerships (PPPs) for Data Infrastructure – Private sector partnerships can support the creation of a robust data infrastructure. Tech companies can bring expertise and innovation, while the government ensures data privacy and ethical standards are upheld.
- Training Government Officials – A culture shift is required for data-driven policymaking to thrive. Training programs should focus on teaching government officials how to interpret data, understand analytics tools, and integrate insights into policy development.
- Transparency and Public Engagement – Transparency is essential to build trust in data-driven policies. By making data public and explaining policy decisions, the government can enhance accountability. Estonia, for example, maintains public dashboards showing real-time government spending and policy impacts, allowing citizens to see where their tax dollars go.
Addressing Privacy and Ethical Concerns
With data-driven policy comes the need for data security and ethical guidelines. Ensuring that data collection, storage, and analysis meet privacy standards will be crucial for maintaining public trust.
- Data Protection Laws – Establish strict data protection laws to govern how citizen data is collected, stored, and used. Lessons can be drawn from Europe’s GDPR model, which mandates consent, transparency, and data security.
- Ethics Committees for Data Use – Form independent ethics committees that review government data projects, ensuring that data usage aligns with citizens' rights and interests. These committees would add an extra layer of oversight and public assurance.
- Citizen Control of Personal Data – Implement systems that allow citizens to control their own data, similar to Estonia’s X-Road platform, where citizens can view which government bodies access their data and can contest if they feel their data is misused.
The Path to a Data-Driven Sri Lanka
By embracing evidence-based policymaking, Sri Lanka can make decisions rooted, not speculation, leading to more effective, efficient, and equitable governance. Investing in data infrastructure, fostering a culture of analytics, and ensuring transparency will create a transformative foundation for Sri Lanka’s future. This is not just a strategy; it’s a path to progress. By adopting data-driven policy, Sri Lanka can harness the power of technology to shape a brighter, more resilient tomorrow.
(The writer is a Consultant specialised in Data Analytics with a Special Focus on Sri Lanka’s Future Direction, and in the fields of Sustainable Energy, ESG, Investments and telecommunications. He can be reached at jithendra.antonio@gmail.com.)