Data Trust and Pipeline Reliability Frameworks

Data silos aren’t just a tech problem - they’re an operational bottleneck that slows decision - making, erodes trust, and wastes millions in duplicated efforts. But we’ve seen companies like Autodesk, Nasdaq, Porto, and North break free by shifting how they approach ownership, governance, and discovery. Here’s the 6-part framework that consistently works: 1️⃣ Empower domains with a Data Center of Excellence. Teams take ownership of their data, while a central group ensures governance and shared tooling. 2️⃣ Establish a clear governance structure. Data isn’t just dumped into a warehouse—it’s owned, documented, and accessible with clear accountability. 3️⃣ Build trust through standards. Consistent naming, documentation, and validation ensure teams don’t waste time second-guessing their reports. 4️⃣ Create a unified discovery layer. A single “Google for your data” makes it easy for teams to find, understand, and use the right datasets instantly. 5️⃣ Implement automated governance. Policies aren’t just slides in a deck—they’re enforced through automation, scaling governance without manual overhead. 6️⃣ Connect tools and processes. When governance, discovery, and workflows are seamlessly integrated, data flows instead of getting stuck in silos. We’ve seen this transform data cultures - reducing wasted effort, increasing trust, and unlocking real business value. So if your team is still struggling to find and trust data, what’s stopping you from fixing it?

Ensuring data quality at scale is crucial for developing trustworthy products and making informed decisions. In this tech blog, the Glassdoor engineering team shares how they tackled this challenge by shifting from a reactive to a proactive data quality strategy. At the core of their approach is a mindset shift: instead of waiting for issues to surface downstream, they built systems to catch them earlier in the lifecycle. This includes introducing data contracts to align producers and consumers, integrating static code analysis into continuous integration and delivery (CI/CD) workflows, and even fine-tuning large language models to flag business logic issues that schema checks might miss. The blog also highlights how Glassdoor distinguishes between hard and soft checks, deciding which anomalies should block pipelines and which should raise visibility. They adapted the concept of blue-green deployments to their data pipelines by staging data in a controlled environment before promoting it to production. To round it out, their anomaly detection platform uses robust statistical models to identify outliers in both business metrics and infrastructure health. Glassdoor’s approach is a strong example of what it means to treat data as a product: building reliable, scalable systems and making quality a shared responsibility across the organization. #DataScience #MachineLearning #Analytics #DataEngineering #DataQuality #BigData #MLOps #SnacksWeeklyonDataScience – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gUwKZJwN

Dear #DataEngineers, No matter how confident you are in your SQL queries or ETL pipelines, never assume data correctness without validation. ETL is more than just moving data—it’s about ensuring accuracy, completeness, and reliability. That’s why validation should be a mandatory step, making it ETLV (Extract, Transform, Load & Validate). Here are 20 essential data validation checks every data engineer should implement (not all pipeline require all of these, but should follow a checklist like this): 1. Record Count Match – Ensure the number of records in the source and target are the same. 2. Duplicate Check – Identify and remove unintended duplicate records. 3. Null Value Check – Ensure key fields are not missing values, even if counts match. 4. Mandatory Field Validation – Confirm required columns have valid entries. 5. Data Type Consistency – Prevent type mismatches across different systems. 6. Transformation Accuracy – Validate that applied transformations produce expected results. 7. Business Rule Compliance – Ensure data meets predefined business logic and constraints. 8. Aggregate Verification – Validate sum, average, and other computed metrics. 9. Data Truncation & Rounding – Ensure no data is lost due to incorrect truncation or rounding. 10. Encoding Consistency – Prevent issues caused by different character encodings. 11. Schema Drift Detection – Identify unexpected changes in column structure or data types. 12. Referential Integrity Checks – Ensure foreign keys match primary keys across tables. 13. Threshold-Based Anomaly Detection – Flag unexpected spikes or drops in data volume or values. 14. Latency & Freshness Validation – Confirm that data is arriving on time and isn’t stale. 15. Audit Trail & Lineage Tracking – Maintain logs to track data transformations for traceability. 16. Outlier & Distribution Analysis – Identify values that deviate from expected statistical patterns. 17. Historical Trend Comparison – Compare new data against past trends to catch anomalies. 18. Metadata Validation – Ensure timestamps, IDs, and source tags are correct and complete. 19. Error Logging & Handling – Capture and analyze failed records instead of silently dropping them. 20. Performance Validation – Ensure queries and transformations are optimized to prevent bottlenecks. Data validation isn’t just a step—it’s what makes your data trustworthy. What other checks do you use? Drop them in the comments! #ETL #DataEngineering #SQL #DataValidation #BigData #DataQuality #DataGovernance

