Emerging Data Technology Trends

Data Integration Revolution: ETL, ELT, Reverse ETL, and the AI Paradigm Shift In recents years, we've witnessed a seismic shift in how we handle data integration. Let's break down this evolution and explore where AI is taking us: 1. ETL: The Reliable Workhorse      Extract, Transform, Load - the backbone of data integration for decades. Why it's still relevant: • Critical for complex transformations and data cleansing • Essential for compliance (GDPR, CCPA) - scrubbing sensitive data pre-warehouse • Often the go-to for legacy system integration 2. ELT: The Cloud-Era Innovator Extract, Load, Transform - born from the cloud revolution. Key advantages: • Preserves data granularity - transform only what you need, when you need it • Leverages cheap cloud storage and powerful cloud compute • Enables agile analytics - transform data on-the-fly for various use cases Personal experience: Migrating a financial services data pipeline from ETL to ELT cut processing time by 60% and opened up new analytics possibilities. 3. Reverse ETL: The Insights Activator The missing link in many data strategies. Why it's game-changing: • Operationalizes data insights - pushes warehouse data to front-line tools • Enables data democracy - right data, right place, right time • Closes the analytics loop - from raw data to actionable intelligence Use case: E-commerce company using Reverse ETL to sync customer segments from their data warehouse directly to their marketing platforms, supercharging personalization. 4. AI: The Force Multiplier AI isn't just enhancing these processes; it's redefining them: • Automated data discovery and mapping • Intelligent data quality management and anomaly detection • Self-optimizing data pipelines • Predictive maintenance and capacity planning Emerging trend: AI-driven data fabric architectures that dynamically integrate and manage data across complex environments. The Pragmatic Approach: In reality, most organizations need a mix of these approaches. The key is knowing when to use each: • ETL for sensitive data and complex transformations • ELT for large-scale, cloud-based analytics • Reverse ETL for activating insights in operational systems AI should be seen as an enabler across all these processes, not a replacement. Looking Ahead: The future of data integration lies in seamless, AI-driven orchestration of these techniques, creating a unified data fabric that adapts to business needs in real-time. How are you balancing these approaches in your data stack? What challenges are you facing in adopting AI-driven data integration?

As we step into 2024, I want to extend my warmest wishes to everyone in The Ravit Show Data & AI Community. As we toast to a new beginning, let’s look at 8 Key Trends in Data & AI -- 1. Semantic Layer: This year marks a significant leap in how machines interpret data. We're moving towards a semantic approach where data is not just numbers and text, but meaningful information that machines can understand contextually, and how we interact with AI systems. 2. Data Products: The concept of 'data as a product' is gaining momentum. It’s not just about collecting data anymore; it’s about refining it into a product that delivers real value - turning raw data into a strategic asset for better decision-making and customer insights. 3. Data Platforms: 2024 is seeing the evolution of data platforms into more sophisticated, integrated systems. These platforms are becoming the linchpin of our digital ecosystem, offering seamless access, processing, and analysis of data across various domains. 4. Multimodal Large Language Models (LLMs): LLMs are now going beyond text to understand and interpret multimedia content. This evolution opens up new avenues for AI applications in areas like content creation, media analysis, and interactive entertainment. 5. New Revenue Streams for Cloud Providers in Generative AI: Cloud computing is getting a major boost from generative AI. This symbiosis is creating novel revenue opportunities and transforming how we think about cloud services and AI capabilities. 6. Rise of Prompt Engineering: As AI becomes more prevalent, the art of prompt engineering is becoming critical. It's about effectively communicating with AI to generate precise and relevant outputs, a skill that's rapidly becoming essential in the tech workforce. 7. Data Privacy, Security, and Responsible AI Practices: With great power comes great responsibility. In 2024, there's an intensified focus on ethical AI, prioritizing data privacy and security. It's about building AI systems that are not only powerful but also trustworthy and responsible. 8. Metadata Management: 2024 is witnessing a surge in the importance of metadata in Data & AI. As we deal with ever-increasing volumes of data, managing metadata – the data about data – is becoming crucial. It’s not just about storing and accessing data anymore; it's about understanding its context, quality, and lineage. Effective metadata management leads to better data governance, quality, and usability, making it a pivotal aspect of data strategy in organizations. These trends are not just predictions; they are the pathways leading us to a more innovative and efficient future in Data & AI. What would you like to add? #data #datascience #datapredictions2024 #theravitshow

