Innovation and Data Analytics

🌦️ Weather models are massive geospatial data engines but most people don’t realize it. When you open a weather app and see the forecast, you’re looking at one of the most complex geospatial data products in the world. They’re built from decades of observations mapped across the entire planet: temperature, humidity, ocean currents, wind fields all on dense global grids. I came across this model in a post from Jason Stock, AERIS from Argonne, which runs on supercomputers and treat the earth as a giant spatial dataset: millions of cells, each storing evolving variables over time They pull in historical reanalysis data such as ERA5 and use advanced machine learning to spot patterns and push forecasts from hours to 90 days out with surprising accuracy Weather and climate modeling has historically lived in its own silo. Meanwhile, modern geospatial analytics has evolved in parallel. We have satellite imagery pipelines and geospatial cloud platforms, but they rarely talk to the teams advancing weather AI. Imagine the impact if we connect these two worlds better. Smarter risk maps for floods and fires built from cutting-edge forecast ensembles Climate-aware routing for shipping and aviation Energy and agriculture planning tied directly to long range weather signals Weather data is spatial data. But our tools and communities don’t always collaborate. It’s time to bridge that gap and bring the best of modern geospatial processing and data infrastructure to weather and climate forecasting, and vice versa. 🌎 I'm Matt and I talk about modern GIS, earth observation, AI, and how geospatial is changing. 📬 Want more like this? Join 9k+ others learning from my newsletter → forrest.nyc

"this position paper challenges the outdated narrative that ethics slows innovation. Instead, it proves that ethical AI is smarter AI—more profitable, scalable, and future-ready. AI ethics is a strategic advantage—one that can boost ROI, build public trust, and future-proof innovation. Key takeaways include: 1. Ethical AI = High ROI: Organizations that adopt AI ethics audits report double the return compared to those that don’t. 2. The Ethics Return Engine (ERE): A proposed framework to measure the financial, human, and strategic value of ethics. 3. Real-world proof: Mastercard’s scalable AI governance and Boeing’s ethical failures show why governance matters. 4. The cost of inaction is rising: With global regulation (EU AI Act, etc.) tightening, ethical inaction is now a risk. 5. Ethics unlocks innovation: The myth that governance limits creativity is busted. Ethical frameworks enable scale. Whether you're a policymaker, C-suite executive, data scientist, or investor—this paper is your blueprint to aligning purpose and profit in the age of intelligent machines. Read the full paper: https://lnkd.in/eKesXBc6 Co-authored by Marisa Zalabak, Balaji Dhamodharan, Bill Lesieur, Olga Magnusson, Shannon Kennedy, Sundar Krishnan and The Digital Economist.

How AI is changing storm response in the U.S. — technically. Have you experienced it? Extreme weather response is no longer driven by single forecasts. It’s driven by ensembles + AI acceleration + real-time data fusion. Here’s what’s happening under the hood: AI-accelerated Numerical Weather Prediction (NWP) Deep learning models (graph neural nets, transformers) are trained on decades of reanalysis data to approximate full physics-based solvers. Result: • Inference in seconds instead of hours • Enables rapid ensemble generation (hundreds of scenarios, not dozens) This allows forecasters to update storm tracks and intensity continuously, not on fixed cycles. Multi-modal data fusion AI ingests: • Satellite imagery (GOES) • Doppler radar volumes • Ocean buoys & atmospheric soundings • Ground IoT sensors • Historical climatology Models correlate spatial-temporal patterns across modalities — something classical models struggle with at scale. Severe weather nowcasting Computer vision models detect: • Convective initiation • Tornadic signatures • Rapid intensification signals Lead times improve by 30–60 minutes for fast-forming events — which is operationally massive for emergency management. Probabilistic forecasting, not single answers ML-driven ensembles output probability distributions, not deterministic paths: • Flood depth likelihoods • Wind gust exceedance • Ice accumulation risk This feeds directly into risk-based decision systems. Infrastructure impact modeling Utilities combine AI weather outputs with: • Grid topology • Asset age & failure history • Load forecasts This enables pre-storm optimization: • Crew pre-positioning • Targeted grid isolation • Faster restoration paths Operational decision intelligence AI systems now bridge forecast → action: • When to evacuate • Where to stage responders • Which assets fail first This is no longer meteorology alone — it’s real-time systems engineering. Storms are getting more chaotic. Our response is getting more computational. AI doesn’t replace physics. It compresses it into time we can actually use. #AI #WeatherModeling #Nowcasting #ClimateTech #InfrastructureAI #DigitalTwins #ResilienceEngineering #HPC

