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
Edwin Bonte’s Post
More Relevant Posts
-
Data Guiding Principle: Purpose-Optimized Persistence Here's another set of "guiding principles" that should be part of every company's data strategy: Rule 1: Persist is optimized for purpose. Rule 2: There should be 1 (and only 1) environment for each purpose. A lot of people talk about “single source of truth.” While that’s noble and important at the data element level (each data element is born in 1 place), it’s not practical (or wise) to treat that source as the only place that data can live. You wouldn’t run machine learning directly against the same database that powers your customer-facing app serving 30M users. Technically possible? I suppose. Practically disastrous? You betchya. Instead, copy the data into an environment built and optimized for ML, and let the production database do what it’s meant to: keep the app running at scale. But here’s the trap: once you have that ML environment, do you need another one? No. In fact the answer is, HELL NO. The minute you spin up multiple environments for the same purpose, you dilute the value of your data, complicate governance, and waste real money on licenses and infrastructure. Companies often justify these overlaps with hair-splitting logic: “This ML environment is for Sales, that one is for Operations.” What that usually reveals is either weak governance, weak leadership, or someone buying into the sales pitch that “our sales-specialized tool will boost sales performance by 5%.” Spoiler alert: it’s almost never the tool, it’s the human using it. If they want it to be better, they’ll make it better and you’ll never know what could have happened with your "standard" tool. Strong leadership and strong governance keep your environment lean and effective. Otherwise, your architecture ends up looking like a NASCAR hood, plastered with every logo under the Sun, none of which are really providing the value they promised to the car you’re driving. #DataStrategy #DataGovernance #DataArchitecture #DataManagement #DataLeadership
To view or add a comment, sign in
-
-
𝘼𝙄 𝙧𝙚𝙖𝙙𝙞𝙣𝙚𝙨𝙨 = 𝙙𝙖𝙩𝙖 𝙧𝙚𝙖𝙙𝙞𝙣𝙚𝙨𝙨 (a CEO’s 5-point quick check) Models are only as reliable as the metadata and controls behind them. Before adding another tool, check the foundations: 1) 𝗟𝗶𝗻𝗲𝗮𝗴𝗲 Key fields are traceable end-to-end: source → transforms → owners. 2) 𝗢𝘄𝗻𝗲𝗿𝘀𝗵𝗶𝗽 Every critical dataset has a named owner and a clear escalation path. 3) 𝗣𝗜𝗜 𝗰𝗼𝗻𝘁𝗿𝗼𝗹𝘀 Sensitive data is classified, masked where needed, and access is enforced. 4) 𝗥𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻 What’s kept, why it’s kept, and when it’s deleted are defined—and applied. 5) 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗦𝗟𝗔𝘀 Freshness, completeness, and accuracy have thresholds that are measured and visible. 𝘖𝘯𝘦 𝘱𝘳𝘢𝘤𝘵𝘪𝘤𝘢𝘭 𝘵𝘪𝘱 Label authoritative sources in the platform itself (schemas, tags, views). Slides drift; governed labels travel with the data. 𝘐𝘯 𝘱𝘳𝘢𝘤𝘵𝘪𝘤𝘦 Consolidating scattered “source-of-truth” notes into a governed knowledge store tied to lineage reduces review loops and cuts hallucinations in LLM workflows. Bookmark for your next roadmap review. #AIReadiness #DataGovernance #DataStrategy #Metadata #Leadership #MLOps #GenAI
To view or add a comment, sign in
-
-
🔍 In data engineering, “small leaks” often sink the biggest ships. It’s easy to get excited about building large-scale pipelines or deploying machine learning models, but many projects fail because of overlooked details like schema drift, inconsistent IDs, or missing timestamps. These issues may seem minor, but they erode trust and slow down decision-making across teams. 📍Case in point: At one organization, marketing and finance teams were pulling “active customer” counts from two different pipelines. The numbers didn’t match, and leadership wasted weeks debating which was correct. The fix wasn’t flashy, we standardized data definitions, added validation rules in Power Query, and set up monitoring alerts. Suddenly, the dashboards aligned, trust was restored, and decisions moved forward without hesitation. 💡 Takeaway: Data engineering isn’t only about moving data; it’s about safeguarding its reliability. A clean, trusted dataset accelerates analytics far more than any new tool or algorithm. 👉 I’d love to hear from you: What’s your go-to method for ensuring data consistency in your pipelines? #DataEngineering #DataAnalytics #ETL #DataQuality #BusinessIntelligence
To view or add a comment, sign in
-
