Addressing Data Gaps in Sustainability Analytics

🚨 ESG Data Collection: The Hidden Challenges & How to Fix Them 🚨 💡 ESG reporting is no longer optional—investors, regulators, and stakeholders demand accuracy, transparency, and reliability. But behind the glossy sustainability reports, consultants struggle with major data challenges that can compromise credibility. Let’s break down what’s going wrong and how to fix it: ❌ Biggest ESG Data Collection Challenges 🔹 Data Fragmentation – Scattered data across internal systems, third-party providers, and public databases makes consolidation a nightmare. 🔹 Lack of Standardization – Different reporting frameworks (GRI, SASB, TCFD) lead to inconsistent and incomparable data. 🔹 Poor Data Quality – Incomplete, outdated, and unverified data weaken ESG insights. 🔹 Regulatory Complexity – Keeping up with evolving ESG regulations across jurisdictions is overwhelming. 🔹 Limited Stakeholder Engagement – Internal teams lack ESG literacy, and external partners are reluctant to share data. 🔹 Technological Barriers – Manual data handling, reliance on spreadsheets, and outdated reporting tools hinder efficiency. 🔹 Scope 3 Emissions Tracking – Indirect emissions data (from suppliers, product use, etc.) is often unavailable or unreliable. 🔹 Benchmarking Issues – Without consistent benchmarks, comparing ESG performance is challenging. 🔹 Resource Constraints – Lack of budget, expertise, and time to process ESG data efficiently. 🔹 Greenwashing Risks – Companies often overstate ESG achievements without proper verification. --- ✅ How to Bridge These Gaps for Better ESG Reporting 🚀 1. Centralize ESG Data – Use AI-powered ESG data management platforms for real-time data aggregation. 🚀 2. Adopt Standardized Frameworks – Align reporting with GRI, SASB, TCFD, or ESRS to ensure comparability. 🚀 3. Automate Data Collection – Leverage RPA and AI to reduce human errors and improve accuracy. 🚀 4. Stay Ahead of Regulations – Implement compliance tracking tools to monitor global ESG policies. 🚀 5. Strengthen Stakeholder Engagement – Train internal teams and suppliers on why ESG matters and set clear incentives. 🚀 6. Leverage Blockchain & AI – Ensure data transparency and traceability with tamper-proof records. 🚀 7. Prioritize Scope 3 Emissions – Work closely with suppliers and use data modeling tools to estimate impact. 🚀 8. Benchmark Smarter – Compare ESG performance using peer-reviewed, industry-aligned metrics. 🚀 9. Invest in ESG Expertise – Upskill internal teams or hire dedicated ESG professionals. 🚀 10. Ensure Transparency & Avoid Greenwashing – Audit and validate ESG data before publishing reports. --- ⚡ The Bottom Line: ESG reporting is only as strong as the data behind it. The future belongs to businesses that embrace data-driven, verifiable, and standardized ESG practices. What challenges have you faced in ESG data collection? Let’s discuss in the comments! 👇 #ESG #Sustainability #ESGData #DataDrivenESG #ESGReporting #CorporateSustainability

Tech vs. Trees: Bridging the Data Gap in Carbon Markets The rapid growth of nature-based climate solutions has brought with it a surge of technology-driven approaches—advanced remote sensing products, AI models galore, even blockchain-based MRV (Measurement, Reporting, and Verification). Yet, despite these advancements, one surprising yet fundamental gap remains: we lack direct, long-term observations of tree growth in natural forests. Without robust, site-specific data on forest dynamics, carbon stock estimates will remain highly uncertain, risk of misallocating credits will remain high, and trust in nature-based solutions low. What’s missing? More empirical data from diverse, unmanaged forests, particularly in tropical regions. Long-term monitoring of tree growth, mortality, and recruitment across different ecosystems is essential for ensuring the integrity of carbon markets. While monoculture plantations are routinely measured in industry, natural forests—where the real long-term carbon benefits lie—are complex and poorly understood at scale. That's why I was excited to see this recent preprint by Nathaniel Robinson and colleagues: https://lnkd.in/gSabS_yT Can technology help? Absolutely. Technology should be used to improve data collection, not just refine models or build products. Innovations like sensor networks, automated dendrometers, and expanded eddy-covariance measurements can complement traditional fieldwork, scaling up direct observations while reducing uncertainty in carbon accounting. For carbon markets to succeed, we need more boots on the ground, more field measurements, and a stronger commitment to real-world data. Technology alone won’t fix the problem—it’s time to invest in better ecological monitoring, not just better algorithms. If you have ideas on how to unlock observations of natural tree growth at scale, please reach out. Let’s discuss! #CarbonMarkets #ClimateTech #ForestCarbon #MRV #EcosystemScience