This new white paper by Stanford Institute for Human-Centered Artificial Intelligence (HAI) titled "Rethinking Privacy in the AI Era" addresses the intersection of data privacy and AI development, highlighting the challenges and proposing solutions for mitigating privacy risks. It outlines the current data protection landscape, including the Fair Information Practice Principles, GDPR, and U.S. state privacy laws, and discusses the distinction and regulatory implications between predictive and generative AI. The paper argues that AI's reliance on extensive data collection presents unique privacy risks at both individual and societal levels, noting that existing laws are inadequate for the emerging challenges posed by AI systems, because they don't fully tackle the shortcomings of the Fair Information Practice Principles (FIPs) framework or concentrate adequately on the comprehensive data governance measures necessary for regulating data used in AI development. According to the paper, FIPs are outdated and not well-suited for modern data and AI complexities, because: - They do not address the power imbalance between data collectors and individuals. - FIPs fail to enforce data minimization and purpose limitation effectively. - The framework places too much responsibility on individuals for privacy management. - Allows for data collection by default, putting the onus on individuals to opt out. - Focuses on procedural rather than substantive protections. - Struggles with the concepts of consent and legitimate interest, complicating privacy management. It emphasizes the need for new regulatory approaches that go beyond current privacy legislation to effectively manage the risks associated with AI-driven data acquisition and processing. The paper suggests three key strategies to mitigate the privacy harms of AI: 1.) Denormalize Data Collection by Default: Shift from opt-out to opt-in data collection models to facilitate true data minimization. This approach emphasizes "privacy by default" and the need for technical standards and infrastructure that enable meaningful consent mechanisms. 2.) Focus on the AI Data Supply Chain: Enhance privacy and data protection by ensuring dataset transparency and accountability throughout the entire lifecycle of data. This includes a call for regulatory frameworks that address data privacy comprehensively across the data supply chain. 3.) Flip the Script on Personal Data Management: Encourage the development of new governance mechanisms and technical infrastructures, such as data intermediaries and data permissioning systems, to automate and support the exercise of individual data rights and preferences. This strategy aims to empower individuals by facilitating easier management and control of their personal data in the context of AI. by Dr. Jennifer King Caroline Meinhardt Link: https://lnkd.in/dniktn3V

