Building Trust in Data Products: Why It Matters

↔️ 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

🚦 𝘚𝘩𝘪𝘧𝘵-𝘓𝘦𝘧𝘵. 𝘚𝘩𝘪𝘧𝘵-𝘙𝘪𝘨𝘩𝘵. But… 𝘄𝗵𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗽𝗲𝗼𝗽𝗹𝗲? This is not the first article I have seen on the shift-left vs. shift-right debate in data governance (and it certainly won’t be the last). The framing is valuable: should governance live in pipelines (shift-left) or in applications (shift-right)? Both matter: proactive guardrails upstream and embedded assurance downstream. But let’s be honest: neither will succeed without the people side of governance. 𝗔𝗱𝗼𝗽𝘁𝗶𝗼𝗻, 𝗮𝗰𝗰𝗼𝘂𝗻𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗰𝗵𝗮𝗻𝗴𝗲 𝗺𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗮𝗿𝗲 𝘄𝗵𝗮𝘁 𝗺𝗮𝗸𝗲 𝘁𝗿𝘂𝘀𝘁 𝗿𝗲𝗮𝗹. Governance “as code” or “in the app” is powerful, yes, but without business stewards, analysts, and decision-makers seeing their role and being supported in it, it risks becoming just another technical layer. 👉 The real shift is not left or right. It is 𝘁𝗼𝘄𝗮𝗿𝗱𝘀 𝗽𝗲𝗼𝗽𝗹𝗲 𝗳𝗶𝗿𝘀𝘁. Because that is where trust is built, and where governance truly holds. So here is the question: 𝘪𝘧 𝘱𝘦𝘰𝘱𝘭𝘦 𝘢𝘳𝘦𝘯’𝘵 𝘪𝘯 𝘵𝘩𝘦 𝘱𝘪𝘤𝘵𝘶𝘳𝘦, 𝘪𝘴 𝘪𝘵 𝘳𝘦𝘢𝘭𝘭𝘺 𝘨𝘰𝘷𝘦𝘳𝘯𝘢𝘯𝘤𝘦 𝘢𝘵 𝘢𝘭𝘭?🤔 #DataGovernance #InformationGovernance #DataManagement #DataTrust #PeopleFirst #DataLeadership

The uncomfortable truth: General/ “all-purpose” agents are not there yet in production environments The AI agent hype is everywhere right now. But before you dive in, let me share what I've learned from deploying agents in real data workflows and why most "general purpose" agents fail spectacularly. I see too many teams building these broad, swiss-army-knife agents that are supposed to "understand your business" and magically solve complex workflows. They demo well, but they break down in production because they're trying to do everything and excelling at nothing. Here's what actually works: 1️⃣ Constrained Environments 2️⃣ Hyper-Specific Prompts 3️⃣ Single-Task Excellence 1. Constrained Environments Put your agent in a sandbox with limited, well-defined tools. Don't give it access to your entire file system, API catalog, or database. Give it exactly what it needs for one specific task. 2. Hyper-Specific Prompts Write surgical instructions ie: "Parse PDF invoices. Extract: vendor_name, invoice_date (YYYY-MM-DD), total_amount (USD, no commas), line_items (product_code:quantity:unit_price). Output JSON. If total_amount > $10,000, flag as 'requires_approval: true'." 3. Single-Task Excellence Each agent should do ONE thing exceptionally well. Ie : "Calculate monthly churn rates from subscription data." Not "customer support" but "categorize support tickets by urgency level." This is the real power of agents. Not replacing human intelligence, but creating an army of specialized, reliable workers that each master one narrow domain. I've watched data teams use Secoda AI to go from one working agent (that generates dataset documentation from schema files) to 50 agents handling their entire data documentation workflow in a matter of weeks. Each agent is simple, testable, and predictable. Data work is perfect for this approach because: ✅ Tasks are often repetitive and well-defined ✅ Success metrics are measurable ✅ Failure modes are containable ✅ Iteration cycles are fast How are you using agents in your workflows today?

