Tips for Building a Robust Data Foundation

When leaders brainstorm over trackers instead of architectures 😅 If only pipelines ran as smoothly as the meetings about how to track them.. As funny as it sounds, this happens way too often in data teams — hours spent debating Jira structures, story points, epics, and subtasks… Meanwhile a pipeline is quietly failing in production. But behind the humor lies an important reminder: → 𝐺𝑟𝑒𝑎𝑡 𝑑𝑎𝑡𝑎 𝑒𝑛𝑔𝑖𝑛𝑒𝑒𝑟𝑖𝑛𝑔 𝑖𝑠𝑛'𝑡 𝑎𝑏𝑜𝑢𝑡 𝑝𝑒𝑟𝑓𝑒𝑐𝑡 𝑡𝑟𝑎𝑐𝑘𝑒𝑟𝑠—𝑖𝑡'𝑠 𝑎𝑏𝑜𝑢𝑡 𝑡ℎ𝑒 𝑟𝑖𝑔ℎ𝑡 𝑡ℎ𝑖𝑛𝑘𝑖𝑛𝑔. Over the years, one pattern stands out: Teams that obsess over tools often under-invest in architecture. And teams that anchor on architecture naturally simplify everything else—tooling, tracking, delivery. Sharing few learnings that have made difference in my data engineering journey to build robust data systems: 1. Think in systems, not tasks Before assigning story points, ask: → What domain does this belong to? → What data contracts govern it? → Is this transformation even necessary? Clear system thinking > endless subtasks. 2. Architecture over trackers A well-defined: → Data model → Lineage flow → Orchestration pattern → and error strategy removes 80% of ticket back-and-forth. Your Jira gets simpler because your architecture is clearer 3. Invest in observability early Strong quality checks, lineage, and alerts mean: → Faster debugging → Better collaboration → No 2 AM firefighting Observability is invisible until you desperately need it. 4. Document why, not just what Trackers show what you did. Architecture docs explain why. Future you will thank present you. 5. Reduce cognitive load → Simplified schemas. → Modular pipelines. → Automated steps. Less time deciphering = less time debating story points. Maturity isn't measured by tracker maintenance — it's measured by systems that don't require constant firefighting. Here's what separates good data engineers from great ones- → Ask "what breaks if this fails?" before writing code → Think in layers, not monoliths → Build systems their junior teammates can debug → Optimize for the team inheriting their work, not just shipping fast → Know when NOT to over-engineer, Right-sizing matters more than resume-driven development → Understand that 99% vs 99.9% uptime isn't a rounding error—it's millions in cost 👉 Remember: Your Jira board doesn't run your pipelines. Your architecture does. Spend your energy accordingly. 𝗕𝘂𝗶𝗹𝗱 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝘁𝗵𝗮𝘁 𝘀𝗰𝗮𝗹𝗲𝘀, 𝗻𝗼𝘁 𝗲𝗻𝗱𝗹𝗲𝘀𝘀 𝗺𝗲𝗲𝘁𝗶𝗻𝗴 𝘁𝗮𝗹𝗲𝘀.

After building 10+ data warehouses over 10 years, I can teach you how to keep yours clean in 5 minutes. Most companies have messy data warehouses that nobody wants to use. Here's how to fix that: 1. Understand the business first Know how your company makes money • Meet with business stakeholders regularly • Map out business entities and interactions  • Document critical company KPIs and metrics This creates your foundation for everything else. 2. Design proper data models Use dimensional modeling with facts and dimensions • Create dim_noun tables for business entities • Build fct_verb tables for business interactions • Store data at lowest possible granularity Good modeling makes queries simple and fast. 3. Validate input data quality Check five data verticals before processing • Monitor data freshness and consistency • Validate data types and constraints • Track size and metric variance Never process garbage data no matter the pressure. 4. Define single source of truth Create one place for metrics and data • Define all metrics in data mart layer • Ensure stakeholders use SOT data only • Track data lineage and usage patterns This eliminates "the numbers don't match" conversations. 5. Keep stakeholders informed Communication drives warehouse adoption and resources • Document clear need and pain points • Demo benefits with before/after comparisons • Set realistic expectations with buffer time • Evangelize wins with leadership regularly No buy-in means no resources for improvement. 6. Watch for organizational red flags Some problems you can't solve with better code • Leadership doesn't value data initiatives • Constant reorganizations disrupt long-term projects • Misaligned teams with competing objectives • No dedicated data team support Sometimes the solution is finding a better company. 7. Focus on progressive transformation Use bronze/silver/gold layer architecture • Validate data before transformation begins • Transform data step by step • Create clean marts for consumption This approach makes debugging and maintenance easier. 8. Make data accessible Build one big tables for stakeholders • Join facts and dimensions appropriately • Aggregate to required business granularity • Calculate metrics in one consistent place Users prefer simple tables over complex joins. Share this with your network if it helps you build better data warehouses. How do you handle data warehouse maintenance? Share your approach in the comments below. ----- Follow me for more actionable content. #DataEngineering #DataWarehouse #DataQuality #DataModeling  #DataGovernance #Analytics