At its core, data quality is an issue of trust. As organizations scale their data operations, maintaining trust between stakeholders becomes critical to effective data governance. Three key stakeholders must align in any effective data governance framework: 1️⃣ Data consumers (analysts preparing dashboards, executives reviewing insights, and marketing teams relying on events to run campaigns) 2️⃣ Data producers (engineers instrumenting events in apps) 3️⃣ Data infrastructure teams (ones managing pipelines to move data from producers to consumers) Tools like RudderStack’s managed pipelines and data catalogs can help, but they can only go so far. Achieving true data quality depends on how these teams collaborate to build trust. Here's what we've learned working with sophisticated data teams: 🥇 Start with engineering best practices: Your data governance should mirror your engineering rigor. Version control (e.g. Git) for tracking plans, peer reviews for changes, and automated testing aren't just engineering concepts—they're foundations of reliable data. 🦾 Leverage automation: Manual processes are error-prone. Tools like RudderTyper help engineering teams maintain consistency by generating analytics library wrappers based on their tracking plans. This automation ensures events align with specifications while reducing the cognitive load of data governance. 🔗 Bridge the technical divide: Data governance can't succeed if technical and business teams operate in silos. Provide user-friendly interfaces for non-technical stakeholders to review and approve changes (e.g., they shouldn’t have to rely on Git pull requests). This isn't just about ease of use—it's about enabling true cross-functional data ownership. 👀 Track requests transparently: Changes requested by consumers (e.g., new events or properties) should be logged in a project management tool and referenced in commits. ‼️ Set circuit breakers and alerts: Infrastructure teams should implement circuit breakers for critical events to catch and resolve issues promptly. Use robust monitoring systems and alerting mechanisms to detect data anomalies in real time. ✅ Assign clear ownership: Clearly define who is responsible for events and pipelines, making it easy to address questions or issues. 📄Maintain documentation: Keep standardized, up-to-date documentation accessible to all stakeholders to ensure alignment. By bridging gaps and refining processes, we can enhance trust in data and unlock better outcomes for everyone involved. Organizations that get this right don't just improve their data quality–they transform data into a strategic asset. What are some best practices in data management that you’ve found most effective in building trust across your organization? #DataGovernance #Leadership #DataQuality #DataEngineering #RudderStack

Do you think Data Governance: All Show, No Impact? → Polished policies ✓ → Fancy dashboards ✓ → Impressive jargon ✓ But here's the reality check: Most data governance initiatives look great in boardroom presentations yet fail to move the needle where it matters. The numbers don't lie. Poor data quality bleeds organizations dry—$12.9 million annually according to Gartner. Yet those who get governance right see 30% higher ROI by 2026. What's the difference? ❌It's not about the theater of governance. ✅It's about data engineers who embed governance principles directly into solution architectures, making data quality and compliance invisible infrastructure rather than visible overhead. Here’s a 6-step roadmap to build a resilient, secure, and transparent data foundation: 1️⃣ 𝗘𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵 𝗥𝗼𝗹𝗲𝘀 & 𝗣𝗼𝗹𝗶𝗰𝗶𝗲𝘀 Define clear ownership, stewardship, and documentation standards. This sets the tone for accountability and consistency across teams. 2️⃣ 𝗔𝗰𝗰𝗲𝘀𝘀 𝗖𝗼𝗻𝘁𝗿𝗼𝗹 & 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 Implement role-based access, encryption, and audit trails. Stay compliant with GDPR/CCPA and protect sensitive data from misuse. 3️⃣ 𝗗𝗮𝘁𝗮 𝗜𝗻𝘃𝗲𝗻𝘁𝗼𝗿𝘆 & 𝗖𝗹𝗮𝘀𝘀𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 Catalog all data assets. Tag them by sensitivity, usage, and business domain. Visibility is the first step to control. 4️⃣ 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 & 𝗗𝗮𝘁𝗮 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 Set up automated checks for freshness, completeness, and accuracy. Use tools like dbt tests, Great Expectations, and Monte Carlo to catch issues early. 5️⃣ 𝗟𝗶𝗻𝗲𝗮𝗴𝗲 & 𝗜𝗺𝗽𝗮𝗰𝘁 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀 Track data flow from source to dashboard. When something breaks, know what’s affected and who needs to be informed. 6️⃣ 𝗦𝗟𝗔 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 & 𝗥𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴 Define SLAs for critical pipelines. Build dashboards that report uptime, latency, and failure rates—because business cares about reliability, not tech jargon. With the rising AI innovations, it's important to emphasise the governance aspects data engineers need to implement for robust data management. Do not underestimate the power of Data Quality and Validation by adapting: ↳ Automated data quality checks ↳ Schema validation frameworks ↳ Data lineage tracking ↳ Data quality SLAs ↳ Monitoring & alerting setup While it's equally important to consider the following Data Security & Privacy aspects: ↳ Threat Modeling ↳ Encryption Strategies ↳ Access Control ↳ Privacy by Design ↳ Compliance Expertise Some incredible folks to follow in this area - Chad Sanderson George Firican 🎯 Mark Freeman II Piotr Czarnas Dylan Anderson Who else would you like to add? ▶️ Stay tuned with me (Pooja) for more on Data Engineering. ♻️ Reshare if this resonates with you!

