Deep Analysis’ Post

⏰ 72% of leading organisations see data management as a key blocker and challenge in scaling AI programmes ⏰ Surprising statistic? I don't think so... This came from a McKinsey study and it forms part of 90% of my discussions right now, not just from a information governance or data protection perspective but more out of the fact that in many organisations, information just isn't ready to maximise investments in GenAI! There's no doubt in my eyes that building a strong foundation of data quality is fundamental in succeeding in AI programmes and at EncompaaS we're utilising AI in a transformative way within our classification and enrichment capabilities that enables our clients to understand their information landscape. There are many benefits of doing this but the two key areas I see are: 1. Reduced risk by creating guardrails around information that shouldn't be accessed by AI, whether sensitive or otherwise 2. Improved efficiency and quality of response by expanding the data pool through deep information enrichment Expanding and restricting the data pool sounds like an oxymoron, however it really does work and by doing this foundational practice to improve data quality and management, it allows you to enhance many business processes on top of the ability to scale AI. I'm interested to hear your own thoughts and experiences on this. Let me know in the comments or via DM if you'd prefer! #GenAI #AI #InformationGovernance #dataprotection

92% of organizations are failing to realize the full potential of their Data & AI initiatives. This failure leads to: ❌ wasted resources and investments ❌ missed opportunities for innovation ❌ teams misalignment At the AI + Human Symposium, our CEO Nicolas AVERSENG, will present the StratOps approach to responsible value creation with Data & AI products. Imagine a streamlined, value-driven AI strategy that meets regulatory requirements and internal ethical standards. Picture your organization leading the way in responsible AI utilization, with well-coordinated and successful AI projects. Ready to achieve Data & IA responsible value creation at scale? Today, join us at the AI + Human Symposium! Don’t miss Nicolas’s talk: 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐥𝐞 𝐕𝐚𝐥𝐮𝐞 𝐂𝐫𝐞𝐚𝐭𝐢𝐨𝐧 𝐚𝐭 𝐬𝐜𝐚𝐥𝐞 𝐰𝐢𝐭𝐡 𝐃𝐚𝐭𝐚 & 𝐀𝐈 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐬: 𝐓𝐡𝐞 𝐒𝐭𝐫𝐚𝐭𝐎𝐩𝐬 𝐀𝐩𝐩𝐫𝐨𝐚𝐜𝐡 📆Today, thursday July 11th ⏰ 5:50 PM-6:20 PM ✍️ save your seat : https://lnkd.in/gdKXFPNG #Data #Analytics #IA #digitaltransformation SWARM Community

𝐓𝐡𝐞 𝐈𝐦𝐩𝐨𝐫𝐭𝐚𝐧𝐜𝐞 𝐨𝐟 𝐀𝐜𝐜𝐮𝐫𝐚𝐭𝐞 𝐃𝐚𝐭𝐚 𝐢𝐧 𝐀𝐈 𝐌𝐨𝐝𝐞𝐥 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 In the realm of artificial intelligence, the quality of data is paramount. Accurate and high-quality data serves as the foundation for training effective AI models. 𝗞𝗲𝘆 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝗼𝗳 𝗨𝘀𝗶𝗻𝗴 𝗔𝗰𝗰𝘂𝗿𝗮𝘁𝗲 𝗗𝗮𝘁𝗮: --> Enhanced Performance: Good data ensures that models learn relevant patterns and make informed decisions, leading to improved performance and reliability. --> Reduced Bias: High-quality data helps minimize biases, which can distort predictions and lead to erroneous outcomes. --> Increased Trust: For businesses, investing in accurate data fosters trust among stakeholders and enhances decision-making processes. Ultimately, the success of any AI initiative hinges on the commitment to sourcing, curating, and maintaining high-quality data. #AI #ArtificialIntelligence #DataQuality #MachineLearning #DataScience #DataIntegrity #ModelTraining #BiasReduction #BusinessIntelligence #AIInitiatives #DataDriven #QualityData #TrustInData