Let’s zoom out for a moment—across every era of tech innovation, from the database boom to today’s LLM gold rush, organizations keep bumping into the same core challenge: breakthrough AI becomes obsolete fast if data foundations aren’t actively maintained and reimagined. It’s easy to get swept up by flashy new models, but lasting competitive edge comes from meticulous care of what lies beneath—data quality, evaluation cycles, and the quiet craft of architectural evolution. The 18-lever approach reframes data architecture, shifting the focus from static plans to dynamic, resilient ecosystems. Raj Grover illustrates exactly how enterprises can move from ad hoc pipelines to robust, continuous practices—think automatic deduplication, self-updating schemas, persistent anomaly detection, and embedded evaluation loops that let platforms keep pace with ever-shifting data. Here’s the strategic bottom line: organizations that treat data curation as a living, ongoing discipline—not a one-off project—slash technical debt and protect themselves from both headline-grabbing and subtle risks (think slow model drift, not just major outages). Consider the market playbook: just like high-frequency trading platforms built their edge by mastering every step of the data lifecycle—not just speed—modern enterprise AI leaders are wiring evaluation and risk monitoring directly into their core digital systems. Staying “AI current” now means viewing architecture discovery as proactive horizon-scanning: your tech infrastructure isn’t just plumbing, it’s an early-warning radar for regulatory, ethical, and market changes. To really make this work, enterprises have to tear down the wall between the models and the data systems: twist data architects and business owners together, and surface evaluation results, risk logs, and metrics at the P&L level—not just in engineering meetings. * Technical insight: Continuous metadata cataloguing and anomaly detection catch drift before it impacts models, slashing data downtime. * Business impact perspective: Enhanced data observability speeds up incident response and patch fixes, cutting downstream costs by up to 25%. * Competitive advantage angle: By treating data and evaluation as institutional priorities, companies prove their maturity to partners, regulators, and clients—outpacing organizations that see architecture as a mysterious black box. Action Byte: Assign “data stewards” to every core product team, owning data lineage, anomaly surfacing, and incident reviews. Roll out open-source cataloguing and monitoring tools within 90 days to target a 40% drop in data-related downtime. Run monthly, cross-team “drift drills”—simulate emerging data quality issues, review team responses, and continually refine your playbooks. Make these learnings visible to the exec team, not just the tech leads. This will keep your AI architecture alive and evolving.

Everyone celebrates the AI skyline. Almost no one wants to invest in the foundation. That foundation is data governance. Not as a policy exercise, but as an operating discipline. When governance is weak, AI looks impressive at first: fast demos clever outputs early wins Then reality shows up: inconsistent answers hidden bias teams arguing over whose data is “right” leaders quietly losing trust in the system That’s not an AI failure. It’s a foundation failure. Here’s the practical playbook I’ve helped organizations use to fix it: 1) Assign real ownership, not committees Every critical data domain needs a clear owner with actual decision rights. If no one owns the data, the model ends up guessing. → Leader question: Who is accountable when this data misleads a decision? 2) Define “good data” in business terms Quality only matters in context. Accuracy, timeliness, and completeness must be tied to how the data is used, not how it’s stored. → Leader question: What decision breaks if this data is wrong or late? 3) Design guardrails before scale Not every dataset should feed every model. Governance is about boundaries: what AI can see, what it can influence, what it can automate. → Leader question: Where must humans stay in the loop, no matter how good the model gets? 4) Treat data pipelines like production systems Monitoring, lineage, versioning, and rollback aren’t optional. If you can’t trace an output back to its source, you can’t trust it. → Leader question: Could we explain this answer six months from now? 5) Build governance where work actually happens Policies on slides don’t scale. Embedded checks in workflows do. → Leader question: Is governance preventing rework later, or just slowing teams down today? AI doesn’t fail because it’s too advanced. It fails because the groundwork was never finished. If you want a skyline that lasts, build where no one is looking. 📌 Save this if AI reliability is now a leadership issue 🔁 Repost to shift the conversation from demos to durability 👤 Follow Gabriel Millien for grounded insight on Enterprise AI and transformation