DataOps: Accelerating Trustworthy Data Delivery As Enterprise Architects, we know: 👉 Moving fast with bad data is worse than moving slow. Data is now the backbone of decision-making. But speed alone won’t cut it—leaders need data that is fast, reliable, and trustworthy. This is where DataOps changes the game. Think DevOps, but for data pipelines—bringing rigor, automation, and governance to every step of delivery. What makes it different? 1️⃣ Continuous integration for data pipelines 2️⃣ Automated testing to catch issues early 3️⃣ Real-time monitoring for failures 4️⃣ Collaboration across engineering, analytics, ML, and business 5️⃣ Versioning for trust and reproducibility For Enterprise Architects, the takeaway is clear: DataOps isn’t just a technical framework—it’s a governance accelerator. It ensures the data flowing into analytics, AI, and dashboards is something your business can trust. 👉 The future of EA isn’t just designing systems. It’s ensuring those systems deliver trusted data at scale.

There's so much irony that the "𝐃𝐞𝐟𝐞𝐧𝐝𝐞𝐫" 𝐚𝐧𝐝 𝐭𝐡𝐞𝐢𝐫 𝐌𝐢𝐬𝐭𝐫𝐮𝐬𝐭 𝐚𝐫𝐞 𝐤𝐞𝐲 𝐭𝐨 𝐛𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐭𝐫𝐮𝐬𝐭 for the everyday data user ⚖️ The word defense comes from the Latin 𝘥𝘦𝘧𝘦𝘯𝘴𝘢𝘳𝘦, meaning to ward off, to protect persistently. Defensive programming, then, is the persistent act of protecting your system from collapse; not just technical failure, but epistemic failure: wrong assumptions, vague specs, or future changes in context. Defensive programming begins with a simple premise: 𝐧𝐞𝐯𝐞𝐫 𝐭𝐫𝐮𝐬𝐭 𝐭𝐡𝐞 𝐰𝐨𝐫𝐥𝐝. A system designed not to trust and work around mistrust. And in defending against mistrust, the system creates a more trustworthy, reliable, or resilient layer for citizens operating on top of it. 𝐓𝐡𝐞 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐆𝐮𝐚𝐫𝐝💂🏻♀️ In other words, 𝐲𝐨𝐮 𝐝𝐞𝐥𝐞𝐠𝐚𝐭𝐞 𝐭𝐡𝐞 𝐛𝐫𝐮𝐧𝐭 𝐨𝐟 𝐦𝐢𝐬𝐭𝐫𝐮𝐬𝐭 𝐭𝐨 𝐭𝐡𝐞 𝐟𝐨𝐮𝐧𝐝𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐩𝐥𝐚𝐭𝐟𝐨𝐫𝐦 to have the luxury of a trusting and relaxing experience. There's an entity out there that can suspect, defend, and always be on guard: all on your behalf. Like a very own secret service for the enterprise data backbone. 𝐖𝐡𝐨 𝐭𝐨 𝐌𝐢𝐬𝐭𝐫𝐮𝐬𝐭 🔔 The system 𝐰𝐨𝐮𝐥𝐝𝐧’𝐭 𝐬𝐩𝐚𝐫𝐞 𝐚𝐧𝐲 𝐞𝐧𝐭𝐢𝐭𝐲 𝐢𝐧 𝐢𝐭𝐬 𝐦𝐢𝐬𝐭𝐫𝐮𝐬𝐭. 𝐍𝐨𝐭 𝐲𝐨𝐮𝐫 𝐮𝐬𝐞𝐫𝐬. 𝐍𝐨𝐭 𝐲𝐨𝐮𝐫 𝐝𝐞𝐩𝐞𝐧𝐝𝐞𝐧𝐜𝐢𝐞𝐬. 𝐍𝐨𝐭 𝐞𝐯𝐞𝐧 𝐲𝐨𝐮𝐫 𝐟𝐮𝐭𝐮𝐫𝐞 𝐬𝐞𝐥𝐟. Because somewhere, somehow, assumptions will break. Systems will lie. Users will be lazy. And in those moments, the code that survives is the code that was 𝐰𝐫𝐢𝐭𝐭𝐞𝐧 𝐰𝐢𝐭𝐡 𝐝𝐨𝐮𝐛𝐭, 𝐰𝐢𝐭𝐡 𝐝𝐢𝐬𝐜𝐢𝐩𝐥𝐢𝐧𝐞, 𝐚𝐧𝐝 𝐰𝐢𝐭𝐡 𝐝𝐞𝐬𝐢𝐠𝐧 𝐟𝐨𝐫 𝐟𝐚𝐢𝐥𝐮𝐫𝐞. 