𝟔𝟔% 𝐨𝐟 𝐀𝐈 𝐮𝐬𝐞𝐫𝐬 𝐬𝐚𝐲 𝐝𝐚𝐭𝐚 𝐩𝐫𝐢𝐯𝐚𝐜𝐲 𝐢𝐬 𝐭𝐡𝐞𝐢𝐫 𝐭𝐨𝐩 𝐜𝐨𝐧𝐜𝐞𝐫𝐧. What does that tell us? Trust isn’t just a feature - it’s the foundation of AI’s future. When breaches happen, the cost isn’t measured in fines or headlines alone - it’s measured in lost trust. I recently spoke with a healthcare executive who shared a haunting story: after a data breach, patients stopped using their app - not because they didn’t need the service, but because they no longer felt safe. 𝐓𝐡𝐢𝐬 𝐢𝐬𝐧’𝐭 𝐣𝐮𝐬𝐭 𝐚𝐛𝐨𝐮𝐭 𝐝𝐚𝐭𝐚. 𝐈𝐭’𝐬 𝐚𝐛𝐨𝐮𝐭 𝐩𝐞𝐨𝐩𝐥𝐞’𝐬 𝐥𝐢𝐯𝐞𝐬 - 𝐭𝐫𝐮𝐬𝐭 𝐛𝐫𝐨𝐤𝐞𝐧, 𝐜𝐨𝐧𝐟𝐢𝐝𝐞𝐧𝐜𝐞 𝐬𝐡𝐚𝐭𝐭𝐞𝐫𝐞𝐝. Consider the October 2023 incident at 23andMe: unauthorized access exposed the genetic and personal information of 6.9 million users. Imagine seeing your most private data compromised. At Deloitte, we’ve helped organizations turn privacy challenges into opportunities by embedding trust into their AI strategies. For example, we recently partnered with a global financial institution to design a privacy-by-design framework that not only met regulatory requirements but also restored customer confidence. The result? A 15% increase in customer engagement within six months. 𝐇𝐨𝐰 𝐜𝐚𝐧 𝐥𝐞𝐚𝐝𝐞𝐫𝐬 𝐫𝐞𝐛𝐮𝐢𝐥𝐝 𝐭𝐫𝐮𝐬𝐭 𝐰𝐡𝐞𝐧 𝐢𝐭’𝐬 𝐥𝐨𝐬𝐭? ✔️ 𝐓𝐮𝐫𝐧 𝐏𝐫𝐢𝐯𝐚𝐜𝐲 𝐢𝐧𝐭𝐨 𝐄𝐦𝐩𝐨𝐰𝐞𝐫𝐦𝐞𝐧𝐭: Privacy isn’t just about compliance. It’s about empowering customers to own their data. When people feel in control, they trust more. ✔️ 𝐏𝐫𝐨𝐚𝐜𝐭𝐢𝐯𝐞𝐥𝐲 𝐏𝐫𝐨𝐭𝐞𝐜𝐭 𝐏𝐫𝐢𝐯𝐚𝐜𝐲: AI can do more than process data, it can safeguard it. Predictive privacy models can spot risks before they become problems, demonstrating your commitment to trust and innovation. ✔️ 𝐋𝐞𝐚𝐝 𝐰𝐢𝐭𝐡 𝐄𝐭𝐡𝐢𝐜𝐬, 𝐍𝐨𝐭 𝐉𝐮𝐬𝐭 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞: Collaborate with peers, regulators, and even competitors to set new privacy standards. Customers notice when you lead the charge for their protection. ✔️ 𝐃𝐞𝐬𝐢𝐠𝐧 𝐟𝐨𝐫 𝐀𝐧𝐨𝐧𝐲𝐦𝐢𝐭𝐲: Techniques like differential privacy ensure sensitive data remains safe while enabling innovation. Your customers shouldn’t have to trade their privacy for progress. Trust is fragile, but it’s also resilient when leaders take responsibility. AI without trust isn’t just limited - it’s destined to fail. 𝐇𝐨𝐰 𝐰𝐨𝐮𝐥𝐝 𝐲𝐨𝐮 𝐫𝐞𝐠𝐚𝐢𝐧 𝐭𝐫𝐮𝐬𝐭 𝐢𝐧 𝐭𝐡𝐢𝐬 𝐬𝐢𝐭𝐮𝐚𝐭𝐢𝐨𝐧? 𝐋𝐞𝐭’𝐬 𝐬𝐡𝐚𝐫𝐞 𝐚𝐧𝐝 𝐢𝐧𝐬𝐩𝐢𝐫𝐞 𝐞𝐚𝐜𝐡 𝐨𝐭𝐡𝐞𝐫 👇 #AI #DataPrivacy #Leadership #CustomerTrust #Ethics

Flood science has historically been trapped between two extremes: hydrodynamic models that are highly accurate but computationally expensive, or global models that are too coarse (>1 km) to capture critical local vulnerabilities. Bridging this divide requires a fundamental shift from physics-based deduction to data-driven induction, a challenge that has defined my research over the last four years. This week, I am very happy to share that I have formalized this solution by submitting my Ph.D. thesis at Hong Kong Baptist University: "Towards GeoAI-based Data-driven Flood Management Solutions: A Synergistic Machine Learning and Earth Observation Framework" As illustrated, the thesis establishes a scalable GeoAI framework built on three synergistic pillars: 1. High-Dimensional Earth Observation (The Data) Leveraging multi-temporal global data streams (Landsat, Sentinel) to transition the field from data scarcity to data abundance. 2. Planetary-Scale Geo-Computation (The Platform) Utilizing cloud clusters (Google Earth Engine) and HPC (Shaheen-III) to democratize processing power, enabling the analysis of petabyte-scale geospatial data without traditional hardware constraints. 3. Machine Learning Analytics (The Engine) We systematically benchmarked 14 ML architectures to resolve the "accuracy-efficiency" trade-off, establishing a robust modeling engine. This framework was first operationalized across Pakistan's diverse landscapes to reveal that 95 million people reside in high-risk zones, before being scaled globally to produce the first harmonized 30 m flood susceptibility baseline. The Output: Global Flood Susceptibility Map (GFSM v1) By applying a climate modeling scheme (across 192 climate zones), we produced the first globally harmonized, 30 m resolution flood susceptibility baseline derived entirely from open-access data. This research addresses the "data equity deficit" in the Global South, where 89% of flood-exposed populations reside, often without high-resolution risk data. Next Steps: I will be releasing the open-source code, the GFSM v1 dataset, and the GEE web applications in the coming weeks. If you are interested in the work, feel free to drop a message to dicsuss further possibilities! For more info, feel free to check my updated portfolio: www.waleedgeo.com #geoai #earthengine #floodrisk #remotesensing #hkbu #datascience #gfsm #flood

