Why separation of concern is vital for ML/AI outcomes

Day 03 : Data!! 📊 AI is only as good as the data it learns from. Real world data reflects the underlying processes that generated it. In practice data may be inconsistent as it moves across subsystems and processes, may be incomplete, and in the age of big data may in unstructured (scanned images/PDF/Audio files). In addition, real world data is fragmented across data silos. To unlock value from incomplete, inconsistent and fragmented data investment in foundational data practices is critical. 🔹 1. Data Governance: Setting the Rules of the Game by defining Ownership and decisions rights, Standards to drive consistency, and Permissible use cases. 👉 Strong Data Governance builds trust, transparency and forms ethical baseline for AI applications 🔹 2. Data Curation: The Art and Craft of moving from Raw to Refined data which involves Cleaning (pre and post processing), tagging/enrichment (adding metadata) so data is searchable and contextual, historical alignment. 👉 Curated data is what turns datasets into decision assets 🔹 3. Automated Data Pipelines: Horizontal and vertical scaling flows moving from Manual ETL (Extract-Transform-Load) to Automated operations, real-time ingestion and data streams. Automated anomaly detection, validation and monitoring. 👉 Automated pipelines allow data and ideas from POC to industrial grade solutions #AI #Finance #DataEngineering #DataGovernance #Analytics #Automation #ScalingAI

After spending over a decade in the data industry, consulting with C-level executives and being part of multiple architecture boards across banks, telcos, and global enterprises, I’ve noticed something fascinating — and slightly frustrating. No matter how far we’ve come — - From data lakes to lakehouses - From BI dashboards to Gen AI copilots - From ETL pipelines to Agentic AI and RAG …the underlying problems haven’t really changed. We’re still fighting the same battles around data quality, trust, and alignment between business and tech. Even as we talk about Cognitive Agents, A2A orchestration, and self-healing data pipelines, the truth is: …All of it collapses if your data isn’t reliable. So why do these issues keep resurfacing — even after 10+ years of “modernization”? 1. Organizational Incentives Are Misaligned: Most data programs are measured by delivery, not trust. Engineering teams are rewarded for speed, not accuracy. Business teams care about outcomes, not lineage. The result? Quality becomes everyone’s responsibility — and no one’s priority. 2. Tooling Evolves Faster Than Culture: We keep reinventing the stack — Databricks today, Snowflake tomorrow, Agentic AI next year — but the mindset around ownership, validation, and accountability hasn’t evolved at the same pace. Tech can’t fix what people and process don’t reinforce. 3. Context Gets Lost in Translation: Data moves faster than understanding. Every handoff — from source systems to pipelines to dashboards — strips away business context. By the time the AI agent or model consumes it, it’s technically perfect but semantically meaningless. ⸻ My takeaway: Before building the next “AI-powered data assistant,” maybe we need a data assistant that can explain our data quality issues back to us — in plain English. Because after a decade of shiny tools and buzzwords, data quality remains the quiet bottleneck behind every AI promise. ⸻ Curious — what’s the one recurring data challenge you’ve seen that just won’t go away?

🚀 “AI-Ready” Data? Last week, I had some brilliant, thought-provoking conversations with colleagues about our AI ambitions for 2026 and beyond. The ideas were inspiring and will deliver real value for our customers and colleagues — but one question stuck with me: How do we know if our data is ready for these use cases? It quickly became clear through asking these questions that there are different interpretations — and even gaps — around what “AI-ready data” really means. For me, a few fundamental controls should be implemented to ensure data is trusted to be used to train and feed AI models: 🧑🦳 Ownership & Stewardship — Every dataset needs clear accountability, with SMEs that know the data inside out that can look after it. 🕐 Currency & Maintenance — Data must be refreshed and managed against agreed SLAs, ensuring AI models are using up to date business information. 📚 Context — Link data (metadata) to a business glossary so your AI model understands more about what it represents. ✅ Quality — Measure it, track it, and make it transparent to consumers as a control. ⛓️💥 Lineage — Know where it came from and how it’s evolved from ingestion to insight, quickly assess impact of changes to data sources and transformations. 🥇 Trust Indicators — Combine these elements into a trust score or data "kite mark" so users can instantly see if your data is certified for AI consumption. Publish these in your Data & AI Marketplace (label like a PEGI rating). These are precursors to achieving the foundations of AI governance: good data in, better outcomes out! 🔍 I’d love to hear from others — what would you add, remove, or redefine when it comes to truly AI-ready data? How are you measuring data readiness for AI?