𝐖𝐡𝐚𝐭 𝐝𝐨𝐞𝐬 𝐢𝐭 𝐦𝐞𝐚𝐧 𝐟𝐨𝐫 𝐭𝐡𝐞 𝐃𝐚𝐭𝐚 𝐕𝐚𝐥𝐮𝐞 𝐂𝐡𝐚𝐢𝐧 🧬 Let’s go back to the conversation of pipeline-first vs. data-first. In pipeline-first, the failure of P1 implies the inevitable failure of P2, P3, P4, and so on…In data systems, defensive programming is not just about protecting your pipelines from the cascading impact of failure, but also protecting your system from assumptions. Today, the biggest assumption is pipeline-first coupling of separate concerns. If one unit fails (say extraction), the other unit (say transform) would also cascade into failure without any fault of its own doing. 𝐒𝐞𝐩𝐚𝐫𝐚𝐭𝐢𝐨𝐧 𝐛𝐲 𝐁𝐫𝐢𝐧𝐠𝐢𝐧𝐠 𝐃𝐚𝐭𝐚 𝐢𝐧 𝐭𝐡𝐞 𝐌𝐢𝐝𝐝𝐥𝐞 🗡️ In the ecosystems we design, data is the decoupling layer. We don’t tie one logic unit to another. We don’t let transformation fail just because data didn’t arrive at 3:07 AM. Instead, our transformation pipelines ask a straightforward question: “Is the data ready?” If yes, they run. If not, they wait. They don’t trigger a failure cascade. 𝐓𝐡𝐞𝐲 𝐝𝐨𝐧’𝐭 𝐭𝐚𝐧𝐤 𝐲𝐨𝐮𝐫 𝐒𝐋𝐎𝐬. The defensive system would enforce the guardrail in a way that you’re propelled to think in this logic, and consequently, bask in the luxury of trust and green SLOs built on a defensive foundation. #DataPlatform

After years of hearing organizations ask, “Can we trust this data?”—even after massive investments in governance—it's clear we need a different approach. As data volume and velocity grow, and the business depends more than ever on trustworthy data, we have to stop reacting to data problems after the fact. We need to prevent them at the source. That’s why forward-thinking CDOs are shifting from reactive governance to proactive, contract-driven approaches that guarantee consistency and quality. Here are three strategic moves I see making the biggest impact: → Create data contracts first using shift left principles → Automate compliance with governance embedded at the source → Evolve your data team from firefighters to innovation enablers Starting with data contracts sets a foundation of trust. When governance is built into the contract and travels with the data through CI/CD pipelines, compliance becomes self-enforcing. No manual policing—just clean, reliable data products that accelerate innovation instead of slowing it down. I wrote about how CDOs can start a governance transformation in this CDO Magazine article. https://lnkd.in/eW7gSJ5m When it comes to data, do you feel like you are constantly fighting fires? #DataGovernance #DataReliability #CDOMagazine