The 6 Pillars of Data Excellence for AI The immediate and future impact of Artificial Intelligence (AI) is unmistakable. To leverage AI's full potential, however, it's crucial that our data stands on solid ground. Tackling the challenges of AI integration, I'd like to highlight six essential attributes that make data genuinely prepared for AI: it must be accurate, relevant, easily accessible, credible, clear, and comprehensive. 1️⃣ Accurate: Precision is non-negotiable. AI can only be as good as the data it's fed. Ensuring accuracy means meticulous verification and regular updates to keep pace with the ever-evolving data landscape. 2️⃣ Relevant: Not all data matters equally. Relevance ensures that the focus remains sharp on data that drives meaningful insights and decisions, cutting through the noise to find the signal. 3️⃣ Accessible: Accessibility is about breaking down barriers. Data must be readily available to those who need it when they need it, in a format that's ready for AI to analyze and interpret. 4️⃣ Authoritative: Trust is paramount. Data sources must be reliable and respected, serving as a definitive guide for AI systems to draw from with confidence. 5️⃣ Explainable: The black box dilemma must be addressed. AI's workings and the data it relies on should be transparent, fostering trust and understanding among all stakeholders. 6️⃣ Complete: In the puzzle of AI, every piece matters. Completeness ensures that no critical information is missing, enabling a holistic view for AI to operate effectively. Using these 6 pillars of data management, take the time to have your technology teams ensure the foundations are solid for future use of AI. I invite you to share your thoughts and experiences in preparing data for AI readiness. How are you ensuring your data meets these six critical criteria? #AI #DataQuality #DigitalTransformation #Innovation

🔍 𝐓𝐡𝐞 𝐂𝐫𝐢𝐭𝐢𝐜𝐚𝐥 𝐑𝐨𝐥𝐞 𝐨𝐟 𝐃𝐚𝐭𝐚 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 𝐢𝐧 𝐀𝐈 🔍 In the ever-evolving world of Artificial Intelligence, data is the backbone that powers innovation and accuracy. But here's the catch: AI is only as good as the data it’s built on. This is where 𝐃𝐚𝐭𝐚 𝐆𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞 steps in as a game-changer. 🌟 Effective data governance ensures that the data feeding your AI models is accurate, consistent, and free from biases. It sets the standards for data quality, making sure that your AI isn’t just running, but running 𝘳𝘪𝘨𝘩𝘵. Without these robust governance practices, even the most advanced AI algorithms can fall short, leading to unreliable predictions and potentially costly mistakes. 🚫 By prioritizing data governance, organizations not only enhance the performance of AI models but also ensure ethical and compliant use of data—essential in today's data-driven landscape. This is the key to unlocking the full potential of AI, driving better decision-making, and maintaining a competitive edge. 💼🔑 Let's remember: Quality data isn't just a technical requirement—it's the foundation of trustworthy AI. #DataGovernance #AI #DataQuality #EthicalAI #Innovation #MachineLearning #DataScience #TechLeadership

Prudent CDAOs must explore alternative approaches that facilitate secure data access and accelerate AI adoption, or risk becoming entangled in a blame game when AI rollouts stall. As a data leader, you understand the immense potential of AI to drive innovation and create competitive advantages. But accessing your organization's most valuable data assets for AI initiatives can be a daunting task, fraught with challenges around data security, privacy, and organizational silos. We are pleased to partner with Sol Rashidi, former CDAO & CDO at Fortune 100s, to bring you our latest guide to secure data and impactful AI. In "Overcoming Barriers to Data Accessibility for AI," we explore these challenges head-on and provide actionable strategies to help you unlock the full potential of your data for AI. 🔑 Key takeaways include: - Navigating the complexities of shared accountability for AI initiatives across CDOs, CIOs, and CISOs - Rethinking your data strategy for the unique demands of Generative AI - The perils of delaying AI projects while waiting for ideal conditions or infrastructure - Implementing a collaborative approach to data classification that balances innovation and security - Leveraging technologies like our Stained Glass Transform to securely unlock access to sensitive data Read the full guide to learn how you can maximize the value of your data while upholding data confidentiality and privacy. https://lnkd.in/g6E_qPKy Want to explore how Protopia can help expand secure access to your data for AI? Connect with our experts by filling out the form at: https://lnkd.in/g9d9unni

💡 AI and Intelligence Analysis: A Complex Relationship Today’s intelligence analysts face unprecedented data streams—enough to overwhelm even the best minds. The question: can AI manage this flood of information, or does it amplify risks? The answer lies somewhere between extremes. AI isn't a cure-all, nor is it a threat. Instead, it’s a capability that evolves, assisting analysts by separating wheat from chaff amidst vast data volumes. Yet, it’s clear AI’s value lies in augmentation, not replacement. Analysts still play a crucial role, navigating cognitive biases and drawing insights AI alone can't provide. While AI can synthesize multilingual data, summarize lengthy reports, and even flag anomalies, it still relies on data that may be flawed or incomplete. And then, there’s the danger of AI hallucinations or errors from insufficient data—a stark reminder that humans must remain in the loop. Ultimately, AI's place in intelligence work isn’t to be an unquestioned oracle but a trusted teammate. A tool that, when matched with human oversight, offers the potential to uncover actionable insights faster and with more precision. At CORSphere, our Human-Machine Teaming (HMT) interface embodies this philosophy, integrating AI to empower—not replace—operators with intelligent insights. Read more here: https://lnkd.in/egfs6TyJ #AI #IntelligenceAnalysis #DataOverload #CognitiveBias #HumanInTheLoop #AugmentedIntelligence

