Data Literacy Webinar: Closing the AI Capability Gap

𝗗𝗮𝘁𝗮 & 𝗔𝗜 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗮𝘁𝗶𝗼𝗻 𝘀𝘁𝗮𝗿𝘁𝘀 𝘄𝗶𝘁𝗵 𝗽𝗲𝗼𝗽𝗹𝗲, 𝗻𝗼𝘁 𝗽𝗹𝗮𝘁𝗳𝗼𝗿𝗺𝘀. One lesson that keeps coming up in my conversations with leaders: 𝗗𝗮𝘁𝗮 & 𝗔𝗜 𝗹𝗶𝘁𝗲𝗿𝗮𝗰𝘆 𝗶𝘀 𝗻𝗲𝘃𝗲𝗿 𝗼𝗻𝗲-𝘀𝗶𝘇𝗲-𝗳𝗶𝘁𝘀-𝗮𝗹𝗹. Different roles need different levels of literacy. Some teams need depth. Others need just enough to work confidently with data and AI in their daily decisions. In our upcoming webinar, 𝗕𝘂𝗶𝗹𝗱 𝗗𝗮𝘁𝗮 & 𝗔𝗜 𝗟𝗶𝘁𝗲𝗿𝗮𝗰𝘆 𝗶𝗻 𝟮𝟬𝟮𝟲: 𝗣𝗲𝗼𝗽𝗹𝗲, 𝗦𝗸𝗶𝗹𝗹𝘀, 𝗧𝗼𝗼𝗹𝘀, Nina Stefels and Rozaliya K. will explore how organizations create: • A shared baseline of Data & AI literacy • Role-specific skills where they matter most • Clarity on what people should and shouldn’t know 📅 𝗙𝗲𝗯𝗿𝘂𝗮𝗿𝘆 𝟭𝟬, 𝟮𝟬𝟮𝟲 | 𝗟𝗶𝘃𝗲 𝗼𝗻 𝗭𝗼𝗼𝗺 If you’re thinking about how to scale Data & AI literacy across your organization without overengineering it, this session is for you. 👉 𝗦𝗮𝘃𝗲 𝘆𝗼𝘂𝗿 𝘀𝗲𝗮𝘁: https://okt.to/jfm0ru. #Data #DataLiteracy #AILiteracy #Teams #DataTransformation #Xebia

Is data literacy the next cross-industry competitive edge? This recent Patterns by Cell Press article examines how the FAIR data principles are (and aren’t) being taught across disciplines, and the implications are big. FAIR isn’t just academic jargon; it’s becoming a foundational expectation in: 🔸 data governance 🔸 analytics workflows 🔸 AI/ML projects 🔸 cross-agency collaboration 🔸 compliance frameworks Whether you work in federal contracting, healthcare, research, or tech, asking “Is our data truly Findable, Accessible, Interoperable, and Reusable?” is a leadership question. What stands out to me is this: Education and communication are the missing infrastructure. We keep producing new frameworks, dashboards, and security language. But without translation, toolkits, and real workforce training, most of it never meaningfully changes practice. Sometimes what’s needed isn’t another model, it’s someone willing to pause and ask: • What does this actually mean in plain language? • What behaviors are we trying to change? • What KPIs demonstrate real impact? • How does this improve outcomes for the end user? If we can’t define it clearly, we can’t teach it. If we can’t teach it, we can’t scale it. Until workforce education keeps pace with AI and data policy, even the best frameworks will struggle to deliver impact. Curious to hear from others: 👉 Have you applied FAIR principles in practice? 👉 What has helped — or hindered — deeper data literacy in your teams? Read the article: https://lnkd.in/ePmNW_Nm Shanahan et al., Progress toward a comprehensive teaching approach to the FAIR data principles 🔗 #DataScience #FAIRData #DataGovernance #AIpolicy #WorkforceDevelopment

