Data Governance in Engineering Projects

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!

🔓 Unlocking the Power of AI Through Data Governance: The Role of ISO42001 AIMS🔓 In discussions around AI, much of our focus is often on models, algorithms, and innovation. But what underpins these systems? The answer lies in a foundation often too overlooked: data governance. For organizations pursuing the deployment of an #ISO42001 based #AIMS, data governance is absolutely essential. ➡️ Why Data Governance Matters in AI AI systems are only as good as the data they consume. Poor data quality, biased datasets, or lack of provenance can compromise the integrity of AI outcomes, leading to unreliable insights, reputational harm, or even regulatory breaches. A robust data governance framework ensures data integrity, compliance, and trustworthiness, addressing key challenges such as: 🔸 #Bias and Representation: Without proper data governance, critical questions about data representativeness go unanswered. This leaves organizations vulnerable to producing biased AI models that perpetuate inequities. 🔸 Compliance and Accountability: Regulatory frameworks like the EU AI Act and voluntary standards like ISO42001 require demonstrable governance processes. Organizations must show how data is managed, processed, and protected at every stage of the AI lifecycle. 🔸 Data Lifecycle Management: AI systems rely on dynamic datasets. Data governance ensures every phase—from acquisition to decommissioning—adheres to organizational standards for quality and security. ➡️ Integrating Data Governance into ISO42001 AIMS ISO42001 provides a structured approach to managing AI risks, focusing on transparency, accountability, and ethical use. Data governance plays a pivotal role across its implementation, directly aligning with the standard’s principles: 🔸 Transparency Through Provenance: #ISO5259 highlights the importance of tracking data provenance. Provenance tells us who created the data, how it was modified, and how it has been used. Incorporating these records into your AIMS builds trust and auditability. 🔸 Quality Assurance: Adopting a data quality framework (as outlined in ISO5259-1) ensures that your datasets meet the necessary benchmarks for accuracy, completeness, and relevance. This improves AI model performance and mitigates risks. 🔸 Ethical Guardrails: Data governance enables organizations to monitor and address ethical concerns by embedding accountability measures within AIMS, ensuring datasets do not inadvertently harm or discriminate. ➡️ The Path Forward: The Data Governance Culture Implementing data governance within an AIMS requires both technical measures and a cultural shift: 🔸 Leadership Buy-In: Leaders must view data governance as an enabler of AI excellence, not a compliance burden. 🔸 Cross-Functional Collaboration: Data governance spans legal, technical, and ethical domains, necessitating collaboration across teams.

The worst way to introduce data governance: Schedule a meeting, read a policy page and call it a day. The biggest challenge in governance is being taken seriously by teams. Analysts, engineers, and data scientists skim it, ignore it, and move on. Keep these three things in mind from now on: ➤ Context matters: Show analysts how consistent naming conventions prevent errors in storage. Explain to engineers how access policies reduce rework and security incidents. ➤ Use clear, simple language: Avoid legal or technical jargon. ➤ Engage continuously: Run workshops on real tickets where governance rules apply, hold office hours for questions, and use concrete examples from ongoing projects. Governance can’t live on paper — it needs to be practical and directly relevant to the work teams are doing every day.

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

Many organizations are adopting a "shift-left" mindset when it comes to data governance. So what exactly does this mean? "Shift left" refers to a proactive approach where governance practices are integrated earlier in the data lifecycle, typically starting from the data creation or acquisition phase and moving towards the data consumption and analysis phases. (While this is the latest branding, I can't help but think of Matthew Roche's Maxim on data transformation already covering the general concept 🤷♂️ ). Applying the shift-left approach involves bringing governance closer to the data source and promoting a culture of responsibility and awareness across the organization. Here are some of the key aspects of a shift-left approach: 1️⃣ Early Integration of Governance:   - Instead of addressing data governance concerns only after data has been collected or analyzed, the shift-left approach involves integrating governance practices at the inception of data-related activities. This helps identify and address issues early in the data lifecycle. 2️⃣ Embracing Data Producers:   - Data producers (some consider data engineers Producers, some consider Producers the business users or systems that actually generate operational data) are brought into the analytical data world to help show the downstream impacts of their work. This improves accountability and ownership upstream. 3️⃣ Data Quality at the Source:   - Emphasis is placed on ensuring data quality at the source. By addressing data quality issues early in the process, organizations can avoid downstream problems that may arise if poor-quality data propagates through various stages. 4️⃣ Increased Collaboration:   - Shift left encourages collaboration between different teams involved in the data lifecycle. Data governance becomes a shared responsibility among data engineers, data scientists, data stewards, and other relevant stakeholders, fostering a collaborative and cross-functional approach. 5️⃣ Automated Governance Controls:   - Automation is leveraged to embed governance controls directly into data pipelines and workflows. This can include automated checks for compliance, data quality, and security, reducing the need for manual intervention and ensuring consistent adherence to policies. By embracing the shift-left concept, organizations can build a more resilient and proactive data governance framework. This approach aligns with the broader trend of integrating governance into the mesh/fabric of data management practices, ensuring that governance is not an afterthought but an integral part of the entire data lifecycle. #datagovernance #shiftleft #activemetadata #data #analytics #datamesh #ai #genai Atlan