Most insurance companies don’t have a product problem. They have a 𝐬𝐢𝐠𝐧𝐚𝐥 𝐩𝐫𝐨𝐛𝐥𝐞𝐦. Trouble shows up early for customers… and late for leadership. McKinsey’s 2025 analysis shows that only a small fraction of insurers capture meaningful value from AI and the reason isn’t model quality. It’s because 𝐝𝐚𝐭𝐚 𝐬𝐢𝐭𝐬 𝐢𝐧 𝐬𝐢𝐥𝐨𝐬 across underwriting, claims, support, and policy servicing. Another study highlights that predictive analytics when actually integrated can reduce loss ratios, speed up claims, and improve risk accuracy. But most insurers never reach that stage because their systems can’t surface early patterns. So what happens? A spike in confusion calls. Customers misusing features. Renewal expectations not matching policy reality. Claim friction rising quietly for weeks. By the time these signals hit dashboards, the damage is already in motion: lower NPS, rising churn, operational load, regulatory exposure. This is why insurance needs an 𝐈𝐂𝐔 - 𝐈𝐧𝐬𝐢𝐠𝐡𝐭 𝐂𝐨𝐫𝐫𝐞𝐜𝐭𝐢𝐨𝐧 𝐔𝐧𝐢𝐭. A team that: 1. Connects disparate data into a single, queryable layer. 2. Builds early-warning models for churn, fraud, sentiment, and claims delay. 3. Flags mismatches between expectation and experience in real time. 4. Routes insights directly into underwriting, ops, and customer teams. When insights arrive early, transformation doesn’t arrive late. And in insurance, 𝐭𝐡𝐞 𝐞𝐚𝐫𝐥𝐢𝐞𝐬𝐭 𝐬𝐢𝐠𝐧𝐚𝐥 𝐢𝐬 𝐭𝐡𝐞 𝐮𝐥𝐭𝐢𝐦𝐚𝐭𝐞 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐭𝐨 𝐰𝐢𝐧. #InsuranceIndustry #DataAnalytics #CustomerExperience #PredictiveAnalytics

Driven by AI, we are entering a new era of enterprise software, ushering in systems of intelligence. In the mid 1980s, driven by the growth of the client/server architecture, we saw the dramatic rise of systems of record. These are the back office software applications that helped enterprises run their ERP, HR, CRM, and core IT workflows. These technologies were relatively specialized, and helped automate any of the most critical tasks in the enterprise. They were defined by structured data, back office automation, and leveraged by only by select users in an enterprise. With the rise of cloud and mobile in the mid 2000s, we saw a new era of systems of engagement, as coined by Geoffrey Moore. In a world of much more dynamic and ad-hoc work in the enterprise, systems of engagement were tools for collaboration, communication, video, work and project management, social and intranets, and more. These tools dealt with all the messy, unstructured data in an enterprise - the conversations, collaborative docs, and media that began to drive a shift in how the entire enterprise worked. Now, in the mid 2020s, we are firmly entering a new era of enterprise software, which gives rise to systems of intelligence. Systems of intelligence combine enterprise data, workflows, and AI, to deliver insights and automation to an organization. Importantly, because of the ability for AI to process unlimited unstructured data - like documents, video, or communications - we also get the same benefit from this messy data as we did our structured data. We can query, synthesize, calculate, and automate all the work around thus unstructured data just as easily as we could query a database before. Unlike systems of engagement that generally broke down the more information that goes into them, we see the reverse now with AI, where software can become more powerful and useful the more data it has access to. And with AI Agents being a native property of systems of intelligence, these systems aren’t only leveraged by every employee, they dramatically expand the output of the workforce. Systems of record are where people work by largely themselves. Systems of engagement let users work collaboratively with other people. Now systems of intelligence let us work seamlessly with people and AI. These systems will also talk to each other in completely new ways. Instead of deterministic APIs and clear handshakes, with Agentic AI, these systems will communicate with each other much like a humans do. A user will make a request in one system, and it will fan out the ask to a variety of other similar systems relevant for the desired information. And if it didn’t get what it wanted, it will simply request again in a different way, just as a person would. We’re going to see systems of intelligence in every domain of work - across every line of business and every vertical. Wild times ahead.

