Emerging Data Technology Trends

The US installed a record amount of battery storage in 2025. The 58 GWh deployed last year was: ➡️ 30% more than in 2024 ➡️ Four times the capacity installed just three years ago Most of this growth came from utility-scale projects, with around 16 GW / 50 GWh connected to the grid. Behind the meter systems added another 12 GW / 8 GWh, while residential installations surged more than 50% to reach 3.1 GWh. And increasingly, batteries are being paired with solar Nearly half of new utility-scale batteries are now co-located with solar farms, charging during the day and discharging into the evening peak. The boom is being driven by grid economics. The reason is simple. As wind and solar expand, power systems need flexibility. Batteries provide that by: ✅ Responding instantly to fluctuations in supply and demand ✅ Smoothing short-term volatility in electricity markets ✅ Reducing the need for gas-fired peaking plants Storage is becoming a structural part of high-renewables power systems - which is why battery deployment continues to accelerate. The energy transition is being driven by technology and economics. Once the economics flip, deployment accelerates – regardless of politics.

One year ago today, Dean Allemang Bryon Jacob and I released our paper "A Benchmark to Understand the Role of Knowledge Graphs on Large Language Model's Accuracy for Question Answering on Enterprise SQL Databases" and WOW! Early 2023, everyone was experimenting with LLMs to do text to sql. Examples were "cute" questions on "cute" data. Our work provided the first piece of evidence (to the best of our knowledge) that investing in Knowledge Graph provides higher accuracy for LLM-powered question-answering systems on SQL databases. The result was that by using a knowledge graph representations of SQL databases achieves 3X the accuracy for question-answering tasks compared to using LLMs directly on SQL databases. The release of our work sparked industry-wide follow-up: - The folks at dbt, led by Jason Ganz, replicated our findings, generating excitement across the semantic layer space - Semantic layer companies began citing our research, using it to advocate for the role of semantics - We continuously get folks thanking us for the work because they have been using it as supporting evidence for why their organizations should invest in knowledge graphs - RAG got extended with knowledge graphs: GraphRAG - This research has also driven internal innovation at data.world forming the foundation of our AI Context Engine where you can build AI apps to chat with data and metadata. Over the past year, I've observed two trends: 1) Semantics is moving from "nice-to-have" towards foundational: Organizations are realizing that semantics are fundamental for effective enterprise AI. Major cloud data vendors are incorporating these principles, broadening the adoption of semantics. While approaches vary (not always strictly using ontologies and knowledge graphs), the message is clear: semantics provides your unique business context that LLMs don't necessarily have. Heck, Ontology isn't a frowned upon word anymore 😀   2) Knowledge Graphs as the ‘Enterprise Brain’: Our work pushed to combine Knowledge Graphs with RAG, GraphRAG, in order to have semantically structured data that represents the enterprise brain of your organization. Incredibly honored to see Neo4j Graph RAG Manifesto citing our research as critical evidence for why knowledge graphs drive improved LLM accuracy. It's really exciting that the one year anniversary of our work is while Dean and I are at the International Semantic Web Conference. We are sharing our work on how ontologies come to the rescue to further increase the accuracy to 4x (we released that paper in May). This image is an overview of how it's achieved. It's pretty simple, and that is a good thing! I've dedicated my entire career (close to 2 decades) to figure out how to manage data and knowledge at scale and this GenAI boom has been the catalyst we needed in order to incentivize organizations to invest in foundations in order to truly speed up an innovate. There are so many people to thank! Here’s to more innovation and impact!