To build a solid Data Foundation for AI Transformation, enterprises must ensure that data is not only available, but trusted, well-governed, and ready for intelligent use. A strong data foundation bridges the gap between business goals and AI model performance. Below are the main components: 🔷 1. Data Strategy & Governance - Data Ownership & Stewardship: Clear roles for who owns, curates, and validates data. - Data Policies: Governance policies for access, usage, privacy, and compliance (e.g. GDPR, HIPAA). - Master & Reference Data Management: Ensure consistency of critical data entities across systems. 🔷 2. Data Quality & Trust - Data Profiling & Cleansing: Remove duplicates, fix inconsistencies, fill gaps. - Validation Rules & Anomaly Detection: Detect data drift or broken pipelines early. - Lineage & Provenance: Know where data comes from and how it has changed. 🔷 3. Data Architecture & Infrastructure - Modern Data Platforms: Data lakes, warehouses, lakehouses, or vector databases. - Real-Time vs Batch Processing: Support both operational and analytical workloads. - Data Integration & APIs: ETL/ELT pipelines, connectors, and API-based data access. 🔷 4. Security, Privacy & Compliance - Data De-identification & Masking: Protect PII while preserving utility. - Role-Based Access Control (RBAC): Ensure only the right users/systems can access the right data. - Audit Trails & Monitoring: Track who accessed what, when, and why. 🔷 5. AI-Ready Data Practices - Labeling & Annotation Workflows: For supervised learning and fine-tuning. - Feature Stores & Embeddings: Reusable, standardized inputs for ML/AI models. - RAG-Enabling Structures: Chunked, semantically enriched documents for Retrieval-Augmented Generation. 🔷 6. DataOps & Automation - CI/CD for Data Pipelines: Automate testing and deployment of data workflows. - Metadata Management & Catalogs: Enable discovery and governance at scale. - Monitoring & Alerting: Real-time health checks on data pipelines and quality metrics. 🔧 Personal Tip: Build Talent Across Data and Infrastructure One of the most underestimated success factors in AI transformation? A team that understands both the data science and the engineering foundations beneath it. Many organizations invest heavily in AI skills, but neglect the cloud, DevOps, and data infrastructure expertise needed to scale those models in production. To make AI real, you need: - Data engineers who can build resilient, governed pipelines - Platform and cloud architects who can support scalable, secure compute - MLOps specialists who bridge model lifecycle with infrastructure operations 📌 AI doesn't run in notebooks—it runs on architecture. And that architecture has to be designed with security, performance, and cost in mind from day one. #AITransformation #DataEngineering #DataManagement #ArtificalIntelligence

A fraud model reports 92% accuracy in testing. Two weeks later, false positives surge. Customers get blocked. Revenue takes a hit. No one changed the model. So what failed? Not the algorithm. The data flow. Late-arriving records weren’t handled. Duplicates weren’t removed properly. Training logic didn’t match serving logic. In production, models rarely break because of machine learning theory. They break because the underlying data system isn’t designed for reality. After building and reviewing multiple ML systems in production environments, one thing is clear: Strong SQL patterns are what separate demo projects from production-grade AI systems. Here are 14 SQL patterns that actually matter in real-world data science systems: 1. Deduplication using window functions Ensure only the latest or correct record per entity survives noisy event streams. 2. Handling late-arriving data Design logic that updates aggregates when delayed records arrive. 3. Idempotent transformations Make pipelines safe to re-run without corrupting outputs. 4. Feature consistency (training vs serving parity) Use identical logic to generate features across batch and real-time systems. 5. Incremental model feature builds Process only new or changed data instead of recomputing everything. 6. Slowly Changing Dimensions (SCD) Track historical changes in user or entity attributes accurately. 7. Sessionization patterns Group events into logical sessions using time-based rules. 8. Rolling and windowed aggregations Compute features like 7-day averages or 30-day sums efficiently. 9. Event ordering and sequencing Preserve chronological integrity for behavioral modeling. 10. Data validation checks in SQL Catch null spikes, schema drifts, and anomalies early. 11. Outlier filtering and anomaly flags Prevent extreme values from poisoning training data. 12. Partition-aware queries Optimize performance and cost for large-scale datasets. 13. Experiment tracking joins Correctly map users to experiments for clean A/B analysis. 14. Reproducible feature snapshots Store versioned datasets to recreate past model states exactly. Final Thought Models get the spotlight. SQL pipelines carry the weight. If your data foundation is weak, your model will eventually expose it. Build patterns that survive real traffic, messy data, and scale. That’s how production AI systems stay reliable. If this helped, repost and follow Sumit Gupta for more insights!!

