Why Your Data Dictionary Is a Strategic Asset

In today’s AI-driven world, “good data” isn’t just accurate and complete—it’s structured, contextualized, and productized. The leap from raw data to AI-ready insights requires more than pipelines and platforms. It requires #taxonomy, #semantics, and meaning. #Taxonomies give us a common language across business domains. #Semantic models ensure that AI understands not only the data but also the relationships and context behind it. Without this foundation, even the most advanced AI risks producing outputs that are inconsistent, biased, or misaligned with business goals. This is why the future of enterprise #AI belongs to organizations that treat data as a product—designed, governed, and consumed with the same rigor as any engineered solution. Good #data principles—quality, governance, lineage—become powerful when embedded into semantic frameworks that fuel reusability, interoperability, and trust. Throughout my career, I’ve helped enterprises like Northern Trust, Accenture, EY, Microsoft, and IBM translate these principles into practice—building scalable data architectures, integrating semantic industry models (FIBO, BIAN, IBM), and enabling AI-ready ecosystems across finance, healthcare, and technology. ✅ Data Vault 2.0 & semantic modeling for resilient, AI-ready data platforms ✅ Cloud-native modernization (Amazon Web Services (AWS) Microsoft Azure #GCP, Databricks Snowflake ✅ Enterprise data governance with Collibra & Unity Catalog ✅ Enabling Customer360, #MDM, and AI-driven analytics at scale As a Data Architect & Modeler with 25+ years of experience, I bridge the gap between data principles and AI execution—helping organizations turn raw assets into trusted, productized data ecosystems that power the next generation of intelligent applications. If your organization is looking to unlock AI’s full potential through semantic, governed, and productized data, I’d welcome the opportunity to connect and contribute. #opentowork https://lnkd.in/exYsm3kD

Data Engineering is the backbone of modern data and AI. Here are 20 foundational terms every professional should know Part 1: 1️⃣ Data Pipeline: Automates data flow from sources to destinations like warehouses 2️⃣ ETL: Extract, clean, and load data for analysis 3️⃣ Data Lake: Stores raw, unstructured data at scale 4️⃣ Data Warehouse: Optimized for structured data and BI 5️⃣ Data Governance: Ensures data accuracy, security, and compliance 6️⃣ Data Quality: Accuracy, consistency, and reliability of data 7️⃣ Data Cleansing: Fixes errors for trustworthy datasets 8️⃣ Data Modeling: Organizes data into structured formats 9️⃣ Data Integration: Combines data from multiple sources 🔟 Data Orchestration: Automates workflows across pipelines 1️⃣1️⃣ Data Transformation: Prepares data for analysis or integration 1️⃣2️⃣ Real-Time Processing: Analyzes data as it’s generated 1️⃣3️⃣ Batch Processing: Processes data in scheduled chunks 1️⃣4️⃣ Cloud Data Platform: Scalable data storage and analytics in the cloud 1️⃣5️⃣ Data Sharding: Splits databases for better performance 1️⃣6️⃣ Data Partitioning: Divides datasets for parallel processing 1️⃣7️⃣ Data Source: Origin of raw data (APIs, files, etc.) 1️⃣8️⃣ Data Schema: Blueprint for database structure 1️⃣9️⃣ DWA: Automates warehouse creation and management 2️⃣0️⃣ Metadata: Context about data (e.g., types, relationships) Which of these terms do you use most often? Let me know in the comments! Join The Ravit Show Newsletter — https://lnkd.in/dCpqgbSN #data #ai #dataengineering #theravitshow

Every Leader Needs Data Engineering Literacy. Data Engineering is often the “invisible” part of Data & AI projects… until timelines and estimations are challenged. When leaders ask: “Why does it take so long?”, this post is a great indicator/reminder. Because before data becomes actionable, it must first become simply usable. Collecting raw data, ensuring governance, building pipelines, cleansing, modeling… these are not “extras,” they are the foundation. I’ve seen too many projects underestimated because the effort behind making data reliable was overlooked. Understanding these core concepts helps set the right expectations and build trust between C-level, business & data teams. Leaders: if your next project requires building from scratch, take a moment to read this. It will help you better evaluate estimations and see the value in the process. Thank you Ravit Jain for the great document. #DataEngineering #AI #DataStrategy #Leadership #BusinessImpact