Data Governance in the Age of AI 🤖 In my latest insight article, I explore the critical role of data governance in the age of artificial intelligence. 💡 Key takeaways: • Data governance has evolved beyond traditional boundaries, becoming more fluid, multifaceted, and interconnected 🌐 • AI governance and data governance are distinct but deeply interconnected concepts 🔗 • Robust data governance is a strategic advantage in the AI era, ensuring quality, ethics, compliance, and scalability ✅ • AI can enhance data governance through automated classification, anomaly detection, and predictive analytics 📈 • Best practices include adopting a flexible framework, focusing on critical elements, integrating with AI strategy, continuous monitoring, and fostering a data-centric culture 💪 Read the full article to dive deeper into the symbiotic relationship between data governance and AI and learn how to effectively implement modern data governance practices. 🌟 📎https://lnkd.in/gd-sbZ79 #DataGovernance #ArtificialIntelligence #AIGovernance #DataDriven #Insights Let me know your thoughts and experiences with data governance in the age of AI! 💬

Bold predictions like "The future of all code will be API calls to LLMs" and "The future of each industry lies in the unique insights currently locked up in proprietary data" converge on one critical path: The race to unlock value from this new era in ML/AI has simultaneously accelerated the race to harness an organization's unique vantage points in the data supply chain. This presents a conundrum for CIOs and CDOs: "How do we harness our data to seize AI-driven market opportunities while continuing to protect our data ownership, as well as mitigating risks to our organization, customers, and partners?" Here's the latest from our Eiman Ebrahimi and Abhishek Sharma who collaborated with Sol Rashidi for helping Data Leaders think through unlocking the full potential of your data in the AI era. #GenAI #CIO #CDAO #ArtificialIntelligence #EnterpriseAI

𝐓𝐡𝐞 𝐢𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐨𝐟 𝐀𝐈 𝐢𝐬 𝐨𝐧𝐥𝐲 𝐚𝐬 𝐠𝐨𝐨𝐝 𝐚𝐬 𝐭𝐡𝐞 𝐝𝐚𝐭𝐚 𝐢𝐭’𝐬 𝐛𝐮𝐢𝐥𝐭 𝐨𝐧. Just like in John Searle’s famous 𝑪𝒉𝒊𝒏𝒆𝒔𝒆 𝑹𝒐𝒐𝒎 𝒆𝒙𝒑𝒆𝒓𝒊𝒎𝒆𝒏𝒕, where instructions dictate responses without true understanding, AI relies entirely on the quality of the data it’s fed. If the data is flawed, your AI’s performance will suffer—no matter how advanced the algorithm. Here’s how data quality impacts AI performance: ➡️ 𝐀𝐜𝐜𝐮𝐫𝐚𝐜𝐲: Poor data leads to inaccurate predictions and faulty decision-making. Clean, reliable data ensures precision in AI models. ➡️ 𝐑𝐞𝐥𝐞𝐯𝐚𝐧𝐜𝐞: Irrelevant or outdated data can make AI systems deliver irrelevant or outdated insights. Relevant, up-to-date data is critical for producing valuable results. ➡️ 𝐂𝐨𝐧𝐬𝐢𝐬𝐭𝐞𝐧𝐜𝐲: Inconsistent data causes AI models to behave unpredictably, undermining trust in the system. Consistent data maintains reliability. ➡️ 𝐁𝐢𝐚𝐬: Biased data can create AI systems that reinforce unfair practices. Balanced, representative data ensures unbiased outcomes. ➡️ 𝐂𝐨𝐦𝐩𝐥𝐞𝐭𝐞𝐧𝐞𝐬𝐬: Missing data points leave AI models with gaps, leading to incomplete conclusions. Comprehensive data fills those gaps, enhancing the AI’s capabilities. Ultimately, the quality of your AI system depends on the quality of your data. Do you have what it takes to build AI that truly understands? Follow for more insights on AI and data testing, or connect for expert advice! #Data #BigData #ArtificialIntelligence Video Source: Thomas Mulligan (Youtube)