What does responsible AI really look like in the public sector?🤖 One of the most watched sessions from last year’s Government Innovation Showcase Federal explored how AI and data analytics are shaping the future of public services, from unlocking value in government data to navigating the realities of governance, ethics, and trust. If you missed it (or want a refresher), this podcast offers practical insight into how federal organizations are thinking about AI adoption, data-driven decision-making, and long-term sustainability. 🎧Listen to the full podcast:https://hubs.ly/Q041PBgQ0 Building on last year’s discussion, at next month's Government Innovation Showcase Federal, Vanessa Kientega, A/Director, Enterprise Data & AI Governance from Emploi et Développement social Canada (EDSC) / Employment and Social Development Canada (ESDC) will be joining a panel discussion on Data Governance and Privacy, sharing new perspectives, lessons learned, and how their approach has evolved over the past year as AI adoption continues to accelerate across government. If this topic matters to your work, you’ll find many more conversations like this throughout the program. Registration is complimentary and exclusive for public sector employee: https://hubs.ly/Q041PJ310 #GIWFederalCA #InnovationFedCA #GovernmentInnovation #AIinGovernment #DataGovernance #ResponsibleAI #GovTech #DigitalGovernment #PublicService

AI Is Not Replacing Data Science. It’s Raising the Standard. The same common concern has been expressed lately: "What happens to data science now that AI is widespread?" One thing struck me as very apparent after reading the January 2026 article How Executives are thinking about AI in 2026 in the Harvard Business Review; AI will not replace data science, AI is on top of it. 99% of executives surveyed say AI and data are top priorities in their organizations. At the same time, 93% percent say the biggest challenge is human issues like culture, fear, and change management, not technology. That difference is important. AI systems do not clean data, question assumptions, or understand social context on their own. They rely on the very foundations that Data Science provides. What AI is doing is automating the surface-level work and exposing who truly understands what is happening underneath. This is not the end of Data Science. It is the end of shallow Data Science. The future belongs to professionals who can combine data, judgment, ethics, and real world understanding. Those who can translate numbers into decisions and systems into impact. AI is not replacing thinkers. It is demanding better ones. Referenced from Harvard Business Review, January 2026. Read more: https://lnkd.in/g2q_mN2P Eric OdokHarrison Weda

Data is no longer a support function. It is the competitive edge. Yet most organizations still operate with fragmented pipelines, siloed teams, reactive governance, and fragile systems that quietly erode trust. We wrote this white paper to address a simple but urgent reality: The companies winning in 2026 aren't the ones with the most dataa, they're the ones who can activate it, govern it, and trust it. In this paper, we explore: • The real cost of data chaos • Why AI changes data operations permanently • How unified platforms compound business advantage • Practical steps to become a data-intelligent organization If you're a CTO, CDO, Head of Data Engineering, or executive responsible for business outcomes driven by data, this will be worth your time. The question is no longer whether data matters. The question is whether your systems are built to turn it into advantage. 📄 Download the full white paper below. https://lnkd.in/eB7D6VNi #DataEngineering #DataLeadership #AI #DataGovernance #EnterpriseTechnology #AkronimForge

**📢 Breaking News: The Quiet Revolution in Data Compression** 🔍 **Curiosity-Driven Insight:** Have you ever wondered how much more efficient your data processes could be? Recent advancements in AI-driven data compression have shown that what was once a 315 KB output can now be reduced to a mere 5.4 KB using innovative techniques like Context Mode in Claude Code. 🧩 **The Pain Points:** In the fast-paced world of digital leadership, organizations face significant challenges: talent gaps in data management, ethical concerns over data usage, scaling hurdles, and the need for rapid innovation. Efficient data handling is crucial for overcoming these obstacles. 💡 **The Opportunity:** This shift towards ultra-efficient data compression empowers teams to handle larger datasets with minimal storage and processing requirements. Leaders can now focus on strategic initiatives rather than infrastructure constraints. 🧠 **Agentic Thinking:** What if our tools become thought partners? Imagine a future where AI not only processes data but also anticipates needs, suggests improvements, and drives innovation. This is the dawn of a new era in digital leadership. 🚀 As we embrace these advancements, let's reflect on how we can leverage this efficiency to drive forward-thinking strategies and lead our organizations into a data-empowered future. #datacompression #aiinnovation #digitalleadership #techtrends #agenticbrief #SwapnilBabu #ai #news

