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The evolution of VAST Data's unified platform marks a significant shift towards a comprehensive AI operating system. Modelled to streamline data management, this innovative system combines storage, database, and compute engines into a singular cohesive platform, inherently designed to maintain the pace with the ever-expansive demands of AI applications. At its core, the VAST DataStore provides scalable storage solutions, ensuring that data remains readily accessible. The VAST DataBase facilitates structured data management, while VAST DataEngine empowers global function execution. This triad forms a symbiotic relationship, collectively simplifying data capture, synthesis, and learning processes. For businesses leveraging AI, these advancements are not just about keeping up but leaping ahead, enabling faster and more insightful AI-driven initiatives. Harnessing the power of this integrated platform, organizations can unlock unprecedented value from their data, pushing the boundaries of what's possible in AI deployments. #AI #DataManagement #DataStorage #ArtificialIntelligence #BusinessAutomation #VASTData Visit https://workflo.agency for your edge in automation. Secure a free consultation and see how automation can redefine your operations. Don't miss the opportunity to advance your business with the latest in automation technology.

The enterprise data landscape is a raging river of information, and multi-cloud environments are the new normal. But managing this vast, ever-growing sea of data can be a herculean task. Here's where AI steps in as a game-changer. Imagine a future where AI takes the wheel, autonomously handling the heavy lifting of data management in multi-cloud environments: Self-Provisioning: No more manual configuration headaches. AI dynamically provisions resources based on real-time data demands, ensuring optimal performance and scalability. Self-Optimization: AI continuously analyzes data and infrastructure, automatically fine-tuning configurations to maximize efficiency and minimize costs. Self-Healing: AI acts as a vigilant guardian, proactively identifying and resolving data management issues before they disrupt operations. Imagine self-healing systems automatically rerouting data flows or repairing corrupted records. AI-powered self-managing data services will free up IT teams to focus on strategic initiatives, while ensuring critical data is always accessible, secure, and optimized. As enterprises navigate the complexities of multi-cloud environments, AI emerges as a powerful ally, helping them tame the data deluge and unlock its full potential. #Informula #AI #DataServices #MultiCloud #Provisioning #Optimization #Healing

As businesses scale up generative AI projects, effective data management is critical for success. According to cio.com, there are three key areas to focus on: 1️⃣ Data Collection and Quality: It's essential to collect, filter, and categorize both structured and unstructured data, ensuring high-quality inputs to minimize issues like AI hallucinations.    2️⃣ Governance and Compliance: Organizations must rethink data governance for AI, ensuring compliance with evolving regulations, like the EU AI Act, while fostering innovation. 3️⃣ Data Privacy and IP Protection: Safeguard data privacy and intellectual property, especially when using public models, to protect sensitive information and maintain control. From my experience, what often gets overlooked is how data strategy aligns with long-term business objectives. For example, in scaling AI, the real challenge is not just data management but ensuring your AI infrastructure is flexible enough to evolve with future use cases. There’s also a significant opportunity in leveraging generative AI to drive operational efficiency beyond the obvious. AI-driven automation can transform not only customer-facing processes but also back-end workflows, like IT support or inventory management. Additionally, Tech leaders must think ahead about data interoperability, especially in a world of increasing AI regulation. Future-proofing the AI strategy by embedding scalable compliance mechanisms will be critical as regulations continue to evolve. Forward-thinking leaders will also need to balance innovation with risk management, particularly when considering third-party AI tools and protecting proprietary data. In brief, data management isn’t just a technical requirement: it’s a strategic advantage. As generative AI scales, the complexity of managing data quality, privacy, and compliance will only grow. Automating these processes, while maintaining strict oversight, ensures that AI models deliver value without exposing the business to unnecessary risks. The organizations that prioritize this balance between innovation and governance will be the ones that stay ahead, turning data into a true differentiator in the AI-driven future. 💡 Source: https://lnkd.in/d5jcKBbC #AI #DataManagement #GenerativeAI #CIO #DataGovernance #AIInnovation #CTOInsights #DataStrategy

Data Quality, Integration, and the Foundation for AI: What It All Means | HackerNoon: To achieve fundamental data quality, data integration is crucial for growing organizations. Integration processes provide huge organizational boosts, ...

💸 Every unoptimized prompt in your enterprise AI stack isn't just inefficient - it's burning through your budget in ways you might not see. We recently analyzed enterprise AI implementations and found a pattern: Companies focus on model selection and infrastructure, while overlooking prompt optimization. This oversight compounds with every interaction. Think about it: • Each unnecessary token adds to your API costs • Every ambiguous prompt requires human review • Suboptimal outputs need multiple iterations • Inconsistent results create downstream inefficiencies This isn't just about better prompting - it's about implementing systematic prompt engineering that transforms cost centers into value drivers. The solution? Start treating your prompts as mission-critical infrastructure. Document, version, test, and optimize them with the same rigor you apply to your codebase. What's your approach to measuring and optimizing prompt efficiency in your organization? Share your insights below 👇 #SmartPrat #PromptEngineering #EnterpriseAI #AIImplementation #TechOptimization

