Data Strategies for Public Good in 2025

With 4 years left of the SDGs, it is worth pausing to reflect on what a decade of monitoring has truly taught us. The United Nations Statistical Commission’s review of SDG measurement is both impressive and candid. Over the past decade, the global indicator framework has expanded substantially, strengthened national statistical systems, improved coordination across agencies, and embedded disaggregation more systematically into development reporting. The SDG framework has become a genuine global public good. At the same time, the report is clear about structural lessons that can inform any post-2030 framework. (1) Alignment: between policy ambition and statistical feasibility must happen earlier. In several cases, targets were politically negotiated before indicators were methodologically mature, creating complexity, uneven implementation, and reporting burdens for countries. (2) Capacity constraints: remain significant, particularly in lower income contexts. Many countries still face resource limitations, data gaps, and uneven ability to report regularly across all indicators. (3) Simplicity: one of the report’s strongest lessons is that the SDG framework became highly complex, with a large number of indicators creating reporting burdens and stretching national capacity. Future frameworks should focus on fewer, clearer, high value indicators that drive decision making rather than compliance reporting. The opportunity for the private sector is not to replace official statistics, nor to create parallel systems. It is to strengthen the architecture that already exists and to bridge any residual data gaps that may exist . Two opportunities stand out. - The emphasis on involving statisticians early in agenda setting. Private sector research organizations can contribute methodological expertise upstream, helping ensure that future development targets are measurable, comparable, and grounded in robust survey and data science practices from the outset. - The use of non traditional data sources, as an important evolution in SDG monitoring. Private sector datasets, including high quality experiential and behavioral data, can complement national sources. But this integration must meet standards of transparency, documentation, interoperability, and quality assurance consistent with official statistics principles. Gallup’s World Poll illustrates what this alignment can look like. Nationally representative, globally comparable survey data have supported SDG related insights on food insecurity, forced labor, diet quality and safety. These data have helped illuminate dimensions of development that are not captured in administrative or consistently captured in national survey systems alone. The broader lesson from a decade of SDG monitoring is clear: sustainable development requires sustainable data systems. Those systems must be methodologically sound, institutionally anchored, and collaborative by design. The report link is in the comments below.

🔍 I've been thinking deeply about what makes data-powered governance truly effective. After some observation and some experience, I've identified three critical ingredients – what I humbly call the "Three D's". 📊 Data Exchange Platforms: The foundation that enables innovation through open data sharing and collaborative models. Estonia's X-Road has revolutionized public services by creating a secure data exchange layer connecting government databases. Citizens can access nearly all government services online, with 99% of public services available digitally. Singapore's Smart Nation Sensor Platform integrates data from sensors and IoT devices across the city to optimize everything from traffic flow to energy consumption. 📜 Data Policies: The essential guardrails that establish trust. The European Union's GDPR has set a global standard for data protection, enhancing citizen trust while creating a framework for responsible innovation. Closer home, the DPDP will start to set benchmarks for data-centric guardrails for a massive, diverse, and data-rich country like India. 🧩 Decision-Support Systems: The mechanisms that transform data into action. South Korea's COVID-19 response leveraged their Epidemic Investigation Support System to enable rapid contact tracing while maintaining transparency with citizens. Also, New Zealand's Integrated Data Infrastructure connects data across government agencies to inform policy decisions with robust economic analysis, resulting in more targeted and effective social programs. 💡 When these 3D's are combined deftly by the public-sector, citizen-centric governance becomes the cornerstone for any government. For the scale India operates at, it's a very good opportunity to show the way for the Global South. 🤔 I think we're at that inflection point with the recent announcement of AI Kosha and the DPDP, and they can help safely incubate innovative solutions that will optimize the delivery of government schemes, thereby ensuring timely, targeted assistance for citizens. Thoughts? #DigitalTransformation #PublicSector #Innovation #DataStrategy