The role of the data steward is being redefined in the age of Agentic AI. Back in my Reltio days, I had the opportunity to walk in the shoes of data stewards through a day-in-the-life program, seeing firsthand the workflows involved in onboarding, reviewing, and cleaning data to ensure it was fit for use. In regulated industries, especially, these tasks are critical but also manual, error-prone, and labor-intensive. What was once a human-heavy responsibility, ensuring data quality, managing metadata, overseeing master data, and enforcing retention, can now be amplified through AI agents that act automatically, continuously, and at scale. This CDO Magazine piece highlights how embedding agentic automation into stewardship shifts us from reactive governance to proactive, real-time assurance: 👉 Data quality checks become autonomous 👉 Metadata is enriched and validated as data lands 👉 Retention and lifecycle policies are applied consistently, without waiting on human intervention For me, it signals a broader shift: trust in data can no longer be after-the-fact. It must be built-in, automated, and always-on because that’s what modern AI and business workflows demand. 📖 Worth a read: Digital Data Steward: Leveraging Agentic AI for Data Quality, Metadata, Master Data Management, and Data Retention by Maria C Villar Mike Alvarez and others. https://lnkd.in/g3ckVBth #agenticworkflows #dataobservability #dataquality #datamanagement

Why analytics on unstructured data is hard (and why it matters)!! In the structured world, analytics is straightforward: you load data into a star schema, define dimensions (vendor, time, product), and run queries. In the unstructured world, things get messy fast: -- The same vendor appears as “Acme Corp.”, “ACME Inc.”, “Acme” depending on the document. -- Revenue shows up as “total after tax” in invoices, “contract value” in agreements, or “recognized revenue” in statements. Which one is correct depends on context. -- Addresses change over time .. sometimes you see HQ, sometimes a remit-to, sometimes just a P.O. Box. -- Critical details are trapped in PDFs, emails, or scanned images with inconsistent quality. To make unstructured data analytics-ready, you can’t just point a dashboard at your files. You need to: -- Extract fields (vendor name, revenue, address) from each source. -- Normalize values like dates, currencies, and addresses. -- Resolve entities so duplicates collapse into a single vendor record. -- Preserve lineage so every number links back to the exact page and paragraph where it was found. Only after this work can you build trusted models (like a star schema) and let business teams run queries with confidence. The lesson: unstructured data isn’t “too messy to use,” it just requires a different kind of infrastructure—one that respects context, governance, and traceability from the start.

Thoughts from our CDO panel at Data Management Summit New York: -We're still in the hype cycle - new hires are asking about the GenAI strategy -ROI might not be the biggest question - 90% of business use cases have no ROI - it's like tryig to explain the ROI for Internet back in the early 90s. -be ready to "fail fast" - speed of evolution / adoption -Talk about AI-Ready data - quality, observability, lineage - AI Strategy == How are you supporting business objectives -Building training sets with structured & unstructured data Unstructured data governance: -Sharepoint sites have suddenly become valuable - 70% of enterprise knowledge is in unstructured data - vendors are trending this way -Semantic model overlays - using GenAI to extract structure from unstructured data with human oversight -Holding models to a standard higher than human error rate -how do you integrate this derived structured data int the process? Data governance frameworks haven't caught up with structured data - e.g. PII data hidden in a document - Using GenAI to automatically tag & classify unstructured data -Firms that successfully implement unstructured data governance will lead Integrate banks policies and procedures - allow business user to ask a single question - not dealing with multiple tools -Datamesh/Datafabric - centralized tech strategy - single comprehensive data catalogue - decentralized execution Managing risks of misclassified data - Purge if not required for regulation - Accountability for data use -If not sure about data quality - test, test -Quality challenge at-scale - velocity and timing requirements for large-scale AI/ML -Data and AI are inextricably linked - falling under the same management umbrella -Data quality management and accountability must be distributed Cloud brings improved immunity to scale challenges - legacy stacks limit and restrict -People-first is key to adoption Next 6-12 months: Pushing "Data Product" Intersection of AI with Diplomacy & Trade - meeting regulatory requirements Cloud journey from local on-prem data warehouses Integration across the enterprise at scale #DMSNYC #datamanagement #data #AI #CDO #dataquality #datamesh #datagovernance #datafabric Peggy tsAI Jean-Christophe Lionti Vanessa Jones-Nyoni CJ Jaskoll Andrew Foster, CFA

