Rafal Jacyna’s Post

Qlik CEO, Mike Capone, recently sat down with CXO Talk to discuss the intersection of data, AI, and responsible innovation.  Tune in now! #Qlik #CXOTalk #AI  hashtag #analytics hashtag #artificialintelligence hashtag #businessintelligence hashtag #dataanalytics hashtag #dataintegration hashtag #dataops hashtag #datascience hashtag #datavisualization hashtag #embeddedanalytics hashtag #software hashtag #strategy hashtag #technology

Avi lays out our platform journey, how we address market shifts and why it matters!

The 2024 MAD (ML, AI and Data) landscape by Matt Turck and Aman Kabeer (Thanks!!) just released 📣 📣 and now that it's released, I'm excited to share Dataloop AI journey from a leading data annotation tool to an end-to-end AI platform for enterprises. This strategic pivot is not just a response to the evolving market demands but a forward-looking vision to redefine how businesses will leverage AI technologies. 𝐓𝐡𝐞 𝐩𝐮𝐬𝐡 𝐟𝐨𝐫 𝐂𝐡𝐚𝐧𝐠𝐞 ↗ Recently, the AI industry has witnessed major shifts, most notably in the availability of GenAI models (LLM, LMM, LXM) as they become completely free for commercial use and the maturity of enterprise AI workflows, which are currently pieced together from dozens of AI/ML tools, becomes unsustainable to a growing demand for integrated AI solutions. Our observations, backed by conversations with industry leaders and our valued customers, highlighted a clear trend: The need for holistic, data-centric AI platforms that cater to the entire lifecycle of AI development, from data prep and management to model deployment and scaling. 𝗘𝗺𝗯𝗿𝗮𝗰𝗶𝗻𝗴 𝘁𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗨𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗗𝗮𝘁𝗮 🎬 🎶 📖 Dataloop AI pivot is driven by the raising of the critical role that unstructured data plays in the AI landscape. Unlike structured data, unstructured data – is the fuel for GenAI models, offering insights and capabilities. Dataloop has always excelled at managing and processing unstructured data, and now with our new platform leverages this expertise to provide the best unstructured data management and data pipeline solutions available. 𝗔 𝗖𝗼𝗺𝗽𝗿𝗲𝗵𝗲𝗻𝘀𝗶𝘃𝗲, 𝗘𝗻𝗱-𝘁𝗼-𝗘𝗻𝗱 𝗔𝗜 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 𝗣𝗹𝗮𝘁𝗳𝗼𝗿𝗺 🔛 Dataloop's new platform is a testament to our commitment to excellence. We're not just addressing the immediate needs of our customers but setting the stage for the future of AI development. Our platform offers a seamless, integrated experience that covers every aspect of AI workflow, making AI development accessible, scalable, and efficient for enterprise. 𝗪𝗵𝘆 𝗧𝗵𝗶𝘀 𝗣𝗶𝘃𝗼𝘁 𝗠𝗮𝘁𝘁𝗲𝗿𝘀 ⏩ This strategic shift is more than just a change in our product offerings; it's a reflection of our deep understanding of the AI industry's trajectory and staying ahead of the curve. By focusing on comprehensive solutions that address the entire AI development cycle, Dataloop is uniquely positioned to empower businesses to unlock the full potential of AI technologies. 𝗟𝗼𝗼𝗸𝗶𝗻𝗴 𝗔𝗵𝗲𝗮𝗱 𝘄𝗶𝘁𝗵 𝗘𝘅𝗰𝗶𝘁𝗲𝗺𝗲𝗻𝘁 😎 The journey ahead is incredibly exciting. We're not just participating in the AI revolution; we're leading it. Our end-to-end AI platform is designed to meet the dynamic needs of the market, ensuring that our clients have the tools and support they need to succeed in the rapidly evolving GenAI landscape. visit www.dataloop.ai #AI #DataAnnotation #GenerativeAI #UnstructuredData #Innovation #TechLeadership #StrategicPivot #datapipeline