Data is at the heart of how civil society organizations operate and coordinate their efforts. To effectively address the complex challenges we face, understanding and managing data is crucial. On Tuesday, our TechSoup Talks 2025 series focused on data governance. We heard insightful discussions from Fabio Fraticelli Fradicelli of Social Techno, TechSoup Italia , and Héc Maldonado-Reis, MS, AM, MPH of Tech Impact. Here are five things I took away from their talks on data governance: ✅ Data Governance is a Framework: Data governance goes beyond managing data. It includes policies, operational, and programmatic decisions to ensure data is valid, secure, replicable, and responsibly used. Data governance involves strategic thinking about implementation, evaluation, and the people and values associated with the data. ✅ Clarity on Roles and Responsibilities is Essential: Defining who is responsible, accountable, consulted, and informed regarding data collection, sharing, and leveraging processes is foundational for effective data governance. This clarity, even for small organizations, can be established through working groups or designated data stewards. ✅ Processes and Standards Drive Data Quality: Informal data management, often relying on disparate spreadsheets, can make it difficult to identify patterns and measure impact. Establishing basic guidelines and internal protocols for data collection and hygiene are critical steps toward reliable and consistent data. ✅ Purpose and Impact Must Be Clear: Organizations need to clearly define why they are collecting data – whether for internal decision-making, improving management control, increasing awareness about their mission, or building powerful counter-narratives based on evidence. A clear purpose ensures data is useful and impactful. ✅ Grapple with Data Justice and Power Dynamics: Data governance must consider historical and current power dynamics, focusing on who makes decisions and interprets information. It's vital to engage communities in understanding how their data is used and understood.  This rigor builds trust and ensures relevance in reporting. The two talks highlighted that significant progress in data governance doesn't necessarily require large budgets, especially with the increasing accessibility of AI-based tools that can assist with data cleaning and consistency. These steps are vital for building resilient civil society organizations. I've dropped links to the recordings of both talks in the comments below. The TechSoup Talks 2025 series continues all week, focusing on digital security, data governance, misinformation, counter-narratives, and collaboration. #TechForGood #CivilSociety #Nonprofits #NPTech #DataGovernance #Philanthropy #DigitalTransformation

#AI | #Blockchain : MahaAgri-AI Policy 2025-2029 .  The key objectives that the department of Agriculture seeks to achieve through this policy are : 1. Develop and deploy a statewide food traceability and quality certification platform as part of #DPI : Establish a digitally integrated platform that ensures end-to-end traceability of agricultural produce and enables verification of food quality through credible government backed and internationally recognised certifications. Leveraging AI, blockchain, QR codes, and #IoT, the platform will enhance transparency, support compliance with national and international standards, and improve market access for farmers and producer collectives. 2.  Promote Farmer Centric Design and Adoption: Ensure farmers are co-creators in AI solution design by enabling participatory model development, multilingual advisory delivery, and community-based piloting mechanisms 3. Deploy Remote Sensing-Based Engine as a Shared Digital Public Good for the state: Deploy a unified, AI-enabled Remote Sensing Intelligence Engine to serve as a shared digital public good across multiple departments. This engine will process satellite imagery, drone feeds, and GIS datasets to generate high-resolution insights on land use, crop health, water availability, soil moisture, vegetation indices, and disaster risk. 4. Build Digital Public Infrastructure for Agriculture (DPI-A): Operationalize the Agriculture Data Exchange (ADeX), expand weather and soil sensor networks, and integrate with platforms such as Agristack and MahaAgriTech to support AI readiness 5. Mainstream GenAI and Emerging technology across #Agriculture value chain: Deploy context-specific GenAI and emerging technology enabled tools for crop planning, disease and pest prediction, irrigation management, supply chain optimization, post harvest handling, and market access.

For years, governments have talked about becoming data-driven. But here’s the uncomfortable truth: Most see data as a technical resource. They don’t yet see it as an architectural imperative. That’s why so many ambitious analytics and AI initiatives stall. Because the problem isn’t data volume or tools. It’s the governance, structure, and authority to make data operational — across an entire institution. Solving for this is what our new CDO Playbook for Government is all about. We show that: · Data leadership is no longer a back-office function. Today’s Chief Data Officers must be mission leaders — shaping strategy, policy, governance, and outcomes, not just technology. ·  Data governance must be enterprise-wide, not siloed. Without clear authority, standards, and accountability, data becomes a source of confusion and risk — not value. ·  AI cannot scale without a data foundation that is fit for purpose. High-quality, governed, trustworthy data is the engine of responsible AI — and the CDO is the architect of that engine. In government, every strategic decision, operational choice, and policy calibration depends on reliable data — yet most institutions still lack the structures to manage data as a strategic asset. Some straight talk: Data strategy without governance is wishful thinking. AI without data readiness is half-built. Analytics without strong stewardship is risk. If a government agency wants to move from episodic reform to continuous impact, it needs to: #1. Elevate data leadership #2. Build governance that sticks #3. Treat data as the connective tissue of mission outcomes That’s the real operating-system change. Not faster tech adoption. Not more pilots. More insights on this — and what it means for future government performance — in our CDO Playbook and in the trends we’re unpacking through 2026. Adita Karkera, Ph.D. Matthew Gracie Joseph Mariani Mark Urbanczyk Kunal Shah John Jacobson Tess Webre Bill Gehrig Aman Vij Todd Johnston Brigid Dunn Stephen Goldsmith Oliver Wise Suma Nallapati Rochelle Haynes Abed Ali Prabhu Kapaleeswaran Tasha Austin-Williams, Ph.D. Mike Greene Uday Katira Monica McEwen Jason Wainstein Vishal Kapur William Frankenstein https://lnkd.in/gcja8958