Many amazing presenters fall into the trap of believing their data will speak for itself. But it never does… Our brains aren't spreadsheets, they're story processors. You may understand the importance of your data, but don't assume others do too. The truth is, data alone doesn't persuade…but the impact it has on your audience's lives does. Your job is to tell that story in your presentation. Here are a few steps to help transform your data into a story: 1. Formulate your Data Point of View. Your "DataPOV" is the big idea that all your data supports. It's not a finding; it's a clear recommendation based on what the data is telling you. Instead of "Our turnover rate increased 15% this quarter," your DataPOV might be "We need to invest $200K in management training because exit interviews show poor leadership is causing $1.2M in turnover costs." This becomes the north star for every slide, chart, and talking point. 2. Turn your DataPOV into a narrative arc. Build a complete story structure that moves from "what is" to "what could be." Open with current reality (supported by your data), build tension by showing what's at stake if nothing changes, then resolve with your recommended action. Every data point should advance this narrative, not just exist as isolated information. 3. Know your audience's decision-making role. Tailor your story based on whether your audience is a decision-maker, influencer, or implementer. Executives want clear implications and next steps. Match your storytelling pattern to their role and what you need from them. 4. Humanize your data. Behind every data point is a person with hopes, challenges, and aspirations. Instead of saying "60% of users requested this feature," share how specific individuals are struggling without it. The difference between being heard and being remembered comes down to this simple shift from stats to stories. Next time you're preparing to present data, ask yourself: "Is this just a data dump, or am I guiding my audience toward a new way of thinking?" #DataStorytelling #LeadershipCommunication #CommunicationSkills

In today’s data-driven world, AI-powered analytics is no longer a futuristic concept—it’s a necessity. Businesses that embrace AI in data analytics are making faster, smarter, and more accurate decisions, giving them a competitive edge like never before. Real-Time Insights for Agile Decision-Making Traditional analytics often relies on historical data, but AI enables real-time data processing. Whether it’s tracking customer behavior, detecting fraud, or optimizing supply chains, businesses can act instantly rather than reacting too late. Automation: Reducing Human Effort, Increasing Accuracy AI takes over repetitive and time-consuming data analysis tasks, allowing teams to focus on strategic decisions. From automated reporting to anomaly detection, AI ensures precision while freeing up valuable human resources. Predictive Decision-Making: Seeing the Future with Data With AI-driven predictive analytics, businesses can forecast market trends, anticipate customer needs, and even prevent operational bottlenecks. Companies leveraging AI can proactively adapt rather than just respond to changes. From Data Overload to Actionable Insights Businesses generate vast amounts of data, but raw data is useless without interpretation. AI helps uncover patterns, correlations, and opportunities hidden in complex datasets—turning data into actionable strategies. 𝑰𝒏𝒅𝒖𝒔𝒕𝒓𝒚-𝑾𝒊𝒅𝒆 𝑰𝒎𝒑𝒂𝒄𝒕: 𝑾𝒉𝒐’𝒔 𝑳𝒆𝒂𝒅𝒊𝒏𝒈 𝒕𝒉𝒆 𝑨𝑰 𝑹𝒆𝒗𝒐𝒍𝒖𝒕𝒊𝒐𝒏? 📈 Retail: Personalized recommendations and inventory optimization 🏦 Finance: Fraud detection and risk assessment ⚕️ Healthcare: Predictive diagnostics and patient care optimization 🚗 Automotive: Autonomous driving and smart maintenance 📡 Telecom: Network optimization and customer service automation As AI continues to evolve, businesses that embrace AI-powered analytics will stay ahead, while those that resist may struggle to keep up. 𝑾𝒉𝒂𝒕’𝒔 𝒚𝒐𝒖𝒓 𝒕𝒂𝒌𝒆? 𝑰𝒔 𝒚𝒐𝒖𝒓 𝒐𝒓𝒈𝒂𝒏𝒊𝒛𝒂𝒕𝒊𝒐𝒏 𝒍𝒆𝒗𝒆𝒓𝒂𝒈𝒊𝒏𝒈 𝑨𝑰 𝒊𝒏 𝒅𝒂𝒕𝒂 𝒂𝒏𝒂𝒍𝒚𝒕𝒊𝒄𝒔? 𝑺𝒉𝒂𝒓𝒆 𝒚𝒐𝒖𝒓 𝒕𝒉𝒐𝒖𝒈𝒉𝒕𝒔 𝒊𝒏 𝒕𝒉𝒆 𝒄𝒐𝒎𝒎𝒆𝒏𝒕𝒔! #aianalytics #DataDrivenDecisionMaking #aipoweredAnalytics #DataAnalytics