The AI Data Integration Gap: Plumbing vs Orchestration

Quick question. If I asked you “what’s our churn by customer segment in the last 90 days,” could you answer it right now? As data engineers, we take some things as pretty obvious… but they’re often not obvious to product, marketing, or even stakeholders. Let’s say you’ve already built your product, everything is up and running, you’re growing month over month, MRR is coming in. Now you’re asking: how do we actually do something with our data? Let’s break it down: - First, you set up a data warehouse that collects data from all sources, cleans, organizes, stores it in a format that’s always usable, and most important, adds context to your data. This is the foundation. - AI + LLM Integration, here’s where things get interesting. By integrating an AI orchestration layer with an LLM, you move beyond dashboards. Stakeholders can ask: “What’s our churn in the last 90 days by customer segment?” in plain English and get an accurate, contextual answer. - Once feedback loops are in place, predictions about customer behaviour can be tested and refined automatically as new data flows in. Your system is learning, not just reporting anymore. At this point, data is no longer just an archive of what already happened. It becomes a tool for deciding what to do next. And while steps like setting up ETL pipelines or modeling data might feel obvious to us engineers, for many teams this shift is transformative. That’s why we love building data platforms.

Three industry leaders are converging on Zero Copy architectures and open standards to deliver immediate, contextual, and governed data for AI use cases that unlock new levels of business value and responsibility.

Introducing the 11-Step GenAI Execution Framework for Domain-Led Pilots in Data Mesh Environments Designed to break pilot purgatory and scale GenAI inside real enterprise constraints.   Most GenAI programs don’t fail because of bad models. They fail because the architecture is wrong. 1.    Central AI teams that don’t own the data. 2.    Data lakes that delay every iteration. 3.    Governance bottlenecks that show up after the pilot has shipped. We have spent the last 4 months reverse-engineering: ·     Why 70%+ of GenAI pilots never reach production (Gartner, McKinsey) ·     Why most enterprise LLM strategies default to central teams that don’t own the data ·     And why domain teams are forced to sit on the sidelines, despite having the context that makes or breaks model performance   What emerged is not just another canvas.
 It’s a battle-tested, domain-first, architecture-aware, 11-step execution framework for fine-tuning LLMs inside domain-owned data products - governed, scalable, and fully Mesh-aligned. What It Fixes 1.    No more “pilot purgatory” - the framework enables 8–10 week, ROI-visible pilots
 2.    No more centralization bottlenecks - data stays in the domain, masked + governed
 3.    No more disconnected models - domain teams co-own prompts, adapters, and evaluation 4.    No more infra waste - QLoRA + token budgets ensure controlled, scalable experimentation
 5.    No compliance blind spots - bias scans, drift detection, PII controls are built-in from day one   Where It’s Gaining Traction This framework is shaped by: ·     Real-world pilots across banking, telecom, healthcare, manufacturing, and retail ·     Architecture teams trying to fine-tune LLMs using governed, domain-owned data ·     Initiatives focused on use cases like complaint summarization, field service logs, vendor audits, and discharge instructions   These efforts share one thing:
They’re proving GenAI value without centralizing data or violating Mesh principles.   Major enterprises, including JPMorgan, Mayo Clinic, Carrefour, BNP Paribas, Siemens Energy, and Verizon have publicly demonstrated elements of this approach in their GenAI journeys, whether through domain-based pilots, decentralized governance, or token-efficient fine-tuning.   This framework distils those learnings, and structures them for repeatable delivery.   For Executives, This Means: ·     Faster time to production without shadow IT ·     Trusted AI deployment without re-platforming ·     Domain-aligned ROI that doesn’t wait for architecture redesign   We didn’t build this framework in a workshop.
 We built it in the trenches - with real architecture teams, real governance constraints, and real domain owners who needed results.   If your GenAI program needs a reset, this is the blueprint to start with. Transform Partner – Your Strategic Champion for Digital Transformation

🚨 The role of the data professional is changing—fast. At Data Futures, we’re seeing two major forces reshaping the field: 1️⃣ The convergence of software and data engineering 2️⃣ The rise (and rise) of Generative AI Data is no longer just for dashboards, it’s now mission critical, powering real-time decisions, products, and experiences. And GenAI is accelerating the need for better engineering, automation, and delivery practices. What does this mean for the future of data teams? We break it down in our latest Medium post 👉 https://lnkd.in/dn_tWey2 Stay tuned for Part 2. #DataEngineering #GenAI #DigitalTransformation #SoftwareEngineering #DataFutures