Standardization has always played a critical role in solving large-scale problems and building connected ecosystems. We've witnessed this across domains and technologies like REST APIs, becoming a fundamental element in software development by enabling interoperability across systems. A similar shift occurred in the world of data. As AI gained momentum, data interoperability emerged as a major hurdle. Models struggled to train effectively on fragmented data coming from diverse formats and protocols. Industries (irrespective of domain) responded with their own standards like HL7, FHIR, ISO 20022, ISA-95, GS1, and others. Now, with the rise of large language models (LLMs), system integration has become the next big challenge, raising the need for standardization once again. One thing is pretty clear that without seamless integration into broader enterprise systems, the value generated from LLMs remains limited. Just having a larger context window will not add lots of value. That’s where platform evolution comes in, and the rise of the Model Context Protocol (MCP) is a promising direction. While the idea of a standardized interface for LLMs to access and process different products is powerful, it also introduces a new layer of complexity, especially around security and governance. We may be on the verge of the evolution of a new kind of marketplace, much like today’s App stores or Play store. But this won't just transform integration; it will reshape business models. How these servers will be monetized or prioritized based on tasks if multiple options are available. Will every product still need a user interface? Or are we moving toward a fundamentally new way of interacting with software where AI is the UI? #ExperienceFromTheField #WrittenByHuman

Last month, our Devsinc business analyst, accomplished something that would have seemed impossible five years ago. In just two weeks, she built a complete inventory management system for our client's warehouse operations – without writing a single line of code. The client had been quoted six months and $150,000 by traditional developers. Fatima delivered it in 72 hours using our low-code platform, and it works flawlessly. That moment crystallized a truth I've been witnessing: we're experiencing the assembly line revolution of software development. Henry Ford didn't just speed up car manufacturing; he democratized automobile ownership by making production accessible and efficient. Today's no-code/low-code movement is doing exactly that for software development. The numbers tell an extraordinary story: by 2025, 70% of new applications will use no-code or low-code technologies – a dramatic leap from less than 25% in 2020. The market itself is exploding from $28.11 billion in 2024 to an expected $35.86 billion in 2025, representing a staggering 27.6% growth rate. What excites me most is the human transformation happening inside organizations. Citizen developers – domain experts who build solutions using visual, drag-and-drop tools – will outnumber professional developers by 4 to 1 by 2025. This isn't about replacing developers; it's about unleashing creativity at unprecedented scale. When our HR manager can build a recruitment tracking app, our finance team can automate expense reporting, and our project managers can create custom dashboards, we're not just saving time – we're enabling innovation at the speed of thought. For my fellow CTOs and CIOs: the economics are undeniable. Organizations using low-code platforms report 40% reduction in development costs and can deploy applications 5-10 times faster than traditional methods. The average company avoids hiring two IT developers through low-code adoption, creating $4.4 million in increased business value over three years. With 80% of technology products now being built by non-tech professionals, this isn't a trend – it's the new reality. To the brilliant IT graduates joining our industry: embrace this revolution. Your role isn't diminishing; it's evolving. You'll become solution architects, platform engineers, and innovation enablers. The demand for complex, enterprise-grade applications will always require your expertise, while no-code handles the routine, repetitive work that has historically consumed your time. The assembly line didn't eliminate craftsmen – it freed them to create masterpieces. No-code/low-code is doing the same for software development, democratizing creation while elevating the art of complex problem-solving.

