CEO's 5-point check for AI readiness: lineage, ownership, controls, retention, quality

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

𝗖𝗮𝗻 𝘆𝗼𝘂 𝗯𝘂𝗶𝗹𝗱 𝗮 𝗱𝗮𝘁𝗮 𝗺𝗲𝘀𝗵 𝘄𝗶𝘁𝗵𝗼𝘂𝘁 𝗗𝗮𝘁𝗮+ 𝗔𝗜 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆? Some assume that once you decentralize ownership and give domains responsibility, a data mesh will simply work. The reality: without data observability, it’s nearly impossible to scale. Here’s why: ✅ 𝗧𝗿𝘂𝘀𝘁: If data products aren’t reliable, domains will quickly lose confidence in each other’s outputs. ✅ 𝗔𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Observability provides the visibility needed for teams to take true ownership. ✅ 𝗦𝗰𝗮𝗹𝗮𝗯𝗶𝗹𝗶𝘁𝘆: A mesh multiplies complexity; observability keeps it manageable. ✅ 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝗰𝘆: Without automated monitoring, domains spend more time firefighting than innovating. With Data + AI observability in place, you can even assign each data product a Data Reliability Score, built from KPIs like freshness, completeness, accuracy, and pipeline health. This makes trust measurable, comparable, and actionable across the mesh. A data mesh is not just about architecture or org design. It’s about ensuring every data product can be trusted and that requires observability at its core. 💬 What’s your take: is data observability optional or essential for a successful data mesh? #DataObservability #AIObservability #DataMesh #DataReliability #DataEngineering #DataOps

<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

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

<p>Check out our latest blog by Robert Gauthier&nbsp;on&nbsp;best practices for building a robust data ingestion pipeline&nbsp;for Observability Data! Learn how to architect scalable, resilient pipelines that ensure your observability data delivers real value&mdash;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/1HRX42

Most people misunderstand DATA MESH. It’s NOT a tool. It’s an OPERATING MODEL. Principles I’ve seen matter in practice: - Data as a product. - Domain ownership. - Self‑serve platforms. - Federated governance. I’ve seen companies fail at mesh. They bought tech but ignored CULTURE. LESSON: DATA MESH works when ORG DESIGN evolves with technology. CTA: Where has data mesh actually worked for you? What changed beyond tools? #DataMesh #DataStrategy #DataEngineering #BigData

Friday – Wisdom to apply + Sneak peek next week 💡 You’ve got options—choose based on maturity and goals. If your organization still struggles with data silos and slow central teams, a Data Mesh (even partial) can supercharge agility. If you're focused on big data analytics with fewer domain needs, a Data Lake may offer simpler scale. Many of today’s architects mix both—using lakes for raw consolidation and meshes for domain empowerment. Cutting-edge approach? Autonomous data products—trusted, governed, domain-owned—and the future of scalable data ecosystems. 👉 What shall we explore next week? Potential topics: "Scalable MLOps Patterns" or "Responsible AI System Design"? Pro Tips: * Always align your architecture with org structure and culture. * Use pilots to validate before full transformation. * Build governance into your design, not as an afterthought. 📖 Read more: 🔗 https://lnkd.in/gqPBS2sG 🔗 https://lnkd.in/gfpPFGQj 🔗 https://lnkd.in/g6Q6V2Jc 🔗 https://lnkd.in/gSyUQCSf #DataMesh #DataLake #DataArchitecture #NextWeekPreview

A Data Lake is more than just storage—it’s the foundation of a modern data ecosystem. 🌊 It brings together structured, semi-structured, and unstructured data in one place, supporting everything from big data processing and log analytics to data warehousing, relational & non-relational databases, and advanced machine learning use cases. 🚀 By enabling organizations to store massive volumes of raw data and process it flexibly, data lakes open the door to faster insights, smarter decision-making, and scalable innovation. 💡 #DataEngineering #BigData #DataLake #MachineLearning #CloudComputing #Analytics

↔️ 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