In a world powered by data and AI, data contracts are rising as a critical foundation for strategy and governance. Think of a data contract as an API for your data, a formal machine-readable agreement that defines the structure, format, quality, and usage terms of data between producers and consumers. By aligning expectations and automating validation at the source, data contracts prevent the all-too-familiar scenario of broken pipelines and surprise downstream. The result is high-quality, trusted data flowing to teams, so that critical decisions are made on reliable information rather than guesswork. From a strategy and governance perspective, the impact is transformative. Data contracts formalize accountability, shifting data quality ownership from a monolithic central team to the domain teams that know the data best. This distributed ownership means data producers commit to schema and quality guarantees, freeing up the central data platform group to build enabling tools and frameworks instead of constantly firefighting issues. Because data contracts often include versioning, SLAs, and even compliance rules, any changes can be managed in a safe, transparent way with no unwelcome surprises. Perhaps most importantly, data contracts foster a feedback-driven, collaborative data culture. Instead of chaos and finger-pointing, organizations get a proactive governance environment where quality checks are embedded, expectations are transparent, data sharing is secure and consistent across systems. In short, embracing data contracts turns data governance from a hurdle into a strategic advantage, building trust in data at every step. For automation, a data contract must be available in a machine-readable form, such as a YAML representation. The Data Contract Specification, an open initiative, provides a common standard, while the Open Data Contract Standard (ODCS) adds enhanced data quality support and remains platform-agnostic. Although adoption is still early and no universal framework exists, several tools are emerging to create, publish, and validate contracts, including: - Data Contract CLI - Data Mesh Manager - Data Contract GPT - Data Contract Editor Dive into the full article on: https://lnkd.in/eNFx3TDY

The question isn’t whether AI can generate SQL. It’s whether enterprise leaders can trust it to generate the right SQL at enterprise scale. That’s the real gap between the demo and enterprise-grade adoption. Demos run on clean schemas and CSVs. Enterprises run on 72-𝐭𝐚𝐛𝐥𝐞 𝐦𝐨𝐝𝐞𝐥𝐬, terabytes of hybrid workloads, and warehouses that don’t even speak the same dialect. As an example, to answer “𝑾𝒉𝒂𝒕 𝒘𝒂𝒔 𝒏𝒆𝒕 𝒓𝒆𝒗𝒆𝒏𝒖𝒆 𝒃𝒚 𝒄𝒉𝒂𝒏𝒏𝒆𝒍 𝒍𝒂𝒔𝒕 𝒒𝒖𝒂𝒓𝒕𝒆𝒓?” you often need 7 tables — orders, items, returns, promo credits, customer segments, product master, and the fiscal calendar. The query requires: - Correct join mix (inner for orders, left for returns/promos) - Fiscal vs calendar alignment (Q1 ERP ≠ Q1 Gregorian) - Grain control to avoid fan-out errors - Dialect awareness across platforms Most AI SQL fails here — inner-joining returns (undercounting), ignoring fiscal calendars, or inflating totals. Benchmarks show  <40% 𝐚𝐜𝐜𝐮𝐫𝐚𝐜𝐲 𝐨𝐧 𝐬𝐭𝐚𝐧𝐝𝐚𝐫𝐝 𝐒𝐐𝐋 𝐭𝐞𝐬𝐭 𝐬𝐞𝐭𝐬.That’s not enterprise-ready. At Tellius, we built for this reality: - A semantic layer to interpret intent and enforce metric governance - Dialect-aware planning for correctness across data platforms - Guardrails to block unsafe or hallucinated queries - AI agents that deliver not just SQL, but drivers, anomalies, and narratives The future of data analysis won’t be defined by flashy demos. It will be defined by whether leaders can trust AI to deliver governed, explainable answers across complex data sources.

🚀 The future of data stewardship with AI agents In the third article of our four-part series, Maria C Villar, Mike Alvarez, Beth Hiatt, and Christine Legner examine how the Digital Data Steward (DDS), powered by AI Agents, augments four critical areas of data governance: ✅ Data Quality ✅ Metadata Management ✅ Master Data ✅ Data Retention From anomaly detection to metadata orchestration and retention compliance, discover how AI agents are shifting from simple automation to cross-agent orchestration that predicts, alerts, corrects — and empowers human stewards to focus on higher-value work. 👉 Read the full article here: https://hubs.ly/Q03G8J1p0

As a Chief Data Officer, I have seen many data teams try to endlessly aim for 100% data accuracy before releasing data products, while users could be making high quality decisions today, even with less than perfect data. The reality? Perfect data is a myth. What actually works: ✅ Release early with clear quality indicators ✅ Make users your quality partners ✅ Define quality in business terms, not technical ones Read the full playbook 👉 https://lnkd.in/gZ3GQW_T #DigitalTransformation, #Leadership, #DataDriven, #WorkforceTransformation #chiefdataofficer, #CDO, #datastrategy, #digitaltransformation, #CTO, #CIO, #AI, #data, #analytics, #DataEngineering