Data Lineage Without the Heavy Tools   When a reference table changes, the first question every analyst asks is: “What will break downstream?”   Traditionally, answering that means waiting for enterprise lineage tools — or manually untangling dependencies across dozens of tables. Both slow, both frustrating.  This month I tested a different approach: using Gemini AI to create a fast, analyst-friendly lineage map.   Example (fictional demo data)   I provided Gemini with three table schemas: transactions (tx_id, customer_id, country_code, amount)  customers (customer_id, home_country_code)  ref_country (code, country_name, region)  Gemini Output (summary)   transactions.country_code → depends on ref_country.code.  transactions.customer_id → depends on customers.customer_id.  customers.home_country_code → depends on ref_country.code.  Impact note: Any change to ref_country.code affects both transactions and customers.  Why This Matters Faster impact analysis → analysts can spot dependencies in minutes.  Less bottlenecked → you don’t wait weeks for central lineage tools.  More proactive → governance boards see risks before controls break.  Takeaway: AI doesn’t replace enterprise lineage tooling, but it gives analysts a first-pass map to navigate dependencies. That agility means fewer surprises when reference tables shift.   Question for you: If you could map lineage in 10 minutes, what would you use it for — audits, forecasting, or change management?   #DataQuality #AI #Governance #Lineage #Gemini #Analytics 

What’s really pushing business forward in big data + #AI right now? We will be at Big Data LDN 2025, bringing the heat from Duamentes, sharing recent reports, key trends, and meet experts sparking the discussions that matter. Here are #Trends & Hot Topics at Big Data LDN to not miss: 1️⃣Generative AI, AI Agents & AI Apps: how multi-agent systems, copilots, app intelligence are transforming business ops. Monte Carlo Data 2️⃣ Data & AI Governance: dealing with compliance, security, privacy, risk; “doing AI right” more than just fast. 3️⃣Data Strategy & Modern Architecture: aligning data strategy with business strategy; building modern data foundations, architectures that support scale, streaming, observability. 4️⃣Data Products / Fabric: creating data products, using fabric-style frameworks; making data usable beyond internal pipelines, more product-centric thinking. 5️⃣Analytics, Visualisation & Storytelling: insight extraction + communicating that insight so that stakeholders act. 6️⃣DataOps & Observability: tracking data health, lineage, monitoring pipelines, incident detection etc. 7️⃣Decision Automation & Streaming Analytics: real-time data, live decisions, automating flows rather than just periodic analytics. 8️⃣ Data For Good / Ethical / Sustainable Data Use: using data to drive social and environmental good; ethics and responsible AI are rising in importance. Let’s connect at the event, reach out to Aleksandr Gontsow we’d love to exchange insights and explore how data-driven research can shape smarter business decisions. #BigDataLDN #BigData #AI #DataScience #DataStrategy #BusinessGrowth #DataDriven #DecisionIntelligence #AIforBusiness #Innovation #CustomerInsights #DigitalTransformation

The data maturity gap: How 2025 will separate winners from losers. The data maturity gap: How 2025 will separate winners from losers. While everyone’s distracted by AI hype, the real battle is happening underground. It’s not about who has the flashiest models. It’s about who has the **strongest data foundation**. Here’s what separates the winners from the losers: **1. Trustworthy Data** Losers: Data chaos. Inconsistent, unreliable sources. Winners: Clear data contracts. Trusted, validated, ready-to-use. **2. Speed to Insight** Losers: Weeks spent cleaning, validating, reconciling. Winners: Pre-defined schemas. Instant access. Decisions in minutes. **3. Scalable Governance** Losers: Ad-hoc rules. Compliance nightmares. Winners: Automated governance. Built-in quality checks. Effortless scaling. **4. AI Readiness** Losers: Garbage in, garbage out. Failed AI projects. Winners: Clean data pipelines. High-quality inputs. AI that actually works. **5. Cultural Alignment** Losers: Data as an IT problem. Winners: Data as a company-wide asset. Everyone speaks the same language. The gap is widening **right now**. Companies investing in data maturity are pulling ahead. Those ignoring it are falling behind. 2025 won’t be kind to those who wait. **Are you building a foundation or digging a grave?** **Like this? Follow for more data leadership insights.** **Share to warn your network about the coming divide.** #DataStrategy #DataMaturity #DigitalTransformation #AI #BusinessIntelligence #TechLeadership #DataDriven