AI Fabric !! Are you struggling with data complexities? Dealing with hallucinations from generative AI? Challenged by switching between LLMs? AI Fabric might be the solution your team needs. In today’s AI landscape, managing the data that powers AI models is often more complex than building the models themselves. AI Fabric tackles this by creating a unified data layer. How AI Fabric Works: • Unified Data Labeling: Labels data with AI-readable terms like “sales,” “customer engagement,” or “machine status,” allowing models to interpret data more effectively. For instance, sales data might be labeled as “monthly sales” or “transaction volume,” providing contextual information that models can recognize. • Hallucination prevention: Provides structured and validated data to minimize misleading outputs, ensuring AI models deliver more reliable insights. • Flexible LLM Integration: AI Fabric’s modular setup enables seamless switching between LLMs. • Data Governance & Security: Enforces standards, ensuring data integrity and securing sensitive information. • Scalability Across Systems: Scales effortlessly with data volume and new sources. • Workflow Automation & Monitoring: Reduces manual intervention with automated workflows and continuous monitoring. Why It Matters: AI Fabric isn’t about replacing existing systems, it’s about enhancing them. By building a well-structured, governed data foundation, AI Fabric ensures your AI models operate reliably and flexibly, driving meaningful insights faster and smarter. #AIFabric #DataManagement #GenerativeAI #DataGovernance #LLM #MachineLearning #AIIntegration #Automation #Scalability #DataStrategy

The potential of #AI for business is unquestionable. Predictive AI is already being used to recognize patterns, achieve dramatic efficiency improvements, and solve business and social problems with speed and effectiveness. But AI also presents critical new challenges for the modern organization with multiple siloed #data sources. How do we harmonize the power of AI with our data initiatives? In my recent article on #CXOInsight, I dive into what I see as the four main areas that organizations need to get right in order to use AI for business: 1. Alignment: ensure alignment between your data and the AI teams functioning within the organization. 2. Unstructured Data: each organization needs an updated view of its unstructured data landscape and its relevant applications to be ready to use them with AI applications. 3. Integration: organizations must integrate their workloads and data with an intelligent multicloud hybrid infrastructure to be able to access any data, from any environment, and any workload. 4. Governance: every organization will be required to strengthen data security and governance in this new landscape of data processing and availability. And NetApp’s intelligent data infrastructure strategy is the foundation for it all. 😎 http://ms.spr.ly/6049lYHmH

In a recent conversation with a data leader, we explored how organizations are overcoming the challenges of scaling AI solutions and turning them into real business value. Here’s how they’re addressing common obstacles: 1. Ensure Data Quality and Availability AI models can only be as good as the data they’re trained on. Ensuring access to high-quality, consistent data is crucial for success. By building reliable data pipelines and robust preprocessing methods, organizations ensure that their models are always fed with the right information. 2. Streamline Model Integration and Deployment Moving from model development to deployment is often complex. To tackle this, organizations are using proven frameworks to automate model integration, reducing friction and speeding up the deployment process so that AI can start delivering results faster. 3. Implement Continuous Model Monitoring and Maintenance AI models need constant monitoring to stay effective. As business conditions change, models may need adjustments. Automated monitoring tools help track performance in real time, allowing teams to quickly identify and address any issues that may arise, keeping models aligned with business goals.

🚀 Exciting news! 🚀 The convergence of Generative AI (Gen AI) and data modernization has redefined business intelligence in unimaginable ways. 🌟 This powerful combination serves as a strategic differentiator, driving innovation, enhancing cybersecurity, and providing the vital competitive edge necessary for rapid growth. 💡 🔗 Source: Gen AI for Enterprise Data Modernization (Mastek Blog) In today's era of progress and the need for constant development to gain a competitive advantage, generative artificial intelligence (GenAI) in business intelligence (BI) is effectively transforming how organizations analyze and use data. 📊💼 [1] [2] [3] 🔗 Source: Leveraging GenAI for Business Intelligence (Billennium) Data has become the new currency in today's dynamic business landscape. 💰 Companies that can effectively harness the power of data analytics and generative artificial intelligence (Gen AI) are poised to gain a competitive edge, drive innovation, and achieve sustainable growth. 📈 🔗 Source: Data Analytics and Gen AI: Fight or Flight? (Mexico Business News) Generative business intelligence, also known as generative BI, combines generative AI with business intelligence tools and promises to help businesses operate in a smarter, faster way. 🤝✨ 🔗 Source: Generating Genuine Business Intelligence from AI Technology (Raconteur) The rise of generative artificial intelligence has the potential to transform the way people work, and one of the most intriguing possibilities lies in how it can make business intelligence software more accessible, enabling true "self-service" access to data. 📲💡 🔗 Source: GenAI Is Transforming Business Intelligence With Easier Access to Insights (AI World Today) Join the revolution and leverage the power of Gen AI for your business! 🌐💪 Share your thoughts and experiences with us in the comments below. Let's shape the future of business intelligence together! 🚀✨ #GenAI #BusinessIntelligence #DataAnalytics #Innovation #AI #FutureOfWork #DigitalTransformation #Technology #DataDriven #CompetitiveEdge References: [1] Quantum Computing and Artificial Intelligence: A Perfect Match?: https://lnkd.in/df254J-g [2] How GenAIxOps revolutionises modern IT operations management and drives business success: https://lnkd.in/dFekXEuP [3] Aligning Data Strategy with Business Goals in the Age of AI: https://lnkd.in/dwYJMVT6

Unlocking AI innovation with trusted data 🎯 AI-based insights are transforming how businesses make critical decisions. At the heart of it all is trusted data, which helps ensure accuracy and scalability. Teradata’s latest advancements look to empower enterprises in leveraging #AI for faster, smarter outcomes. ✨ Read more: 🔗https://lnkd.in/gh-29Ugg Teradata’s Trusted AI initiative focuses on delivering reliable, integrated data that businesses can depend on to drive meaningful 🤖 AI-based insights, Daniel Spurling, senior vice president of product management at Teradata, told #theCUBE. Through collaborations with industry leaders such as #AWS and #Nvidia, Teradata is solidifying its position as a leader in AI innovation. “As we move forward, it's not just about having data; it’s about having the right data, integrated and harmonized, to drive AI success,” Spurling explained. “Businesses can't afford to operate in silos anymore. They need seamless integration.” Discover how trusted data is driving AI initiatives to the next level. ⚡