Siloed climate data challenges

Physical climate risk data: the more we learn, the less we know? Khalid Azizuddin's recent piece in *Responsible Investor captures well what many practitioners are grappling with today: - asset-level data that remain incomplete or hard to interpret; - physical hazard exposure often disconnected from financial materiality; - little visibility on supply chains or customers; - adaptation and resilience efforts largely ignored; - and a risk of over-simplifying complex realities into a single “score.” Some three years ago, EDHEC Business School set out to address exactly these challenges, working to advance climate risk modelling and make decision-useful for investors, companies, and public authorities. In this work, we have developed: 🔹 a blueprint for a new generation of probabilistic climate scenarios; 🔹 high-resolution geospatial modeling capabilities to allow for geographic and sectoral downscaling, consistent with each scenario; 🔹 an open database of decarbonisation and resilience technologies through the #ClimaTech project, which officially launched this week. While the research is public, the new EDHEC Climate Institute has also been assisting a school-backed venture, Scientific Climate Ratings (SCR), which integrates this research to deliver forward-looking quantification of the #financialmateriality of climate risks for infrastructure companies and investors worldwide. While SCR provides a rating scale for comparability, it avoids the trap of over-simplification. Each rating is backed by probabilistic scenario modelling, analysis of physical and transition risk exposures, and explicit accounting for adaptation measures. The result is a synthesis that remains transparent, interpretable, and anchored in scientific rigour. Together, these initiatives aim to move the discussion from data abundance to decision relevance, equipping practitioners with tools that connect climate science, finance, and strategy.

What if you could access all the data you need for sustainability decisions, in one place? In 2017, Google took a big step towards making that possible with the launch of the Data Commons Initiative. Imagine how hard it was, just a few years ago, to track climate data or emissions across different states or countries. Everything was scattered—thousands of databases, each with its own format, locked in silos. Climate predictions, emissions, demographics, and economic impacts were fragmented, making it nearly impossible to see the bigger picture. Data Commons changes that. It’s now one of the largest sustainability-focused data repositories in the world, pulling together insights from sources like NASA’s climate projections and the Environmental Protection Agency’s emission reports. With just a few clicks, you can pull up greenhouse gas emissions across all 50 U.S. states or project temperature peaks in a region like India. What makes this valuable isn’t just the scale of the data—it’s the connections we can build. When you have climate data, economic indicators, and social statistics all in one place, you start seeing patterns. You begin understanding the broader impact of emissions on communities, or how economic changes relate to environmental shifts. For decision-makers, this kind of insight is a game changer. It allows organizations to make choices that aren't just efficient but also sustainable, aligning business goals with environmental impact. Seeing the world this way—data brought together to tell a story—can feel eye-opening, even a bit sobering. But it's the kind of perspective we need if we’re going to tackle climate change together. #DataCommons #Sustainability #ClimateChange #DataInsights #EnvironmentalImpact

South Asia’s climate crisis is already a displacement crisis but our region still treats the most critical resource of all as confidential: data. In my latest article for the Refugee Law Initiative at the School of Advanced Study, University of London, University of London, I explore how siloed climate and migration information is quietly fuelling humanitarian risk and why cross-border data transparency is no longer optional but foundational to climate security. Key Takeaways: • The biggest bottleneck isn’t tech, it’s political mistrust that turns life-saving data into a strategic asset • Data-sharing strengthens disaster response and delays make migrants, women and marginalised groups absorb the worst impacts • Border hotspots (Sundarbans/Assam floodplains/Himalayan basins) are climate + geopolitics combined, but data remains siloed within states If South Asia wants to anticipate climate-driven mobility instead of reacting to it, the shift must happen now: from sovereignty → shared security. Would love to hear how others working on climate, borders, or migration are thinking about this. Read the full article here: https://lnkd.in/e7iMVQe6 #ClimateMigration #SouthAsia #DataGovernance #ClimateSecurity #RegionalCooperation