I’ll be speaking at Lab of the Future USA in two weeks on a topic that continues to surface in conversations with R&D and data leaders.    AI capabilities in discovery are advancing rapidly. What remains difficult is making outcomes repeatable across teams, programs, and time.    In many environments, success still depends on local knowledge: who prepared the data, how assumptions were handled, and which transformations were applied. When that context is not preserved, results become harder to validate and even harder to reuse.    Repeatability is not a modeling problem. It is a data foundation problem.    This is the lens I’ll bring to my keynote: how context-rich, governed data enables predictive R&D that organizations can rely on, not just demonstrate.  #LifeSciences #RAndD #ScientificData #FAIRData #AIInDiscovery #LabOfTheFuture

Repeatable AI outcomes don’t happen by accident — they happen when data foundations are built to preserve context, governance, and traceability. That’s exactly what Wolfgang Colsman, Founder & CEO of ZONTAL, will be speaking about at Lab of the Future USA. 𝐊𝐞𝐲𝐧𝐨𝐭𝐞 + 𝐩𝐚𝐧𝐞𝐥 𝐝𝐞𝐭𝐚𝐢𝐥𝐬 — 𝐃𝐚𝐲 2 | 𝐌𝐚𝐫𝐜𝐡 3  • 12:35 — Making AI Discovery and Translational Evidence Repeatable: The Data Foundation for Predictive R&D (Keynote)  • 12:50 — Panel Discussion and Q&A Learn more about Wolfgang’s keynote and why context-rich data enables predictive R&D you can rely on: https://lnkd.in/gbUi7ABz If you’re attending, stop by Booth 17 and say hello to the ZONTAL + SciY team. ➤ Connect with our team and book time with our executive leadership: https://lnkd.in/gtuWddk8 #LabOfTheFuture #LifeSciences #ScientificData #DataIntegrity #AIinRAndD #PredictiveRAndD #DigitalLab #ZONTAL #SciY

𝟰𝟲% 𝗼𝗳 𝗼𝗿𝗴𝗮𝗻𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀 𝘀𝗮𝘆 𝘁𝗵𝗲𝗶𝗿 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 𝗯𝗮𝗿𝗿𝗶𝗲𝗿 𝘁𝗼 𝗔𝗜 𝘀𝘂𝗰𝗰𝗲𝘀𝘀 𝗶𝘀𝗻’𝘁 𝘁𝗲𝗰𝗵𝗻𝗼𝗹𝗼𝗴𝘆, 𝗶𝘁’𝘀 𝗰𝘂𝗹𝘁𝘂𝗿𝗲. Despite heavy investment in #dashboards, analytics, and #AI tools, many organizations still struggle to turn data into better decisions. The reason is simple: technology alone doesn’t change how people think, question, and decide. In our upcoming webinar, Build Data & AI Literacy in 2026, we’ll explore how organizations build the skills, mindset, and confidence people need to work effectively with data and AI, across roles, not just in technical teams. 🎓 𝗟𝗶𝘃𝗲 𝗪𝗲𝗯𝗶𝗻𝗮𝗿 📅 𝗙𝗲𝗯𝗿𝘂𝗮𝗿𝘆 𝟭𝟬 | 𝟭𝟭 𝗔𝗠 𝗖𝗦𝗧 | 𝟭𝟬:𝟯𝟬 𝗣𝗠 𝗜𝗦𝗧 | 𝟲 𝗽𝗺 𝗖𝗘𝗧 👉 𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿 𝗵𝗲𝗿𝗲: https://okt.to/M0OYRd. #Xebia #Data #DataLiteracy #Learning #DataDriven