The graveyard of healthcare innovation is filled with technically brilliant solutions that couldn't integrate with the systems clinicians use every day. I've watched promising MedTech companies pour millions into product development only to hit an adoption wall when they discover their solution can't access the data it needs or share insights with other systems. By the time they realize interoperability isn't just a technical feature but a fundamental business requirement, it's often too late. The hard truth: in today's interconnected healthcare ecosystem, your technology is only as valuable as its ability to exchange data seamlessly across the continuum of care. As value-based arrangements expand, now covering over 50% of Medicare members, isolated solutions simply cannot deliver the coordinated care and measurable outcomes these models demand. In today’s newsletter we explore Pillar 4 of the VBMT framework, and discuss how data architecture and interoperability can transform integration from technical hurdle to a strategic advantage.     ___________________________________________ Sam Basta, MD, MMM is a pioneer of Value-Based Medical Technology and LinkedIn Top Voice. Over the past two decades, he advised many healthcare and medical technology startups on translating clinical and technological innovation into business success. From value-based strategy and product development to go-to-market planning and execution, Sam specializes in creating and communicating compelling value propositions to customers, partners and investors. His weekly NewHealthcare Platforms newsletter is read by thousands of executives and professionals in the US and globally. #healthcareonlinkedin #artificialintelligence #ai #valuebasedcare #healthcare Vivek Natarajan Tom Lawry Subroto Mukherjee Rana el Kaliouby, Ph.D. Rashmi R. Rao Paulius Mui, MD Avi Rosenzweig Deepak Mittal, MBA, MS, FRM Elena Cavallo, ALM, ACC Chris Grasso  

📌 “𝗙𝗼𝗿 𝗖𝗹𝘂𝗲𝘀 𝗢𝗻 𝗔𝗜’𝘀 𝗜𝗺𝗽𝗮𝗰𝘁 𝗢𝗻 𝗝𝗼𝗯𝘀, 𝗪𝗮𝘁𝗰𝗵 𝗧𝗼𝗱𝗮𝘆’𝘀 𝗧𝗲𝗰𝗵 𝗝𝗼𝗯𝘀”   I recently connected with Joe McKendrick to share my perspective on how AI is reshaping the tech workforce. Grateful to see our conversation featured in Forbes.   Joe underscores a point we’ve been emphasizing for months: 𝗔𝗜 𝗶𝘀 𝗻𝗼𝘁 𝗮 𝗵𝗲𝗮𝗱𝗰𝗼𝘂𝗻𝘁 𝗿𝗲𝗱𝘂𝗰𝗲𝗿—𝗶𝘁’𝘀 𝗮 𝗰𝗮𝗽𝗮𝗯𝗶𝗹𝗶𝘁𝘆 𝗺𝘂𝗹𝘁𝗶𝗽𝗹𝗶𝗲𝗿.   It moves the constraint from compute cycles to the Human Intent Layer, where talent, judgement, and abstraction become the new premium.   Fresh labor signals back this up: 🔹450,000+ US tech openings (CompTIA) 🔹AI-related job postings nearly doubled YoY 🔹50%+ wage premium for AI-fluency (PwC) 🔹Revenue per employee rising 3x faster in AI-driven sectors 🔹12%+ of tech job ads now reference AI—and climbing (Federal Reserve Bank of Atlanta)   As I note in the article, we’re not witnessing the end of software engineering—we’re seeing its evolution. Developers are becoming AI trainers, strategic integrators, and adaptive problem-solvers.   𝗖𝗼𝗱𝗲 𝗶𝘀 𝗮 𝗰𝗼𝗺𝗺𝗼𝗱𝗶𝘁𝘆. What matters is how well we frame problems, guide systems, and turn intelligence into outcomes.   Thank you, Joe, for the thoughtful conversation. To other leaders: where do you see this shift heading?   📖 Read the full article linked below.   #AI #FutureOfWork #TechJobs #Leadership