🚀 Building a Resilient Data Strategy to Power Enterprise AI As AI becomes a core driver of innovation, establishing a robust, technology-driven data foundation is essential for organizations aiming to harness its full potential. However, many enterprises face a range of challenges—data silos, governance limitations, and quality issues—that prevent them from fully capitalizing on AI’s transformative power. A recent insight from MIT Technology Review in partnership with Snowflake underscores the need for a resilient data strategy that enables AI to scale effectively. As Prasanna Krishnan from Snowflake highlights, “A strong data foundation brings data together, controls access, and enables insights.” This approach involves unifying data across platforms, enforcing governance, and ensuring quality and accessibility at every stage. Key challenges like fragmented data and limited access can undermine AI’s capabilities, but overcoming these barriers allows organizations to democratize insights, enhance decision-making, and fuel innovation. Moreover, by integrating advanced AI-powered tools, companies can tap into new insights from unstructured data, enabling proactive responses to market shifts. This is not just about meeting today’s demands but future-proofing for an AI-driven tomorrow. Organizations that build scalable, adaptive data frameworks now will be ready to navigate emerging complexities in the digital landscape, making AI a sustainable, competitive advantage. To learn more, read the full article on creating a strong, adaptable data foundation that powers enterprise AI! https://lnkd.in/gH_mX6EP 💡 #DataStrategy #EnterpriseAI #DigitalTransformation #AI #snowflake

AI models need to be trained & validated on high-quality, fit-for-purpose data to produce trustworthy, accurate, & consistent results.  Increasingly, this data is being drawn from real-world data sources, many of which require careful curation to get to the point of being usable for these purposes. So what’s really involved in this process, & how will it change in the future as our understanding of both AI & RWD evolve? We asked one of COTA’s product leads, Nick Ritter, to help us answer some key questions. 1️⃣ What data quality metrics have proven effective in identifying & mitigating biases in AI models developed from real-world-data?  Mitigating biases in models developed from RWD starts by mitigating any biases in the datasets themselves.  We would rely on quality metrics such as data completeness & representativeness, appropriate sample sizes, the recency of data to ensure the RWD & resulting AI-based products would perform with minimal biases. 2️⃣ Given the current limitations of data standardization, how can we ensure that AI models trained on heterogeneous RWD maintain high predictive accuracy across different healthcare settings? The answer lies in collaboration across the industry & across settings where AI models are being deployed.  Developers & users need to create high-quality data standards – & push themselves & others to conform to those standards.  Users of AI models also need to do their part by understanding which datasets & standards their models are based on so they can make the right choices about how to use the results & provide meaningful feedback to developers.  At COTA, we’ve developed a data model based on the type of high-quality standards we’d like to see across the industry, & we’re subsequently building our AI applications based on those standards. 3️⃣ What’s your 3-to-5 year prediction about the dynamic between human data curation & AI’s ability to perform the same functions? While we’re excited about the potential use of AI for the purpose of curation, we know there’s currently a gap between AI’s maturity in this space & the imperative to ensure that researchers have access to the highest quality RWD.  That being said, COTA is currently developing products where AI shows the most opportunity to provide assistance. We’re staying focused on the extremely high standards these models have to meet in order to replace or assist experienced humans in these tasks. In the next few years, I suspect we’ll see certain data elements that can be reliably curated by technology, given the nature of how they’re documented & discussed among healthcare teams.  But we can’t lose sight of the value that human intelligence & organic clinical knowledge can bring to the curation process, especially around complex health data. #RWD #AI #Healthcare

As #AI spreads across companies, having a strong data foundation is a must. #DataGovernance is crucial for #DataDemocratization. Our own Christian Kleinerman explains how Snowflake is committed to helping you strengthen your data strategy. Read more here: https://lnkd.in/gU7Cf8Sk