With over a decade in the data space, I’ve seen the evolution firsthand from ETL scripts and warehouses to AI-driven pipelines and governed data ecosystems. But one truth has stayed constant: data decides the direction, not just the decision. 💡 What’s changing in 2025 isn’t the amount of data it’s how intelligently we use, govern, and scale it. 🔹 Quality > Quantity: Reliable, contextual data fuels every trusted insight. 🔹 Observability: Detecting drifts and anomalies in real time is no longer optional. 🔹 Data as a Product: Teams that treat data like a deliverable documented, discoverable, and dependable are the ones driving transformation. 🔹 AI-Ready Foundations: Machine learning success starts with strong data infrastructure. After 10 years in this journey, I’ve realized technology changes fast, but the discipline of data excellence will always define the future of analytics and AI. #DataEngineering #DataStrategy #DataGovernance #DataQuality #MLOps #Analytics #AI

Once upon a career lifetime ago, I participated in a complex project to build a predictive analytics and clustering enterprise application aimed at improving data-driven decision making for a large multinational firm. The solution was intricate, built on the InfoSphere suite to leverage data from a diverse ecosystem, providing predictive recommendations using algorithms like K-Means and Kohonen on what was one of the largest stores of transactional data at the time. It was very complex and cool stuff back then and I was fortunate to partake. The project was both challenging and rewarding, providing an appreciation for the rigor required for robust machine learning practices to enable value delivery. This included framing the problem statement, preparing the data, model training, and applying classification metrics. There were no shortcuts, and each step required measured discipline. This discipline still matters today. Indeed, knowing what is in the black box may matter even more than ever. This is why I think that Info-Tech Research Group’s latest playbook, ‘Assess Your Data Science and Machine Learning Capabilities,’ is an important publication. We need to institutionalize data science and machine learning as a continual learning discipline and understand what is within the black box. This research material offers a structured framework for assessing these capabilities and determining a target state maturity level, along with practical activities to achieve that target state. While the AI investment bubble may burst, establishing a strong data science and machine learning discipline will position your organization for the long game, including driving sustainable return on investment. And explaining K-Means and Kohonen clustering models is still really cool. Link here: https://lnkd.in/gstTYBMv

From raw data to real-time predictions, this is the seemingly forgotten truth behind the machine learning model successfully launched in production… The Machine Learning Lifecycle represents a continuous feedback driven ecosystem where every stage fuels the next. Each phase, from data collection to model monitoring, forms a loop of constant improvement. This ensures that models perform well at launch and continue to learn and adapt as new data flows in. Here’s how the architecture works. Data scientists, ML engineers, and AI engineers will find themselves spending time more or less within the different stages listed👇: 1.🔹Process Data: The journey begins with data collection and preprocessing. Data is cleaned, transformed, and engineered into features that become the foundation of every model. 2.🔹Develop Model: With prepared data in place, models are trained, tuned, and evaluated for accuracy and efficiency before being registered for deployment. 3.🔹Store Features: Features are stored in Online and Offline Feature Stores to enable consistent access for real time and batch inference. This ensures reliable data availability for both deployment and retraining. 4.🔹Deploy: Models are deployed through automated pipelines and integrated into production environments where they power intelligent applications and perform inference in real time. 5.🔹Monitor: Continuous monitoring tracks performance, detects drift, and triggers retraining workflows when accuracy drops. 6.🔹Feedback Loops: Performance and Active Learning feedback loops keep models updated with new insights and data, ensuring continuous evolution. 💡 In essence: A strong ML lifecycle should be cyclical. Data fuels models. Models power applications. Applications generate new data and the loop continues. 🧠 Building such an architecture enables scalability, adaptability, and governance across the entire machine learning ecosystem, but it doesn’t come without challenges. What obstacles have you encountered in your patch? How have surmounted them? #MachineLearning