Today's update on the challenges facing #GenAI adoption and enterprise #scaling revisits the brewing crisis and further raises the issue of skills shortage, vendor lock-in, and the elusive #ROI of adopting AI. In my research, I identified a lack of business value as a key impediment and the new skills around #CognitiveCollaboration and "Probabilistic Problem Solving" as the critical skills in the AI age. But the main stories first: --- AI Enterprise Scaling: The ROI Crisis Deepens This week's updates show enterprise AI struggling with systemic issues beyond technology, creating a crisis of confidence and hindering measurable ROI. Update 1: The Visibility Gap Crisis Challenge: Massive underestimation of actual AI usage and costs. Details: According to new research, IT leaders underestimate application usage by 1,600%. This creates a massive blind spot for AI tool engagement, costing companies millions in inefficiencies and making true ROI measurement nearly impossible. Source: https://lnkd.in/gaPqywbP Update 2: The Skills Crisis Reaches a Breaking Point Challenge: Critical talent shortages are derailing transformation timelines. Details: A staggering 87% of organizations now report skills gaps, a problem projected to cost the global economy $5.5 trillion. Despite high demand for AI/ML roles, only 28% of companies have achieved adequate data literacy, stalling deployments. Source: https://lnkd.in/g2GYGngG Update 3: The Vendor Lock-in Epidemic Challenge: Vendor lock-in is actively destroying AI value. Details: AI project abandonment rates have soared to 42%. This is linked to vendor-led pilots that create dependency without proving value, trapping companies in solutions that fail to integrate with core business workflows or deliver on ROI promises. Source: https://lnkd.in/ge85hWT6 Key Takeaway The AI scaling crisis is now more organizational than technical. Success hinges on clear ROI visibility, proactive workforce development, and vendor-independent strategies that prioritize proven business value over technology.  --- Those interested in exploring the Theory of #CognitiveChasms are welcome to check out my work at https://lnkd.in/gjsb4H-7. Cognitive Chasm

In the past two weeks, I’ve met with leadership at NiCE, Amazon Web Services (AWS), Zoom, and 8x8. There’s a pattern - here are some reflections on these conversations. AI is eating the enterprise communications playbook for breakfast. If you're not paying attention, you're already behind. Key assumptions that have guided the industry for decades are rapidly becoming obsolete in the age of AI. Here are SIX critical shifts occurring: ONE: Voice is the New UI. Remember “My voice is my passport” as a security phrase, now it’s my voice is my keyboard. APIs are old school. Enterprise-wide, applications and integrations will be voice-enabled from meetings (with AI scribes) to customer service. The future is frictionless, voice-first interactions and integrations, multilingual, and without code. TWO: Mind Your Data: AI without contextual data is like a kiss without a squeeze. Every data repository is a treasure trove, and new moats protect the repositories instead of business practices. Examples include Microsoft putting up CAPTCHA to access Teams meetings and Slack locking down its customers’ data. The new browser wars are unconcerned about eyeballs. THREE: Workflows are the New Apps. Forget simple automation. We're entering the era of AI-native automation, where AI handles complex workflows that require judgment. An AI that doesn't just listen to a customer call but understands the intent, updates the backend systems, and routes the follow-up autonomously. #GameChanger. FOUR: Bottlenecks Be Gone: The modern workplace has largely been throttled by human bottlenecks, and these bottlenecks will disappear. We see this first with code generation; developing new code is becoming the fastest part of a project. Other bottlenecks are various barriers to decisions, such as data collection and analysis. We are moving from concept to code to scale in days, not months or years. FIVE: Soon This Will Matter: As disruptive and consuming as AI has become, none of this matters, yet! That’s because AI isn’t that useful, yet. We are in the Scantron era again. Scantron bubbles revolutionized paper scoring. A good step, but digitization is what mattered. AI is automating existing workflows. The real stuff comes in the reimagination of work. The first glimpse of this is in agentic AI. Focus on outcomes, not processes. SIX: The Barriers to Entry are Changing: The comms sector has enjoyed numerous barriers to entry over the decades, and most of them are disappearing. AI is simultaneously commoditizing and enabling competitive advantage. Giants may or may not fall, but their businesses will radically change. The Giants Cometh. #AI #FutureOfWork #EnterpriseCommunications #VoiceAI #Automation #Tech  #UCaaS #CCaaS Tanya (Blackburn) Shuckhart John Sun Megan Donaldson Schevone Johnson