The Most Expensive Air on Earth: Telcos Spent $1T on Spectrum Over the last 25 years, telecom operators have transferred close to one trillion dollars to governments worldwide for spectrum access. Not for networks, not for equipment, not for innovation, but simply for the right to transmit. Approximately $400 billion was paid up front in license awards, tens of billions more in clearing costs, and hundreds of billions continue to flow annually through recurring spectrum fees. This is one of the largest and least discussed capital extractions in the history of modern infrastructure. What makes this moment important is that the price story most people repeat is wrong. The latest NERA data show that spectrum prices per MHz-pop declined throughout the 5G cycle and stabilized in 2025. On paper, the spectrum looks cheaper than ever. In reality, the total burden on operators increased. Lower unit prices were offset by larger spectrum blocks, more licenses, higher annual fees, and longer payment tails. Spectrum did not become cheap. It became structurally embedded in the cost base. At the same time, the spectrum policy is fragmented. Some countries crossed the 1 GHz threshold below 8 GHz and permanently lowered their network cost per delivered bit. Others stalled and were forced to scale capacity through densification, adding sites, power, fiber, and permits instead of air. The result is not better coverage or higher speeds. It is a widening national cost gap that compounds year after year and shows up in ROIC, energy intensity, and capital efficiency. mmWave was intended to address scarcity, but it revealed a deeper problem. High-band spectrum shifts cost into civil works and energy while delivering a limited economic return at scale. South Korea’s decision to revoke mmWave licenses made the issue explicit. The problem was not technology. It was economics. The deeper shift is this. Spectrum is no longer a one-time investment decision. It has become a permanent tax on network investment. GSMA data indicate that spectrum costs now absorb 7-10% of operators' revenues in many markets, even as revenue per MHz continues to decline. This is why network rollout slows despite increasing traffic: the constraint is not demand. It is the return thresholds. As the industry looks toward 6G, the real risk is not spectrum scarcity. It is capital scarcity. If spectrum continues to be priced as a fiscal asset rather than as an infrastructure input, the next generation will arrive with cleaner rules and weaker networks. Read more here: https://lnkd.in/ekcJ5Agw

🗞️ Just out! Latest from our NATO Strategic Communications Centre of Excellence ! “Democratising Data Integration” 🔹Examines the need for standardised data integration and communication protocols in NATO’s strategic information environment. 🔹 Core argument : while advanced data processing tools exist, the lack of standardised integration protocols limits efficiency, security, and rapid decision-making. 🔹Highlights the challenges of fragmented data systems, interoperability issues, and inconsistent data-sharing methodologies across allied organisations. Key Challenges 1. Metadata Standardisation – Inconsistencies in metadata structures lead to misinterpretations and operational inefficiencies. 2. Security Classifications – Differing classification methods create access restrictions, limiting data-sharing effectiveness. 3. Institutional Divergence – NATO allies use various data-sharing protocols, impeding interoperability. 4. Technical Expertise Gaps – The shortage of skilled personnel slows the adoption of modern integration frameworks. 5. Resource Constraints – Budgetary limitations restrict the transition to scalable and secure data systems. 6. Privacy and Compliance Issues – Conflicting regulations (e.g., GDPR) create legal and operational barriers. Proposed Solutions 🔹The report proposes adopting standardised communication protocols to ensure seamless interoperability. Frameworks like Federated Mission Networking (FMN) and VAULTIS are highlighted as potential models for structured data sharing. AI-driven solutions, automated classification systems, and improved governance mechanisms are recommended to enhance operational efficiency. Standardisation would lead to: 🔹Improved Strategic Communications – Faster, more reliable data-driven decision-making. 🔹Operational Efficiency – Reduced manual processing, better crisis response. 🔹Cost-Effectiveness – Lower integration costs through streamlined interoperability.