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

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

📊 Data Accuracy: The Foundation of Trust in Analytics When we talk about reliable decision-making, everything starts with one thing: accurate data. Without accuracy, even the most advanced dashboards or AI models can lead us astray. Here are some key practices to ensure data accuracy: 🔍 Validation at Ingestion – check formats, ranges, and duplicates before data even enters your systems. ⚖️ Consistency Rules – align business definitions across sources to avoid conflicting interpretations. 🧪 Automated Testing – implement unit tests on transformations (dbt is great for this). 📌 Data Lineage & Documentation – track where your data comes from and how it changes along the way. 🔄 Continuous Monitoring – set up alerts to detect anomalies and drift in real time. 💡 Remember: accurate data builds trust, and trusted data drives confident decisions.

🚀 Just when businesses think they’ve mastered data, the rules change again. In 2025, data engineering is no longer just about moving data from point A to B. It’s about 𝐀𝐈-𝐝𝐫𝐢𝐯𝐞𝐧 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧, 𝐫𝐞𝐚𝐥-𝐭𝐢𝐦𝐞 𝐩𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠, 𝐃𝐚𝐭𝐚 𝐌𝐞𝐬𝐡 𝐚𝐫𝐜𝐡𝐢𝐭𝐞𝐜𝐭𝐮𝐫𝐞𝐬, 𝐬𝐭𝐫𝐨𝐧𝐠𝐞𝐫 𝐠𝐨𝐯𝐞𝐫𝐧𝐚𝐧𝐜𝐞, 𝐚𝐧𝐝 𝐜𝐥𝐨𝐮𝐝-𝐧𝐚𝐭𝐢𝐯𝐞 𝐬𝐭𝐚𝐜𝐤𝐬 - the trends reshaping how businesses operate and scale. The challenge? Many organizations are still weighed down by outdated systems: - Data silos that block collaboration. - Slow, batch-based processes that can’t keep up with market demands. - Rising costs and stalled AI projects caused by weak infrastructure. 💡 Understanding these trends is no longer optional, it’s the key to staying competitive, reducing costs, and turning data into real-time business value. 👉 𝐑𝐞𝐚𝐝 𝐭𝐡𝐞 𝐟𝐮𝐥𝐥 𝐛𝐥𝐨𝐠 𝐡𝐞𝐫𝐞: https://lnkd.in/dph4z2r2 💬 Facing data silos, outdated pipelines, or costly failed AI initiatives? Contact us - our 𝐝𝐚𝐭𝐚 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐬𝐞𝐫𝐯𝐢𝐜𝐞𝐬 help you modernize infrastructure, eliminate bottlenecks, and build a scalable, future-ready data foundation. #DataEngineeringTrends #AIDataEngineering #DataEngineeringTrends2025 #LatestTrends #FutureOfData #DataEngineeringServices #LatestBlog #SculptSoft

“We need better data capabilities by Q1.” This message is echoing in many data platform leaders’ minds as cursors blink on 2025 budget spreadsheets. Your 10-person team is already working long hours. Data quality challenges persist, new data products are constantly requested, and now there’s demand for AI-ready datasets. Current infrastructure already demands significant time and maintenance. Budget guidelines call for cost savings or significant productivity improvements at current spending levels. How on earth can this equation be solved? I’ve had the opportunity to discuss this with our clients who’ve faced these same challenges. When asked how they approached solving this puzzle, the answer was surprisingly straightforward: “We stopped trying to hire our way out of the problem. Instead, we found a platform that let our existing team triple the outcome. Low-code approach, massive automation, embedded data quality testing & monitoring and even analytics to make our data platform transparent to all stakeholders. Our junior developers were building enterprise-grade data pipelines in days, not months.” Data products are still largely built through manual coding and craftsmanship, creating outcomes that can be extremely challenging to further develop or maintain. Often the biggest risk is continuing with the same operational methods and trying to scale purely by adding internal or external human resources. The result is very likely overall productivity decline and increased costs. It’s clear that technology is only one part of the equation, which is why a holistic examination of organizational expectations and readiness to change operating models and work culture is essential. What’s your data platform story heading into 2025? https://lnkd.in/gm6uhbnn DataEngineering #DataTransformation #AI #Leadership #Innovation #DataPlatform