Top 5 AI Trends to Watch in 2024 As 2024 unfolds, the AI landscape is poised for pivotal advancements. Here are my top 5 predictions, blending various industry insights: The Emergence of AI-Generated Video and Multimodal AI Models: This year, expect AI video models to mature into sophisticated products, paralleling the evolution of text and image models. Additionally, multimodal AI models capable of processing text, images, audio, and video will become more intuitive and commonplace, led by advancements in models like GPT-4. AI Expertise Becomes a Core Competency in the Job Market: With AI's expanding influence, having AI skills will be as essential as traditional tools like Excel. Morgan Stanley predicts AI's impact on 40% of the workforce, and employers are increasingly seeking AI-qualified talent. Advancements in AI for Mobile Technology and Specialized Models: 2024 will witness significant progress in AI applications for smartphones and the rise of smaller, more specialized AI models like Microsoft's PHI-2. These developments will make AI more versatile and accessible across various industries. Open Source AI Gains Prominence Over Closed Models: Open source AI is set to surpass closed models in popularity and innovation. Companies like Meta and Huggingface are leading this shift, democratizing access to AI technology. Data Quality Takes Center Stage: As concerns around copyright and training materials increase, the focus on the quality of data used to train AI models will become more crucial than ever. Ensuring high-quality, ethically sourced data will be a key consideration in AI development. As we step into 2024, let's look forward to a year rich in AI-driven innovation and transformation. Happy New Year, and here’s to a trailblazing year ahead in the world of AI!

Thought provoking and great conversation between Aravind Srinivas (Founder, Perplexity) and Ali Ghodsi (CEO, Databricks) today Perplexity Business Fellowship session sometime back offering deep insights into the practical realities and challenges of AI adoption in enterprises. TL;DR: 1. Reliability is crucial but challenging: Enterprises demand consistent, predictable results. Despite impressive model advancements, ensuring reliable outcomes at scale remains a significant hurdle. 2. Semantic ambiguity in enterprise Data: Ali pointed out that understanding enterprise data—often riddled with ambiguous terms (C meaning calcutta or california etc.)—is a substantial ongoing challenge, necessitating extensive human oversight to resolve. 3. Synthetic data & customized benchmarks: Given limited proprietary data, using synthetic data generation and custom benchmarks to enhance AI reliability is key. Yet, creating these benchmarks accurately remains complex and resource-intensive. 4. Strategic AI limitations: Ali expressed skepticism about AI’s current capability to automate high-level strategic tasks like CEO decision-making due to their complexity and nuanced human judgment required. 5. Incremental productivity, not fundamental transformation: AI significantly enhances productivity in straightforward tasks (HR, sales, finance) but struggles to transform complex, collaborative activities such as aligning product strategies and managing roadmap priorities. 6. Model fatigue and inference-time compute: Despite rapid model improvements, Ali highlighted the phenomenon of "model fatigue," where incremental model updates are becoming less impactful in perception, despite real underlying progress. 7. Human-centric coordination still essential: Even at Databricks, AI hasn’t yet addressed core challenges around human collaboration, politics, and organizational alignment. Human intuition, consensus-building, and negotiation remain central. Overall the key challenges for enterprises as highlighted by Ali are: - Quality and reliability of data - Evals- yardsticks where we can determine the system is working well. We still need best evals. - Extreme high quality data is a challenge (in that domain for that specific use case)- Synthetic data + evals are key. The path forward with AI is filled with potential—but clearly, it's still a journey with many practical challenges to navigate.