All data ultimately has a human source—it is not collected, but created. Data-savvy leaders understand this nuance. Decision infrastructures are often built on the premise that data is objective, definitive, and value-neutral. This leads organizations to treat data as an infallible compass. However, every byte of information springs from human actions, decisions, interactions, goals, and biases. Customer data, for example, doesn't just show behavior but reflects how people navigate interfaces we've designed, within constraints we've established. Even pristine financial data carries the imprint of human judgment—from revenue recognition timing to expense categorization—codified in vast accounting guidelines, but human-made nonetheless. Does this mean data is just subjective figures open to any conclusion? Of course not! It means that for proper understanding and interpretation, data's context is vital. All that metadata and methodology documentation isn't a footnote, but a crucial user's manual. Even the most carefully constructed dataset can be misinterpreted without proper context. This demands a targeted response. Implementing the following five specific structural changes can help address this reality: 1️⃣ Make the documentation of collection methods, decision points, known biases, and limitations a part of your data quality metrics. 2️⃣ For major decisions, require stakeholders to articulate which assumptions the data implicitly reflects and how changes would affect conclusions. 3️⃣ Pair data specialists with subject matter experts who understand the contexts generating the data. Formalize this collaboration for critical insights. 4️⃣ Integrate behavioral variables into risk assessment by testing how human motivations could invalidate data patterns. Create alternate scenarios for more robust strategies. 5️⃣ Establish mechanisms to test data-derived insights against lived experiences, where frontline observations can challenge or validate data-based conclusions. When businesses acknowledge that humans shape every piece of data, they gain insights that others miss and avoid misinterpretations, strategic missteps and compliance failures (like algorithmic bias). Success comes not from making data more human-friendly, but from recognizing data as fundamentally human in the first place.

AI won't fix your bad data. But a solid data foundation will transform your AI... Too many companies rush to implement AI before organizing their data. It's like building a skyscraper on quicksand. No structure. No consistency.  No strategy. This approach leads directly to: • Unreliable insights that mislead decision-makers • Inefficient AI models that waste computing resources • Thousands of dollars spent with minimal return The hard truth: Data is an ingredient. Intelligence is the outcome. You can't cook a gourmet meal with spoiled ingredients. (I haven't tried it but I'm guessing) A strong data roadmap solves these fundamental problems by: → Breaking down organizational silos → Structuring data for optimal use → Creating consistency across systems → Enabling truly intelligent decision-making Companies that invest in data structure will lead the AI revolution. The rest will struggle to keep up, constantly wondering why their AI investments aren't delivering. The difference isn't in the AI tools. It's in the data foundation. Our team at Michigan Software Labs addresses this head-on: 1. Data Discovery - Uncover what data exists and pinpoint any gaps. ~3 weeks. 2. Data Structuring - Organize and refine your data for clarity and quality 3. System Connectivity - Link platforms and tools to break down silos 4. AI Enablement - Apply AI solutions to well-prepared, structured data Stop throwing good money after bad. Start building the foundation your AI initiatives need to thrive. p.s. - If you've been following me for a while but we've never connected directly, I'd love to hear from you. Drop me a comment or send a quick note. Whatever professional challenge you're facing, I'm here to help - and if I can't, I’ll point you to someone who can.