Data is fundamental for decision-making, especially in sustainability, where it underpins efforts from measuring project impacts to evaluating policy effectiveness. However, in our rush to gather data, we often overlook a crucial question: is the information we need already available? Many times, organizations jump to new data collection projects without first examining existing resources, leading to unnecessary costs, wasted effort, and environmental impact. Building large analytics teams, purchasing third-party data, and conducting extensive surveys are standard practices, but failing to leverage existing datasets can contradict the core principles of sustainability. In my 15 years of experience working in the sustainability field, I have observed that many organizations don’t make the best use of available data. Too often, the first response to a data need is to collect fresh data, even when high-quality datasets already exist. This results in redundant data collection efforts, with multiple surveys and analyses producing similar findings. For example, in one project, a detailed city transportation survey conducted by another team provided data on vehicle composition and age. Through an analysis of existing data sources, we achieved nearly identical results, showing that sustainable data use is achievable. This experience inspired me to look more critically at how data can be used effectively. In my recent analysis, I estimated Vehicle Kilometers Traveled (VKT) per day by car in different Indian cities using available car sales data and existing datasets. This approach allowed me to produce results that were comparable to findings from previous primary surveys, which typically involve extensive fieldwork and resource investments. Additionally, using existing data enabled me to go further by obtaining detailed breakdowns by car type, engine type, transmission type, and providing estimates across a larger number of cities than would have been possible with a single primary survey. The chart below visualizes the VKT estimates across different cities, illustrating how leveraging existing data can yield reliable results that align closely with other studies. This example underscores that sustainable data practices aren’t just about reducing costs; they’re also about minimizing environmental impact and making efficient use of existing resources. By strategically using what is already available, we conserve time, money, and energy. Effective data use in sustainability starts with clear objectives and a careful evaluation of existing resources. Before new data collection, we should ask: Why do we need this data? What level of uncertainty is acceptable? Can available data meet our needs? Sustainable data practices help save costs, reduce environmental impact, and improve efficiency by repurposing existing datasets instead of conducting costly and redundant surveys.

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

You can’t have credible sustainability without credible data. Over the years, I’ve seen great initiatives stall because their data was incomplete, inconsistent, or scattered across systems. Governance doesn’t sound glamorous, but it’s what keeps the numbers honest and the story clear. The best place to start isn’t with another new tool — it’s by understanding the systems your teams already use and building structure around them. When governance is strong, people trust the data, leaders make better decisions, and sustainability goals actually move forward. I see data governance as the foundation for every credible impact report. It’s how we turn sustainability from a statement into a system. How is your organization strengthening data trust within your sustainability work? I’m continuing to partner with organizations and explore full-time opportunities where building data trust is the foundation for real progress. Let’s connect! #SustainabilityData #DataGovernance #ESGReporting #ImpactMeasurement #SustainabilityLeadership

A statistic that deserves more attention than it gets. Joint Capgemini Research Institute and CDP research: Scope 3 emissions represent 92% of emissions disclosed — yet only 37% are being actively addressed. That gap is 55 percentage points. And most net-zero commitments are built on top of it. The problem is not intent. It is data. Primary Scope 3 data requires cooperation from thousands of suppliers, most of whom cannot provide it. So companies estimate what they can, report what they have, and quietly move on. There is a better way — and it starts with ML-based spend analysis that can push Scope 3 coverage from ~30% to 70–80% without a single supplier questionnaire. I have written about it in this Article 2 of my series on AI-first sustainability leadership. It's the most technically specific piece I've published. If you work on Scope 3 reporting, supply chain emissions, or CSRD compliance — this one is for you. Read here 👇 #Scope3 #ESG #SBTi #Sustainability #NetZero #AIfirst Vincent Charpiot Jérôme Coignard Franco Amalfi Jordan Friedman Sol Salinas David Spitzley Ann Tracy Beth Hart Audrey Leduc Yuri Gusak Jen Upthegrove Deepa Poduval Bertrand SWIDERSKI Kelly Bowland Phil Gilchrist Bill Combs Marie-Luce GODINOT Georgia Lechlitner Claire Lund Nancy Mahon Jennifer Motles 🌻Michael Kobori Lenaic Pineau

What happens when a sustainability team finally gets visibility into their waste invoices? William Klimpert just wrapped a cost analysis exercise with one of our clients and it showed the fragmented corporate structure that creates a double-standard for Sustainability teams. It's increasingly expected that sustainability will benefit the bottom line, but these teams don't always have access to the right information to make that happen efficiently. 👀 The challenge? Like many sustainability teams, our client knew there were cost savings opportunities tied to waste — but they didn’t have consistent access to the data that could prove it. Their regular reporting came from vendor exports — not the invoices that show actual expenses. So together, we took on the lift: 📂 The team worked hard to collect invoices from across their portfolio so WATS could analyze them. 🧠 What we found: 💰 Over 50% of their expenses are tied to the trash waste stream ⚖️ There were gaps between hauler-reported data and what invoices showed — one site was 40%+ off 📌 Top 4 properties driving the majority of total costs 🔎 What’s next? ✅ Investigating potential refunds ✅ Building a business case for diversion ROI ✅ Documentation to support pushback on unclear Organics invoices ✅ Developing some deeper analytical insights based on client feedback ✨ The big takeaway? ➡ Sustainability teams are increasingly being asked to show how their work impacts the bottom line — but too often, they’re left without access to the data that proves it. At WATS, we’re proud to help bridge that gap. Moving forward, we’ll be regularly ingesting this client’s invoices — turning hidden costs into actionable insights that help right-size services, optimize contracts, and drive smarter decisions. ♻️ Because waste data isn’t just reporting fodder — it’s a business tool. Stay tuned - we’ll definitely be building a case study based on this analysis! #WasteData