💥 AI is transforming content—and your storage strategy must keep up. AI-driven content isn’t just changing workflows. It’s driving unprecedented growth in file volumes, complexity, and retention needs. The question isn’t if your storage requirements will grow—it’s are you ready? 🚀 Enterprise leaders are adapting fast, turning this challenge into a competitive advantage. 📊 Learn more about how AI is reshaping content and storage—and check out the tools you can use today: https://ebay.us/P1Bb0y #AI #ArtificialIntelligence #ContentStrategy #DataManagement #EnterpriseTech #CloudStorage #DigitalTransformation #BigData #TechTrends #Seagate #FutureOfWork #Innovation #DataStrategy #GenerativeAI #TechLeadership #eBay

I can hardly count the number of times I have seen data scientists target specific precision and recall thresholds without understanding the underlying problem that their models are meant to solve. We, as practitioners, need to show how a model will improve our target metrics, and understand how models outputs will be used by the business. This lets us forecast our expected impact, set meaningful thresholds, and communicate the benefits of deploying a model. Failing this, we, at best, provide sub-optimal results, and, at worst, waste time and resources on projects that will not provide a benefit to the business.

Most organizations treat old data like clutter—but it’s actually their most valuable asset. With CAEVES Deep Storage, archived data becomes searchable, AI-ready intelligence that drives compliance, forecasting, and innovation. Read the latest Storage Mythbusters article: “Old Data Has No Value? Think Again,” below. https://lnkd.in/eriCwSG8

Too many companies see old data as dead weight. The truth? It’s your institutional memory—and your competitive edge. Instead of viewing it as a liability, businesses should consider how to leverage it. In my latest Storage Mythbusters piece, I dig into how CAEVES helps turn archives into active intelligence. Take a look and rethink what’s sitting in your storage.

😓 Most enterprises today deal with fragmented data systems: warehouses for structured analytics, lakes for big data, specialist ML platforms, separate governance tools. That spells complexity, slow time-to-insight and increased risk. The Lakehouse paradigm changes that. By unifying data types, analytics workloads, and governance under one roof, it reduces silos and accelerates innovation. Discover how we guide organizations through this transformation. https://lnkd.in/gHG-FxFT Sign up for a call with us today: https://lnkd.in/gDf2YTQN #TransformXperience #DigitalTransformation

Legacy healthcare data platforms weren’t built for today’s needs. They slow workflows, retain unnecessary data, and increase security risks. In Part 2 of his blog series, Jeffrey Eyestone, Chief Strategy & AI Officer at PnT Data Corp., explains how Post-n-Track Gen 3 uses an agent-based architecture to deliver faster, more secure, and more flexible data sharing — without long-term data storage. Read Part 2: https://lnkd.in/eYdjHhhh

Enterprises today collect more data than ever before yet most struggle to turn it into decisions that move the business forward. That’s why we built Inferyx – The Data Intelligence Platform. It’s where your catalog, engineering, and analytics finally come together in one intelligent layer. The result? ✨ Faster insights with less effort ✨ Governance built into every step ✨ AI-driven intelligence that scales across the enterprise Your data already has the answers. Inferyx helps you unlock them. 🎥 Watch this short video to see how we make it happen: Yogesh Palrecha Vinay Mahajan Balaji Krishnamoorthy Arslan Khan Robert Lee Timothy Koropsak, CFA BG Pal Abhimanyu Diwaker Alok Tiwari

Tools change. Mission delivery is the real focus. Information Technology (IT) is the language that moves data into action, it is the stage where data shouts, sings, or is silent. Data strategy matters because it ensures direction before tools enter the conversation. Consider your data strategy the blueprint for decision-making...under pressure and in progress. Remain tool agnostic. Strategy should never be led by platform preference. It should be built on mission, architecture, and governance. When those are clear, many tools can serve. When they aren’t, no tool will. Before selecting technology, establish direction by answering: ✅ What problem is being solved? ✅ Who does this affect? ✅ What must remain true as we build? ✅ How will this decision hold over time? As fast as technology evolves, remember: tools support the work. Tools are replaceable. Delivery of the mission is the focus. How will you leverage data and technology to serve humanity? #PromisingPractice #DataDiscipline #StrategicDesign #MissionAlignment #ResponsibleTechnology #TheDataLady

Perfection is stalling your impact more than bad decisions ever will I used to think “not good enough” meant “not ready.” So I postponed launches, delayed improvement updates, and shelved ideas. Now I think every pro started as a beginner. The beginning part is key though as small improvement's compound to big impacts over time In circular economy work, this is also true: teams often spend ~80% of their time chasing the last ~2% of data that won’t move the needle. Here’s my new playbook: 1️⃣ Start with reference/proxy data to map material flows and screen suppliers. 2️⃣ Set thresholds: act at 70–80% confidence; tag gaps for follow-up. 3️⃣ Document assumptions in plain language. 4️⃣ Iterate as better data arrives; upgrade decisions, don’t stall them. What changes: you flag high-risk materials earlier, cut analysis time by ~35%, and organize pilots in 3 weeks instead of 3 months. If you stopped chasing perfect data tomorrow, what’s the first decision you’d make with “good enough” inputs? #CircularEconomy #Sustainability #SupplyChain #DataStrategy #ProgressOverPerfection

We have published new research: "Business models of incumbent-owned platforms: A taxonomy and archetypal patterns" by Dardan Behrami, Prof. Dr. Gerrit Remané & Markus Böhm. https://lnkd.in/drGKZzMa We have published a new study on incumbent-owned platforms. Although start-ups in the platform economy have received significant attention, the unique journey of established firms transitioning to platform business models is an important yet often overlooked area. This research addresses the fragmented nature of existing studies by analyzing 88 incumbent-owned platforms across various industries. The authors develop a robust taxonomy of incumbent-owned platform business models featuring 10 dimensions and 26 characteristics. From this taxonomy, the study empirically derives six archetypal business models: C2C platforms, Community Platforms, Product Ecosystems, Hybrid Service Platforms, Data Ecosystems, and Independent Platforms. Beyond classification, the research formulates propositions on the transformation pathways that incumbents follow, the strategies they use to overcome the chicken-and-egg problem, and the business model factors that influence platform success, survival, and failure. The paper offers an empirically grounded framework that contributes to platform business model theory. It also provides managers with a structured tool for navigating strategic options and anticipating the challenges of platform transformation.