HIMSS 2025: Data+AI and the High Roller Journey Last week I was at HIMSS 2025 in Las Vegas, and after a fantastic Databricks Executive Roundtable, I’m excited to share some insights... Think of navigating Data + AI like riding the High Roller (see photo below) —it’s all about perspective, gradual climbs, and enjoying the view as you reach new heights.  Here are my 7 takeaways: 1. More protein, less sugar: Avoid chasing every "shiny object" (sugar) and focus on building a strong data foundation (protein). Like a solid base for the High Roller, your data infrastructure is crucial for everything that follows. 2. Strategic leadership and team alignment: Consider creating a dedicated CAIO (Chief AI Officer) role, separate from the CDO/CDAO, to drive and oversee your Data + AI initiatives if you need to move faster. Don't hesitate to reorganize your teams if it strengthens your foundation. Building a strong team is essential for scaling your data and AI efforts. 3. Business alignment & comms: It's not always easy to get everyone on board. If business leaders don't appreciate the value of investing in data, it's on you to communicate its importance in business terms. Think of the FedEx tracker - simple, clear, and gives people confidence; even though it may include specific transit locations, those details don't increment knowledge, they infuse credibility. 4. Stakeholder education and engagement: Stakeholders without "data" in their titles almost always have a limited understanding of data-centric primitives. Take the time to build relationships, understand their context, and meet them where they are. 5. Incremental progress: start small & be opportunistic: Avoid starting with overwhelmingly complex use cases, but incrementally add complexity. Prioritize AI use cases where the data is already clean, curated, and governed for quicker wins. These early successes can build momentum and demonstrate value. Don't get too comfortable with low-complexity use cases; increment complexity with each subsequent use case. 6. From projects to processes for scalability: To scale effectively, shift your focus from individual projects to repeatable processes, with the eventual goal of having a data-driven culture. This can take time and will likely require multiple attempts in larger-scale organizations. 7. Data + AI is a journey (no surprise!): Like raising children, Data + AI efforts start with misbehavior and imperfect outcomes. But with enough iteration, they mature into something you can be proud of. Embrace the iterative process and learn from each step - you will be much more impressed with your adult children who have gone through many iterations of learning… and nagging 🙂. Big thanks to Nick Iannoni (Intermountain Health) and Rajiv Synghal (Kaiser Permanente) for generously sharing their learnings on the Data+AI High Roller they’ve been on for many years! Your expertise made the event a huge success.

I am a senior Data engineer at Amazon with 7+ years of experience. If I could sit down with a junior in Data, here are some good pieces of advice I would tell them that my seniors told me. Start simple. 1. Daily batch jobs? A cron scheduler is enough. You don’t need Airflow for everything. Complexity should be earned, not assumed. 2. Own your pipelines like production code. If your data is consumed across teams or feeds real-time products, treat it like software. Use DAGs, define SLAs, and log everything. 3. Tools are easy. Trade-offs are not. Snowflake and BigQuery are great for ad-hoc analysis. But for high-throughput systems, you’ll need serious tuning, caching, partitioning, pruning, the works. 4. Schema changes are dangerous. They don’t just break dashboards, they can break trust. Use contracts. Validate upstream assumptions. Think like a platform owner. 5. Monitoring is not optional. If your pipeline fails once and no one notices, that’s a miss. If it fails and nobody knows why, that’s a disaster. Build observability early. 6. Spark is powerful and unforgiving. You can move terabytes of data or crash your cluster. Learn how shuffles work. Understand partitioning. Tune before you scale. 7. . APIs will fail. Retries, deduping, and idempotency aren’t optional, they’re survival tools. Treat external data like it’s unreliable by default. 8. Data quality depends on context. Reporting pipelines? Focus on cost. Real-time ML systems? Focus on accuracy and latency. Your design goals should match your business impact. No fancy certification can replace this. These are the lessons you only learn by building real systems, breaking them, fixing them, and owning the fallout. You want to stand out? Start by thinking like the person who has to clean up what you ship. — P.S.: If you like this post, you will like my upcoming livestream session with Zach Wilson even more! I’ll be talking about my journey in Data, the lessons I’ve learned, and a few stories I’ve never shared before on the crazy 24-hour livestream that Zach has organized! Date: 23 May Time: 7: AM (PST), and 7.30 PM (IST) Here’s the link. https://lnkd.in/geVUZfh9 Be sure to join in, you don’t want to miss this.