🔎 The semantic layer is more than theory—it's the key to making your AI and analytics efforts actually work at scale: https://bit.ly/3Gdm3lb In this on-demand session, discover how to: 🔷 Align architecture with business outcomes 🔷 Enable governed, AI-ready data access 🔷 Simplify your enterprise data stack CData Software #CData #SemanticLayer #DataVirtualization #AI #DataOps #DataGovernance #OnDemand

The race to AI readiness is making us rethink data engineering and metadata management as we've come to know them in analytics. Trust is taking on a whole new meaning. Over the past few years, data contracts have been hailed as the answer to messy pipelines and misaligned teams. But in practice, most of what I see in enterprises is stale YAML files, schema definitions that drift from reality, and contracts that get treated like documentation instead of enforceable agreements. In my latest blog, I share why the concept of data contracts isn’t the problem; it’s the execution. Static approaches can’t keep up with the dynamic, AI-driven systems we’re building today. The way forward isn’t to abandon contracts, but to rethink them: 🔹 Make them live instead of static 🔹 Tie them to business logic, not just schemas 🔹 Ensure they are enforced and trusted at runtime Full blog post linked in the comments. #dataobservability #metadataactivation #dataquality #aiinfrastructure #dataengineering

🚨 Data architecture has a problem. Data Lakes → became dump yards. Warehouses → rigid & expensive. Data Mesh → great in theory, but demands cultural change few orgs are ready for. Data Fabric → automates a lot, but still metadata-heavy. 👉 The result? Costly, fragmented, hard-to-govern ecosystems. 🔥 The next evolution: AI-Native Data Architecture 1️⃣ Hybrid Lakehouse → unified foundation. 2️⃣ AI-Driven Fabric → auto-discovery, lineage, quality, policy enforcement. 3️⃣ Cognitive Layer → AI agents that build products, heal pipelines, and optimize continuously. 4️⃣ Conversational Access → NL → SQL, semantic search, governance through chat. 💡 The shift: from manual wrangling → to AI-assisted, self-optimizing platforms. Not about replacing humans — it’s about freeing them to create value instead of firefighting. 🚀 The journey: Data Lake → Lakehouse → AI Fabric → Cognitive Platform. Do you see AI as the missing piece to fix data architecture — or just another layer of complexity? #DataArchitecture #Lakehouse #DataFabric #DataMesh #DataGovernance #AI #AIDriven #CognitiveAI #FutureOfData #DigitalTransformation

👍 Your data structures are holding you back. While AI is building ones that evolve in real-time. 😊 Most developers are using data structures designed decades ago. Static arrays. Fixed hash tables. Rigid trees. They force your data into boxes instead of adapting to its natural shape. I’ve been testing AI-generated self-optimizing data structures. The difference isn’t incremental—it’s transformational. Here’s what changes: 1. **Morphing Memory Layouts**: These structures analyze access patterns and physically reorganize data in memory. Hot data moves to faster locations. Cold data gets compressed dynamically. No manual indexing. No query optimizations. It happens autonomously. 2. **Predictive Allocation**: Instead of pre-allocating fixed sizes, they forecast growth patterns and adjust capacity before bottlenecks occur. Say goodbye to out-of-memory errors and wasted reserved space. 3. **Context-Aware Organization**: They understand semantic relationships between data points. Related information clusters together geographically in storage—reducing latency by 60-80% on complex queries. The results? Systems using adaptive structures show 40-90% performance boosts without code changes. Database maintenance time drops by 70%. Storage requirements often shrink by 50% through intelligent compression. This isn’t just better data structures. It’s **alive** data structures. They learn from every operation. They improve with use. They turn storage from a passive container into an active partner. Your move: Audit one critical data structure in your system this week. Notice how many manual optimizations it requires. The future isn’t better algorithms. It’s algorithms that improve themselves. What’s the most outdated data structure you’re still using?

❄️ Don't miss Snowflake's Field CTO's presentation at AI HotSpot 2025 AI will transform your business. But that's not possible without high-quality data. 📊 Pedro Jose, Field CTO at Snowflake, will explain why successful AI always starts with a solid, scalable, and well-governed data strategy. No model can solve problems on its own — only data with the right quality, context, and architecture can. In his talk, you’ll hear: 🔹 How to break down data silos to gain real-time customer intelligence 🔹 How governed data sharing accelerates multi-team collaboration on LLM projects 🔹 How to build secure, scalable, model-ready pipelines from operational data 🔹 Why GenAI can only rely on trusted, high-quality enterprise data Drawing on real-world stories, he’ll show that no AI tooling can compensate for poor data architecture. 📍 16 October | DOX Prague | AI HotSpot 2025 👉 Registration: https://aihotspot.ai/