Is 2025 the end of the line for Data Scientist roles? People have predicted data science’s demise since 2014, but I’m hearing it from experienced data science leaders and hands-on ICs this time. One quote from an EVP of data science (hands-on technical…not a business cutout) stuck with me, “The writing’s on the wall. Our business’s data scientists refuse to accept it and adapt.” Here’s what’s changed. Models are being commoditized, and data scientists who only train models are, too. Models that power prototypes and PoCs are only 20% of the solution. Productization and commercialization require new capabilities. Third-party AI platforms support a growing number of internal use cases, and ‘buy’ now beats ‘build’ for operations. Data scientists who only do efficiency, reporting, or productivity initiatives won’t be very busy next year. 2025 is the year of product and customer-facing data science. One door is closing, but higher-value doors are opening. It’s not the end. This is what we should have been doing all along. Here are the new high-demand capabilities. Businesses need data scientists who extend beyond model training to address usability, scalability, reliability, and integration. They must work within customer, cost, and data constraints. Success is defined by real-world performance metrics like customer outcomes and satisfaction. Latency, throughput, and resource efficiency are as critical as precision and recall. Models are just one piece of the platform. Data and AI are just technologies in a stack. Data and AI products are built collaboratively with cross-functional technical teams. Validating models for reliability before shipping and continuous improvement in production are expensive. Models must be built for longevity to reduce the costs on both ends. Opportunities are bigger than ever, but I’m worried. Data scientists aren’t adapting to take advantage of them. Layoffs are a new reality, and finding a new job is no longer guaranteed. Change is inevitable, and the future is bright for data scientists who adapt. Embrace customer-facing and production-ready. AI products are in the driver’s seat for the next 5+ years. #DataScience #ArtificialIntelligence #Career

This year, India’s defense sector unveiled advancements in AI that are reshaping military strategies & boosting national security. Here’s what the data tells us: --> AI is now central to defense modernization. --> Collaboration across sectors is driving innovation. Let’s explore these in detail. 1️⃣ AI-Powered Technologies Transforming Defense India’s armed forces are deploying AI across critical areas: ➤ Autonomy in operations: AI-enabled systems like swarm drones & autonomous intercept boats enhance mission precision, reduce human risk, & improve tactical outcomes. ➤ Intelligence, Surveillance, & Reconnaissance (ISR): AI-based motion detection & target identification systems provide real-time alerts for better situational awareness along borders. ➤ Advanced robotics: Silent Sentry, a 3D-printed AI rail-mounted robot, supports automated perimeter security & intrusion detection. Example: Swarm drones use distributed AI algorithms for dynamic collision avoidance, target identification, & coordinated aerial maneuvers, providing versatility in both offensive & defensive tasks. 2️⃣ Collaboration as the Catalyst for Innovation India’s AI advancements are the result of partnerships between the government, private industries, & research institutions. ➤ Indigenous solutions: 100% indigenously developed systems like the Sapper Scout UGV for mine detection. ➤ Startups and SMEs: Innovative contributions from tech firms and startups have fueled projects like AI-enabled predictive maintenance for naval ships and drones. ➤ Global export potential: Systems like Project Drone Feed Analysis and maritime anomaly detection tools are export-ready, positioning India as a major global defense tech player. 3️⃣ The Data-Driven Case for AI ➤ Efficiency: AI-driven systems exponentially improve surveillance coverage and reduce operational time. For example, the Drone Feed Analysis system decreases mission costs while expanding surveillance areas. ➤ Safety: Predictive AI systems in vehicles and maritime platforms enhance safety by identifying potential risks before failures occur. ➤ Economic impact: AI-powered predictive maintenance for critical assets like naval ships and aircraft maximizes uptime while minimizing costs. Real Impact ➤ Swarm drones: Affordable, scalable, and capable of BVLOS operations, offering precision in combat. ➤ AI-enabled maritime systems: Detect anomalies in vessel traffic, securing trade routes and protecting economic interests. ➤ AI-driven mine detection: Enhances soldier safety while automating high-risk tasks. What does this mean for defense organizations? AI isn’t just modernizing defense; it’s placing it firmly in the global defense innovation market. With bold policies, dedicated budgets, and a growing ecosystem of public and private sector players, this will help lead the next wave of AI-driven defense technologies. But the question remains: How do we ensure these technologies are deployed ethically and responsibly? Agree?