Not long ago, I watched a major data engineering program stumble. The architecture was beautiful on paper. Medallion layers, modern tooling, real-time ingestion. Everyone was excited. But governance? That was treated as a “side task.” Just a couple of consultants. Minimal attention. “We’ll figure it out later.” At first, things looked fine. Data was moving, dashboards were live, leadership was smiling. ••• Then the alarms went off. Numbers in the executive dashboard didn’t match finance reports. Customer counts varied depending on which layer you queried. Regulators asked for data lineage, and no one could provide it. Critical decisions were made on insights that simply weren’t true. ••• Suddenly, the shiny new platform became a liability. Trust eroded. Confidence collapsed. The “data-driven” initiative stalled. Millions of dollars were at risk. And all because Data Quality was mistaken for Data Cleansing. ••• Here’s the hard truth: if you don’t invest in Data Governance upfront, you will pay for it later with interest. Because when executives stop trusting the data, the program fails - no matter how elegant the pipelines are. It’s time we stop underestimating governance. Start treating Data Quality as the foundation of everything. Your data platform is only as strong as the trust people have in it. Don’t wait for the alarms to go off. #DataGovernance #DataQuality #DQ #DataEngineering #DataPrograms #DataProjects #DataManagement #DataCleansing #Fractal #FractalAnalytics #FractalAI Fractal
To view or add a comment, sign in
-
-
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
To view or add a comment, sign in
-
↔️ 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
To view or add a comment, sign in
-
-
🚦 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
↔️ 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
To view or add a comment, sign in
-
-
Data silos aren’t just an inconvenience - they’re silent profit killers. Every isolated database. Every locked department. Every “we’ll sync later” moment… They all cost you: speed, clarity, and ultimately - growth. The signs are everywhere: – Reports that never align – Teams working in the dark – Missed opportunities hidden in plain sight Now imagine this instead: Your data flows seamlessly - from one team to another, one decision to the next - with zero friction. Here’s what makes it possible: > Centralized Data Platforms – A single source of truth instead of fragmented chaos > ETL & ELT Pipelines – Structured + unstructured data, connected in real time > Data Governance & Accessibility – Secure yet collaborative access for every stakeholder > AI & Automation – Metadata driven categorization for instant discoverability At Brilliqs, we help businesses unlock seamless, interconnected data ecosystems that drive faster, smarter decisions. Because when data flows freely, so does innovation. Because when data flows, innovation follows. Want to break down the silos slowing your progress? Comment below or message us directly - let’s start unlocking your next wave of growth. #DataSilos #DigitalTransformation #BusinessIntelligence #DataStrategy #EnterpriseData #Brilliqs
To view or add a comment, sign in
-
After working on my 𝗳𝗶𝗿𝘀𝘁 𝗿𝗲𝗮𝗹 𝗽𝗿𝗼𝗷𝗲𝗰𝘁 and spending more than 6 months in a company fully focused on AI, I understood something fundamental: 𝗧𝗵𝗲 𝗺𝗼𝘀𝘁 𝘃𝗶𝘁𝗮𝗹 𝗽𝗮𝗿𝘁 𝗶𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝘁𝗵𝗲 𝗺𝗼𝗱𝗲𝗹. 𝗜𝘁’𝘀 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮. 👉 Data engineering 👉 Data handling 👉 Data quality & governance 👉 SQL and pipelines 👉 Everything related to data I realize 𝘀𝘁𝗿𝗼𝗻𝗴 𝗱𝗮𝘁𝗮 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀 𝗮𝗿𝗲 𝘄𝗵𝗮𝘁 𝗺𝗮𝗸𝗲 𝗔𝗜 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝘄𝗼𝗿𝗸 𝗶𝗻 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻. Without clean, reliable, well-structured data → even the most powerful model will fail. With good data practices → even simpler models deliver amazing results.
To view or add a comment, sign in
-
No contract = chaos. And chaos kills data reliability. The more companies move fast, The more things break. And when they break, it’s usually not the data team’s fault. I’ve seen models collapse because: → A product manager renamed an event → A developer removed a field in production → A team deprecated a table without warning This isn’t sabotage. It’s misalignment. Data contracts fix that. They’re not buzzwords. They’re agreements. They define: → What data will be produced → When it will be available → How changes will be communicated Think of it as an API for data. It’s not rigid bureaucracy. It’s clarity. Here’s how I introduce data contracts: 1. Start with a critical pipeline. Pick a use case that hurts when it breaks. 2. Identify data producers + consumers. You need both sides. 3. Create shared expectations. Use a doc or schema. Define breaking vs non-breaking changes. 4. Assign owners. If everything breaks, who gets pinged? 5. Automate checks. Use dbt tests, contracts-as-code, alerts. It doesn’t have to be perfect. It just has to be consistent. And once it works for one pipeline, expand. If your data stack feels fragile, Don’t just scale it. Secure it. Using data contracts? What’s your stack?
To view or add a comment, sign in
-