💡 𝐃𝐚𝐭𝐚 𝐒𝐭𝐫𝐚𝐭𝐞𝐠𝐲: 𝐀 𝐏𝐫𝐨𝐦𝐢𝐬𝐞 and 𝐓𝐡𝐞 𝐅𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐬 𝐭𝐨 𝐑𝐞𝐚𝐥𝐢𝐳𝐞 𝐈𝐭 For me, every data strategy starts with a promise. 👉 “𝘞𝘦 𝘸𝘪𝘭𝘭 𝘨𝘪𝘷𝘦 𝘱𝘦𝘰𝘱𝘭𝘦 𝘈𝘐 𝘢𝘯𝘥 𝘥𝘢𝘵𝘢 𝘢𝘱𝘱𝘴 𝘈, 𝘉, 𝘊, 𝘋 𝘢𝘯𝘥 𝘌 𝘴𝘰 𝘵𝘩𝘦𝘺 𝘤𝘢𝘯 𝘥𝘦𝘭𝘪𝘷𝘦𝘳 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴 𝘷𝘢𝘭𝘶𝘦 𝘟, 𝘠 𝘢𝘯𝘥 𝘡, 𝘧𝘶𝘭𝘭𝘺 𝘢𝘭𝘪𝘨𝘯𝘦𝘥 𝘸𝘪𝘵𝘩 𝘰𝘶𝘳 𝘣𝘶𝘴𝘪𝘯𝘦𝘴𝘴 𝘴𝘵𝘳𝘢𝘵𝘦𝘨𝘺.” That promise is usually crystal clear. The harder part? Figuring out how to actually 𝐫𝐞𝐚𝐥𝐢𝐳𝐞 it. That’s where the foundations come in: 🥇 𝐃𝐚𝐭𝐚 𝐐𝐮𝐚𝐥𝐢𝐭𝐲 – How do we ensure our apps run on reliable data? 🎩 𝐑𝐨𝐥𝐞𝐬 & 𝐑𝐞𝐬𝐩𝐨𝐧𝐬𝐢𝐛𝐢𝐥𝐢𝐭𝐢𝐞𝐬 – Who does what in the future data-driven organization? 📦 𝐃𝐞𝐥𝐢𝐯𝐞𝐫𝐲 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐞𝐬 – How do we move an idea from concept to production? 👻 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐲 – How do we stay aligned with regulatory and ethical requirements? When you connect the promise with the foundations, you create a strong playfield for transformation: ✅ The “why” is clear (the promise). ✅ The “how” is concrete (the foundations). From there, the next step is to slice the work over time, shaping a roadmap and transformation program that makes the promise a reality. 𝐓𝐡𝐚𝐭’𝐬 𝐰𝐡𝐞𝐧 𝐚 𝐝𝐚𝐭𝐚 𝐬𝐭𝐫𝐚𝐭𝐞𝐠𝐲 𝐬𝐭𝐨𝐩𝐬 𝐛𝐞𝐢𝐧𝐠 𝐣𝐮𝐬𝐭 𝐰𝐨𝐫𝐝𝐬 𝐨𝐧 𝐩𝐚𝐩𝐞𝐫 𝐚𝐧𝐝 𝐬𝐭𝐚𝐫𝐭𝐬 𝐝𝐫𝐢𝐯𝐢𝐧𝐠 𝐭𝐫𝐮𝐞 𝐛𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐢𝐦𝐩𝐚𝐜𝐭.

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 ...