Technology has come a long way—from being a tool of convenience to becoming the driving force behind transformation. Each year, we witness remarkable advancements that reshape industries, redefine possibilities, and address challenges we never thought possible. As we step into 2025, the pace of innovation continues to accelerate, bringing with it opportunities to create a smarter, and much more resilient world.    Here are 5 transformative #TechTrends that take the spotlight in 2025: 🚩 #CustomAI: Customized AI is becoming a game-changer, allowing organizations to create bespoke solutions for their unique needs. By using domain-specific data, businesses can solve niche problems with precision, opening doors for personalized experiences and industry-specific innovations. 🚩 The Rise of Agentic AI: Generative AI is entering a transformative phase of “agentification,” evolving from task-specific tools to specialized, interconnected AI agents. Soon, we will witness the emergence of “superagents,” orchestrating interactions between multiple AI systems to enhance collaboration, efficiency, and reliability.    🚩Future-Ready Supply Chains: Powered by AI, IoT, and blockchain, supply chains are becoming more agile, sustainable, and resilient. Technologies like low-earth orbit satellites are increasing connectivity, enabling real-time tracking and visibility, while regulatory frameworks push for greener, more transparent processes. 🚩#CleanTech: As we accelerate the shift towards renewable energy, AI will play a crucial role in optimizing systems and advancing technologies like Small Modular Reactors (SMRs) and nuclear fusion. This fusion of AI and clean tech promises better energy efficiency and a sustainable future. 🚩 #Cybersecurity: With AI-enhanced cyberattacks on the rise, cybersecurity is more critical than ever. AI-powered defenses, alongside advancements in Post-Quantum Cryptography, will ensure that businesses stay resilient and confident in their digital ecosystems, future-proofing their data security systems. These trends are a testament to how innovation can drive meaningful change, solve critical challenges, and empower industries to reimagine the future. As we stand on the brink of 2025, the question isn’t just about adopting these technologies but how we can harness them to create a smarter, more sustainable, and inclusive world. Surabhi Agarwal, The Economic Times

Arguably the most successful AI use case in healthcare to date is Revenue Cycle Management (RCM).  Given the current dominant payment model, that's a problem.  Why? Because AI optimizes billing 𝘳𝘦𝘭𝘦𝘯𝘵𝘭𝘦𝘴𝘴𝘭𝘺.  AI-powered RCM tools enable a new degree of itemization in which every patient touchpoint can be tagged and justified at levels of granularity never before possible, all in the name of extracting 𝗺𝗮𝘅𝗶𝗺𝘂𝗺 𝗮𝗹𝗹𝗼𝘄𝗮𝗯𝗹𝗲 𝗿𝗲𝗶𝗺𝗯𝘂𝗿𝘀𝗲𝗺𝗲𝗻𝘁 in a fee-for-service system.  What used to be a 4-line claim may now become 40, generated with algorithmic precision and clinical plausibility, all technically compliant and defensible.  𝘚𝘰 𝘸𝘩𝘢𝘵'𝘴 𝘵𝘩𝘦 𝘱𝘳𝘰𝘣𝘭𝘦𝘮? There is consequence in aligning AI development with fee-for-service financial incentives in a healthcare system already under strain.  ❓ Ask yourself - what is the logical endgame of "smart billing" in a dysfunctional payment model that predominantly rewards volume? Yes, AI in RCM will undoubtedly improve efficiency and reduce missed revenue opportunities. But in a FFS environment, 𝗺𝗼𝗿𝗲 𝗰𝗮𝗽𝘁𝘂𝗿𝗲𝗱 𝗿𝗲𝘃𝗲𝗻𝘂𝗲 ≠ 𝗯𝗲𝘁𝘁𝗲𝗿 𝘃𝗮𝗹𝘂𝗲. If you aren't concerned that RCM AI could serve as a force multiplier for some of the worst incentives of dominant care economics, in which revenue is extracted regardless of clinical value, you aren't asking the right questions. #healthcareai #revenuecyclemanagement #healtheconomics #healthpolicy #healthcareleadership

