Karima Makrof, MBA’s Post

↔️ Shift-Left vs Shift-Right in Data Governance: Who Owns Trust—and Who Builds It? ➡️ When Alation started the data catalog, it was all about engagement and adoption, shifting the work of data management to the right.  ⬅️ Then, with the modern data stack, data engineering teams pulled governance towards the left, moving quality controls, contracts, metadata, and validation upstream, closer to the source, with the premise that engineers could bake in trust from Day 1. ➡️ Today, LLMs and AI empower less-technical stewards & analysts to scale rapidly. Raluca Alexandru called it a Shift‑Right moment. ❓If you had to choose one, which one would you choose? 👈 Shift‑Left: Governance as code, embedded in pipelines, ensuring data quality before downstream risk. 👉Shift‑Right: Governance embedded in applications, revalidations at consumption, trust on demand—especially where AI-generated outputs are concerned. ❗ Why it matters: - Shift-Left gives you proactive guardrails, fewer data surprises, and more efficiency. - Shift-Right gives users embedded assurance when and where they need it—especially essential for LLM-driven workflows. Sanjeev Mohan and Guido De Simoni what are your thoughts on this one? #datagovernance #datatrust

Financial firms are tackling an explosion of complex data - from structured trading records to unstructured research reports. Scaling data lakes that 𝗱𝗲𝗹𝗶𝘃𝗲𝗿 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲, 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲, 𝗮𝗻𝗱 𝗔𝗜 𝗿𝗲𝗮𝗱𝗶𝗻𝗲𝘀𝘀 is now the critical next step. Is your organization facing these challenges? ⏳ Legacy systems slowing down under data growth 🐢 Lengthy analytics delaying insights 🔒 Managing compliance risks across complex data environments 🤯 Difficulty delivering trusted, unified data access at scale Discover how financial institutions can overcome these barriers with 𝗺𝗼𝗱𝘂𝗹𝗮𝗿, 𝗴𝗼𝘃𝗲𝗿𝗻𝗲𝗱, 𝗮𝗻𝗱 𝗺𝘂𝗹𝘁𝗶-𝗰𝗹𝗼𝘂𝗱 𝗱𝗮𝘁𝗮 𝗹𝗮𝗸𝗲 𝗮𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲𝘀. Our latest guide, “𝗧𝗵𝗲 𝗕𝗹𝘂𝗲𝗽𝗿𝗶𝗻𝘁 𝗳𝗼𝗿 𝗔𝗜-𝗥𝗲𝗮𝗱𝘆 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲𝘀 𝗶𝗻 𝗙𝗶𝗻𝗮𝗻𝗰𝗶𝗮𝗹 𝗙𝗶𝗿𝗺𝘀,” offers: ✅ Real-world lessons and case examples ✅ A clear framework covering ingestion, storage, analytics, governance, and consumption ✅ Key trade-offs and how to navigate them ✅ A practical, step-by-step roadmap to build and mature your data lake ✅ A maturity model to benchmark your AI readiness progress 📥 Download the attached PDF to unlock actionable insights and accelerate your AI transformation journey. 💬 What’s the biggest challenge your data teams face at scale? Or which breakthrough moved your analytics forward? Let’s discuss in the comments! #DataLakes #AIinFinance #Fintech #CloudData #DataGovernance #MachineLearning #FinancialServices #BigData

AI-ready data isn’t a workshop, it’s an operating discipline. If exec dashboards or GenAI features wobble, it’s rarely the model. It’s the data plane. >> Here’s the checklist I use with CXOs to turn “governance” into runtime controls: >> Inventory the revenue-critical data products. Give each a DRI, SLA, and named downstream consumers. If nobody owns it, nobody saves it. >> Instrument lineage end-to-end (column-level). Source → lake/warehouse → transforms → BI/models. Impact analysis should take seconds, not meetings. >> Define drift thresholds + SLOs. Freshness, volume, distribution, schema. Treat violations like pager incidents. >>Embed governance at the table level. Auto-classify PII, wire retention/consent, and tag materiality in metadata. Policy should be executable, not decorative. >>Automate incident routing. Pipe observability alerts into the on-call tools you already live in (Snowflake/BigQuery hooks, Opsgenie/PagerDuty, Slack). >>Report quality KPIs like SRE. MTTD, MTTR, recurrence rate right next to uptime/latency. If you can’t measure trust, you can’t manage it. >>Run a controlled pilot. One pipeline, clear thresholds, prove MTTR/accuracy gains—then roll out. >>What “good” looks like in the wild >>95% owner coverage • 99% SLO adherence on tier-1 tables • MTTD < 15 min • Zero incidents reaching execs If you want a copy of the AI-Ready Data Checklist, drop “expert” below and I’ll set up a free 1:1 with our data engineering lead or grab the guide here and run it yourself. #AIReadyData #DataObservability #DataGovernance #DataLineage #DataDrift #MLOps #DataTrust Rakuten SixthSense

