Foundations for effective climate data programs

📢New Article 🌍 As COP29 unfolds in Baku, we get reminded that tackling today's planetary challenges will require a bold, collaborative approach rooted in #openscience. ➡️ At the heart of this effort are two foundational pillars: Data Commons and Data Stewardship. Together, they operationalize the FAIR principles (Findable, Accessible, Interoperable, Reusable) and unlock the full potential of data to drive innovation and solutions. 🔑 Data Commons enable shared access to data, breaking silos and fostering global collaboration. They ensure that resources like climate data or generative AI training sets are equitably accessible, empowering diverse stakeholders to tackle our biggest challenges together. 🛡️ Data Stewardship ensures that data is made accessible for re-use responsibly, building trust and sustainability. Stewards play a vital role in safeguarding data integrity, enhancing transparency, and ensuring long-term usability and sustainability. 🤔 These twin pillars are critical to foster a more equitable, inclusive, and impactful data ecosystem that can accelerate progress toward addressing today's global polycrisis. 💻Read: https://lnkd.in/eXxnqSnK ✍️ With co-author Jean-Claude Burgelman 💻 See also: The need for climate data stewardship: 10 tensions and reflections regarding climate data governance: https://lnkd.in/eve6-X6N #COP29 #OpenScience #DataCommons #DataStewardship #FAIRprinciples #ClimateAction #Sustainability #AIforGood #Polycrisis

One common mistake and challenge associated with corporate carbon footprint measurement is the critical decision of how to set up your organizational structure? Establishing an organizational structure for carbon management is crucial—not just for compliance, but as the foundation for effective climate action. Here’s a breakdown of key best practices to keep your company on the right path: 1️⃣ Define Boundaries Thoughtfully   The first step in carbon management is choosing the right boundary approach (operational, financial, or equity) as outlined by the GHG Protocol. Most companies use Operational Control for simplicity and effectiveness, aligning emissions data directly with decision-making processes. 2️⃣ Prioritize Accountability and Insight   A well-designed structure supports: - Accountability: Assign responsibility for emissions to specific teams. - Data-Driven Decision-Making: Analyze emissions by business unit, facility, or region to identify hotspots. - Goal Setting: Establish baselines and KPIs, setting clear targets across levels. 3️⃣ Start Simple, Scale Gradually   Begin by reviewing your current org chart. Group emissions by practical categories (e.g., facilities, business units). As your data grows, refine the structure for greater granularity, which brings improved visibility without overwhelming your team. 4️⃣ Tailor for Practicality  Focus on physical locations that generate emissions—factories, warehouses, etc. Customizing your approach by industry (e.g., retail, manufacturing) ensures efficiency and clarity. Getting this right now will create a robust foundation for sustainable operations, ensuring your team is empowered to measure, manage, and reduce emissions effectively. 

Effective data collection is the foundation of measuring impact, optimizing decision-making, and improving program efficiency. This guide offers a comprehensive approach to planning and collecting all types of data, ensuring that evaluation processes capture accurate, relevant, and actionable information. By integrating structured methodologies, organizations can streamline their data collection efforts and enhance data reliability. The document provides detailed guidance on selecting appropriate data collection methods, including surveys, questionnaires, interviews, focus groups, direct observations, simulations, and performance monitoring. It explains when and how to use each method, ensuring that data collection aligns with evaluation objectives and program needs. Special emphasis is placed on enhancing response rates, reducing bias, and ensuring data validity, making the guide an essential tool for evidence-based decision-making. For M&E professionals and researchers, this resource serves as a structured roadmap for optimizing data collection strategies. It highlights best practices in designing questionnaires, conducting qualitative and quantitative assessments, and integrating various data sources to support comprehensive program evaluations. By following these strategies, organizations can improve data accuracy, enhance accountability, and drive impactful interventions.

In Chapter 3 of Implementing Data Mesh, Eric Broda and I introduce Climate Quantum Inc., a fictional company used as a case study to apply Data Mesh principles to climate change data management. Climate change is real and its data presents unique challenges due to its vast volume, diversity, and constantly evolving nature. Additionally, businesses face regulatory pressures and the need for precise climate reporting. Climate Quantum Inc. aims to address these challenges by leveraging Data Mesh to make climate data easier to find, consume, share, and trust. By decentralizing data ownership and treating data as a product, Climate Quantum Inc. can manage the complexity and scale of climate data more effectively. The chapter outlines Climate Quantum Inc.'s strategy, which includes creating a Global Climate Data Mesh, a Climate Data Marketplace, and utilizing standardized data interfaces and publishing mechanisms. By employing Data Mesh principles, Climate Quantum Inc. enhances data accessibility, reliability, and transparency. The company’s approach serves as a model for enterprises facing similar data management challenges, demonstrating how to implement Data Mesh to break down data silos, streamline data consumption, and establish robust data governance. Through a detailed exploration of Climate Quantum Inc.’s architecture and organizational design, the chapter provides a roadmap for building a scalable and effective Data Mesh system tailored to the needs of climate data. Find out more on the O'Reilly learning platform at: https://lnkd.in/gD9DE43B #DataMesh #ImplementingDataMesh #DataProduct #ClimateQuantum