Most companies right now are investing a lot of time and money in accuracy. Clean data. Verified certificates. ESG dashboards with every checkbox ticked. That’s important — but it’s only half the battle. Because if that data isn’t actionable across the business, what’s the point? In our conversations with teams handling climate compliance, the pattern is the same: They’re buried in spreadsheets. They’ve got some third-party platforms. But when procurement wants to screen a supplier, or finance needs to evaluate climate risk, they’re still waiting on a PDF or someone to interpret a system they don’t have access to. This is the real bottleneck. Not just “do we have the data,” but: – Can the right person use it at the right time? – Is it structured enough to inform supplier selection, risk disclosures, or design decisions? – Can we connect sustainability goals with actual financial and operational levers? If the answer’s no — then all you’ve built is a reporting silo. And compliance will always feel like a burden. But if your data is structured, traceable, and accessible? Then, climate compliance becomes something else entirely: A decision-support system. A source of business intelligence. A catalyst for smarter choices. The shift we need isn’t from unverified to verified. It’s from buried to usable. That’s how we stop climate reporting from being a cost center — and start making it a competitive edge.

3 years of weather data, never used... I spoke to the head of digital at one of South Asia's largest fertilizer companies last week. They've spent three years collecting weather data. 14-20 parameters. Village and district level. Across their entire geography. But it's not being used... His words: "We have all the data. It's just not in the right context to make decisions from." That sentence stuck with me, because it's not really about weather data. It's a microcosm of where most agri-input companies are sitting right now. The data exists. Historical sales. Dealer stock movements. Seasonal patterns. Weather. In silos, each of these is interesting. Connected, with the right interpretation layer, it becomes a forecast. But that last step, converting good data into usable intelligence, is still the gap. What makes it worse, all of this is backward-looking. It tells you what happened. It doesn't tell you what's about to happen. The forward signal already exists. It's just not being captured. Every week, their field team is walking into retailer meetings, farmer group discussions, distributor conversations. They're hearing what's on people's minds; which crops are shifting, which problems are intensifying, which products are moving. That's not anecdote. Across 1,000+ field staff, that's a real-time sensor network proxying millions of farmers. But none of it gets recorded. Because it takes too long to write down. So the company sits on three years of weather data they can't fully act on, while their best forward-looking intelligence evaporates after every field visit. The problem isn't data collection. It's context. And context doesn't come from more dashboards. It comes from closing the gap between what your field team sees and what your planning team knows. Myca is a context powerhouse. Not just capturing, but converting real-time field intelligence into strategic action that drives sales, improves margins, and automates operational decisions.

Something I keep coming across in my research on retail logistics, and in conversations with others in the field too is - it's not that organizations lack data or they don't have the right tools. The challenge is that data often lives in silos, and the time to connect it rarely exists. Routing decisions in one system. Carrier performance in another. Emissions data, if it's tracked at all, somewhere else entirely. By the time someone pulls it all together, the shipment has already moved and the decision is made. This is why so many logistics decisions still feel reactive. Not because people aren't trying, but because the way data flows, or doesn't, makes proactive decision making incredibly hard in day-to-day operations. So, what would it actually look like to change this? 👇 🔗 Connect the silos first: Before any model or algorithm, the foundational work is integrating routing, carrier, cost, and emissions data into a single view. Tools that help: SQL pipelines, data warehouses, API integrations between TMS and ERP systems Someone has to build it, but when it exists, everything else becomes possible. 📊 Build models that do the heavy lifting: Once data is connected, multi-objective optimization models can evaluate routing decisions across cost, service level, and carbon emissions simultaneously. Scenario analysis tools let teams run 'what if' comparisons in minutes rather than hours. The math isn't the barrier. The integrated data is. ⚡Make outputs actionable, not just reportable: EPA SmartWay emission factors layered onto route cost data can produce a live scenario comparison, showing the true financial and environmental cost of every routing option before a decision is made. That's not a complex build. It's a smarter use of what already exists. Here's what I keep coming back to - the tools and methodologies to do all of this already exist. The gap isn't technical capability. It's time, integration, and organizational will to prioritize it. That's exactly the problem worth solving. 🌱 #RetailLogistics #SupplyChainAnalytics #DataDrivenDecisions #LogisticsOptimization #Sustainability