🚀 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗛𝗮𝗻𝗱𝗹𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗦𝗶𝗺𝗽𝗹𝗔𝗜: 𝗛𝗮𝗿𝗻𝗲𝘀𝘀 𝘁𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗮𝗻𝗱 𝗨𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗗𝗮𝘁𝗮! 🧩 At SimplAI, we're redefining the boundaries of AI and data integration. With the ability to seamlessly manage Document Collections (Unstructured), Structured, and Structured with Files datasets, we're enabling businesses to maximize the potential of their data like never before. Here's how: 🔍 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻 (𝗨𝗻𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱): Whether it's a trove of text documents, web content, or other unstructured data formats, SimplAI effortlessly processes and extracts insights using advanced techniques. Our platform turns raw, unstructured data into actionable intelligence, making sense of the complexities that traditional systems overlook. 📊 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝗗𝗮𝘁𝗮: For those who rely on organized datasets such as databases and spreadsheets, SimplAI ensures a smooth, intuitive integration. Our platform handles your rows and columns with precision, allowing you to deploy AI models that yield high-value, context-aware insights from clean, well-organized data. 📁 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝗱 𝘄𝗶𝘁𝗵 𝗙𝗶𝗹𝗲𝘀: SimplAI bridges the gap between structured data and file-based inputs, offering a unique approach to integrating diverse data sources. From CSVs to JSON files, our platform not only supports these formats but enriches them, aligning with your AI strategy for consistent, accurate results. 🌟 𝗪𝗵𝘆 𝗖𝗵𝗼𝗼𝘀𝗲 𝗦𝗶𝗺𝗽𝗹𝗔𝗜? 𝗨𝗻𝗶𝗳𝗶𝗲𝗱 𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆: SimplAI's flexible dataset management capabilities mean you’re not limited by data type. Train, deploy, and scale AI models without the hassle of data silos. 𝗘𝗻𝗵𝗮𝗻𝗰𝗲𝗱 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗠𝗮𝗸𝗶𝗻𝗴: With a comprehensive view of both structured and unstructured data, SimplAI enables more informed, data-driven decisions. 𝗘𝗳𝗳𝗼𝗿𝘁𝗹𝗲𝘀𝘀 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Whether it's millions of unstructured documents or complex structured files, SimplAI scales with your needs, ensuring your AI solutions are always on point. Data diversity is not a challenge, it's an opportunity. SimplAI empowers your data journey, no matter the format, driving innovative solutions with ease and efficiency. Ready to elevate your AI capabilities? Let's simplify AI, together. Sandeep Dinodiya Santhosh Kumar K. 💻 Request Demo: https://lnkd.in/gK7bDCNs #AI #DataScience #MachineLearning #UnstructuredData #StructuredData #DataIntegration #NLP #ArtificialIntelligence #DataStrategy #TechInnovation #AIApplications #BigData #DataManagement #SimplAI #AIPlatform #AITransformation #DigitalTransformation #SmartData #FutureOfAI

4 ways for data officers to take the helm on AI initiatives

I recently wrote an article for Data Centre Review on a topic that’s top of mind for every leader right now: generative AI (gen AI) and how it’s transforming businesses. In our latest research, 82% of business leaders agree AI is significantly impacting organizational goals, with almost half of board members prioritizing it above all else. But here's the key: success with gen AI isn’t just about modernizing your data stack—it’s about picking the right use cases to drive real ROI. In this piece, I dive into: ✅ The importance of balancing data modernization with practical gen AI use cases. ✅ How organizations can set up their teams for AI success.  ✅ Why Large Language Models (LLMs)could be the game-changer your business needs right now. Check out the full article to learn more: https://lnkd.in/eP9syAi5 #GenerativeAI #DataModernization #AI

Below are some ideas to help you start on your AI journey: [1] Get your data sorted: Data is the fuel for anything you want to do with AI. Establish a datalake, ingest some data, map it, clean it - don’t do anything until you have a nice, clean, credible source of data. [2] Define your problem: Know why you're implementing AI. If your CEO says “we need AI” - that’s not actually a reason. Identify some use cases, define the problems you are trying to address. Don’t start “implementing AI” until you have a clearly articulated problem you are trying to fix. [3] Identify areas of opportunity: You will get a second, third, fourth chance - so focus on a high-impact area. Pick something important - do it well, then you will be well placed to do other things. Don’t kick off 20 AI projects trying to fix the whole company. [4] Evaluate your internal capabilities: You likely have a wealth of internal talent who would jump at the chance to work on some AI projects. Perhaps you need some external help to get you started - but leverage your internal talent first. [5] Pilot an AI project: Build it, test it, learn from it, and then iterate. A pilot for even a complex problem should only take a few weeks. If the resources are saying it will take months before anything is produced then you need external help, because whoever is telling you it will take months doesn’t know what they are doing. [6] Either make it better, or pick the next use case: Celebrate small victories and build from there. If your first project delivers 80% of the benefit then consider moving to the next project, rather than spending the next 12 months trying to squeeze the remaining 20% of value. [7] Revisit your data: Streamline your data collection, establish proper data management, make someone responsible for the datalake - your first AI project would have taken 20% of the time if you had proper clean data at the start. If your data team is saying the data is perfect get external help - because whoever is telling you this doesn’t know what they are doing. [8] Continuously Improve AI Models and Processes: Stay adaptive and responsive to changes. The journey to AI implementation isn't a sprint; it's a marathon. You need a clear vision, it pays to take incremental steps, and everything is easier if you have nice clean data. #artificialintelligence