𝐅𝐫𝐨𝐦 𝐃𝐚𝐭𝐚 𝐌𝐞𝐬𝐡 𝐭𝐨 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐌𝐞𝐬𝐡: 𝐇𝐨𝐰 𝐌𝐂𝐏 𝐢𝐬 𝐑𝐞𝐬𝐡𝐚𝐩𝐢𝐧𝐠 𝐅𝐞𝐝𝐞𝐫𝐚𝐭𝐞𝐝 𝐃𝐚𝐭𝐚 𝐀𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞   Traditional data mesh architectures promise decentralized ownership with federated access, but in practice still require custom integration work for each data source.   MCP introduces a standardized protocol layer that abstracts away the underlying API differences between heterogeneous data products. Our implementation sits between MCP clients and our existing data products (TCGA, clinical trials, biomarkers, etc.), handling discovery, authentication, and data transformation through a common interface.   The technical shift is significant: instead of building point-to-point integrations, we're seeing emergence of what might be called an "agentic mesh" - where AI agents can computationally discover and query federated data sources without knowing their underlying schemas or APIs. This reduces the integration complexity from O(n²) to O(n) when connecting multiple data products.   Key architectural lessons (We are still learning) Here are some of the challenges we faced: - Connection pooling and caching become critical when serving multiple concurrent agent requests. - Data product adapters need to handle version differences gracefully. Streaming support is essential for large datasets that agents typically request.   The broader implication is that MCP may become the missing application layer for federated data architectures. Teams building data products should consider MCP compatibility as a first-class requirement, not an afterthought, as it determines whether their data can participate in automated cross-domain analysis workflows. Let us know your thoughts or if you want to try out our server (https://lnkd.in/ei8s4-tT)

🚀 Automating Data Governance: What’s Next? From AI-driven policy enforcement to real-time, cloud-native governance—data governance is evolving fast. In this latest series, Fred Krimmelbein breaks down emerging trends like self-service platforms, metadata-driven automation, and Gen AI use cases that are reshaping how we manage data. 👉 https://lnkd.in/gVsuiy8d #DataGovernance #Automation #GenAI #DataOps #CloudData #Compliance

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

How do you scale Data Products without losing control? It’s a question I hear from many organizations. As data ecosystems decentralize, cover many technologies the opportunities grow — but so do the risks. Governance is NOT an after thought, NOT a reactive action it should be embeded in the full process from ideation to deployment and runtime of datat products. Take the active approach because... I see common challenges keep surfacing: > Schema and data drift that silently break dependencies > Quality issues that erode trust in analytics and AI > Increasing compliance demands across multiple jurisdictions > Teams moving fast, but without a shared framework > Traditional governance approaches — manual checks, post-facto audits, endless documentation — can’t keep up. They slow delivery instead of enabling it. We’ve taken a different path: automated computational governance. Policies and data contracts are embedded directly into the Data Product lifecycle. The result: ✅ Producers and consumers know exactly what to expect ✅ Compliance is built in, not added later ✅ Teams keep autonomy, while the business gains trust and explainability This is not just technology — it’s about building a formal way of working that lets organizations innovate fast and responsibly. I’d love to exchange thoughts with peers on how you’re approaching this balance in your own data strategy. So let’s connect and share some knowledge around Witboost the data product management paltform with automated computational governance. #DataProducts #GovernanceByDesign #DataContracts #Witboost #AIReady

5 actions to build an AI-ready data culture - CIO: Dun & Bradstreet monitors over 85 billion data quality observability points with homegrown tools DataShield and DataWatch. The former enforces ...

Data Products with Trust at the Core 80% of data projects don’t fail because of technology. They fail because of broken trust. Everyone talks about “data products.” But without trust, they’re just tables with a fancy name. 🔑 Why this matters now: AI agents amplify bad data faster than ever. One corrupted “customer” definition can cascade through dozens of AI-driven decisions in minutes, not months. A true data product isn’t pipeline output — it’s a living, trusted asset that is: 1️⃣ Trusted → Built on data contracts that define expectations, ownership, and SLAs upfront 2️⃣ Measured → Quality, usage, and business impact tracked like revenue KPIs 3️⃣ Governed → Clear business + technical ownership with defined escalation paths 4️⃣ Automated → CI/CD for data where validation, lineage, and access are code, not tickets 5️⃣ Monitored → Real-time observability for freshness, accuracy, and compliance 💡 Examples already working in the enterprise today: ▪️ Master Data Products → A “Golden Customer” with 99.5% accuracy SLA, owned by Sales Ops ▪️ Event Products → “Order Completed” with schema versioning + backward compatibility ▪️ Analytical Products → “Monthly Recurring Revenue” with one calculation, reused across 15 teams The practical shift: 👉 Instead of building 15 customer tables across teams, build ONE trusted Customer product that serves 15 use cases. Start small. Pick your most duplicated entity. Define its contract. Measure its usage. Scale from there.

<p>Check out our latest blog by Robert Gauthier on best practices for building a robust data ingestion pipeline for Observability Data! Learn how to architect scalable, resilient pipelines that ensure your observability data delivers real value-whether for real-time incident response or long-term analytics. This guide is packed with actionable tips, covering everything from data quality to distributed processing and security.</p> <p><br></p><p>Perfect for anyone using DX Operational Observability (DX O2) or exploring next-gen AIOps strategies!</p> <p><br></p><p>Dive into key insights like:</p> <ul><li>Ensuring high-fidelity data capture for accurate insights.</li><li>Best practices for handling massive data volumes efficiently.</li><li>Strategies to integrate, process, and secure observability data at scale.</li></ul> <p>Read the full blog for expert advice and step confidently into the future of IT operations.</p> <p><br></p><p>#DXO2 #Observability #AIOps #DataIngestion #DXOperationalObservability #Broadcom</p> https://dy.si/Fr5b5