🔗 Blockchain in Global Supply Chains: Towards Decentralized, Programmable and Financial Infrastructures 🌍 The digital transformation of industrial supply chains — such as steel, rubber, and critical minerals — is shifting from centralized models to blockchain-based infrastructures that enable end-to-end traceability, automation, privacy, and native financial operations. Blockchain is not just a distributed database. It is a decentralized logical infrastructure capable of: ✅ Executing smart contracts to automate payments and audits ✅ Protecting sensitive data through Zero-Knowledge Proofs (ZKPs) and Fully Homomorphic Encryption (FHE) ✅ Integrating external sources (IoT, oracles) for real-time validation ✅ Tokenizing physical and financial assets, enabling instant liquidity ⚙️ Current applications across global industries: The Goodyear Tire & Rubber Company and Michelin are tracking rubber from plantations to assembly lines, certifying sustainable practices on-chain. ArcelorMittal and thyssenkrupp are tracing emissions and raw material origins in the steel industry to meet ESG standards. Platforms like Circulor, MineHub, and TradeLens are operating as blockchain-based industrial networks, fully integrated with ERP systems and IoT devices. 🚀 Emerging trends driving this transformation: 🔹 DePIN (Decentralized Physical Infrastructure Networks): Networks such as Helium and DIMO allow the direct recording of physical data (logistics, geolocation, air quality, load sensors, etc.) on blockchain — without relying on centralized operators. This enhances real-time visibility across the supply chain, even in remote regions. 🔹 Tokenization of trade finance instruments (e.g., letters of credit, invoices): With enterprise-grade DeFi solutions (like Centrifuge or TradeFinex), it is now possible to issue and trade tokenized credit instruments on blockchain, using real-world assets (invoices, orders, contracts) as collateral. This brings instant liquidity to industrial SMEs and reduces reliance on traditional banking systems. 📊 The result: A self-governing, resilient, and financial supply chain, where physical, digital, and monetary flows are integrated into a single, verifiable network — fully aligned with global regulatory requirements (CSRD, CBAM, ISO 14067...). 📣 Companies that understand blockchain as infrastructure — not just technology — are leading the new era of intelligent and sustainable logistics. #Blockchain #SupplyChain #DePIN #Tokenization #SmartContracts #IndustrialIoT #Fintech #ESG #Web3 #FHE #ZKP #Traceability #Steel #Rubber #Liquidity #DigitalTrade #Sustainability Joaquim Alfredo José Daniel Nelley Alejandro Sivakumar Tomás David Juan Paris Hidenori Dra. Carlos

💡There’s an interesting trend I observed with organizations recently: they are choosing to save money and simplify their operations by using slower but cheaper storage systems. This is especially true when they handle large amounts of data and sub-second latency isn't critical. Let’s find out what’s motivating this. Data loses its value over time. Once data becomes older and rarely accessed, real-time performance becomes less crucial. While developers need to access historical data for analysis, ad hoc queries, and compliance requirements, they can accept some latency. Their priority now shifts to storing this older data most cost-effectively and efficiently. Compute-storage decoupling is something that we inherited from the Hadoop era, allowing storage systems to use tiered storage for improved cost-efficiency and scalability. ✳️ Object stores became the de facto tiered storage Amazon S3 was officially launched in 2006. Almost 20 years later and with trillions of objects stored, we now have reliable infinite storage. People started to call this cheap, infinitely scalable storage a Data Lake(or Lakehouse nowadays). For developers, it offers a simple path to disaster recovery. When you upload a file to S3, you immediately get eleven nines of durability—that's 99.999999999%. To put this in perspective: if you store 10,000 objects, you might lose just one in 10 million years. As object stores like S3 become more affordable, databases and OLAP systems have increasingly utilized deep object storage to enhance cost efficiency and durability. For example, PGAA, the EDB’s analytics extension for Postgres, allows you to query hot data and cold data with a single dedicated node, ensuring optimal performance by automatically offloading cold data to columnar tables in object storage, reducing the complexity of managing analytics over multiple data tiers. ✳️ Not only databases, but streaming data platforms are evolving too Redpanda and WarpStream show how modern streaming platforms can save money while maintaining good performance. They do this by using a mix of fast local storage (SSDs) for quick access and cloud storage for most of their data, avoiding costly cross-AZ data transfers. ✳️ Why not make the object stores Iceberg compatible? That will transform simple storage solutions into powerful data management systems like data lakehouses. This compatibility brings essential features like schema evolution, time travel capabilities, ACID transactions, and performance optimizations—all while maintaining the cost benefits of object storage. This gives organizations the flexibility to choose their own query engine and catalog, making data platforms more modular and composable.