Building Trusted Data Before Cloud Deployment

Too many enterprise programs still treat privacy as a policy checkbox. But privacy - done right - isn't simply about compliance. It’s about enabling confident, ethical, revenue-generating use of data. And that requires infrastructure. Most programs fail before they begin because they’re built on the wrong foundations: • Checklists, not systems. • Manual processes, not orchestration. • Role-based controls, not purpose-based permissions. The reality? If your data infrastructure can’t answer “What do I have, what can I do with it, and who’s allowed to do it?” - you’re not ready for AI. At Ethyca, we’ve spent years building the foundational control plane enterprises need to operationalize trust in AI workflows. That means: A regulatory-aware data catalog Because an “inventory” that just maps tables isn’t enough. You need context: “This field contains sensitive data regulated under GDPR Article 9,” not “email address, probably.” Automated orchestration Because when users exercise rights or data flows need to be redacted, human-in-the-loop processes implode. You need scalable, precise execution across environments - from cloud warehouses to SaaS APIs. Purpose-based access control Because role-based permissions are too blunt for the era of automated inference. What matters is: Is this dataset allowed to be used for this purpose, in this system, right now? This is what powers Fides - and it’s why we’re not just solving for privacy. We’re enabling trusted data use for growth. Without a control layer: ➡️ Your catalog is just a spreadsheet. ➡️ Your orchestration is incomplete. ➡️ Your access controls are theater. The best teams aren’t building checkbox compliance. They’re engineering for scale. Because privacy isn’t a legal problem - it’s a distributed systems engineering problem. And systems need infrastructure. We’re building that infrastructure. Is your org engineering for trusted data use - or stuck in checklist mode? Let’s talk.

💡 Every company says they want to be AI ready. But AI success does not start with models. It starts with preparing the data. In my role leading strategy for private cloud, I have seen the same journey repeat across enterprises: Stage 1 – Discover: Know what data exists. Inventory and assess quality. Stage 2 – Integrate: Break silos. Consolidate into unified pipelines. Stage 3 – Clean & Transform: Fix the mess. Correct errors and standardize formats. Stage 4 – Govern: Make it safe. Compliance, access, and security. Stage 5 – Enrich: Add value. Derive features and external context. Stage 6 – AI Ready: Enable outcomes. Model ready datasets for training and production. The steps are clear, but execution at scale is where most organizations stall. Silos, legacy infrastructure, and inconsistent governance get in the way. And the hardest part is often cultural. People protect their information, yet sharing data is what creates opportunities. Building trust so teams are willing to share is critical to moving forward. 👉 Where is your organization on this journey? Still discovering and cleaning, or already enabling AI outcomes?

Some data headaches are really just trust issues in disguise. Let me explain: I once met with a medical device company’s President who complained endlessly about their on-prem ETL failures and nightly data fires. On the surface, it was a purely technical problem - broken scripts, crashing servers, and no backup plan. But as I asked more questions, I realized the true pain was deeper... Nobody trusted the numbers. Reports conflicted, definitions varied, and decisions were stalled or based on gut feel. In short, they had no data management strategy. Every stakeholder boiled their frustrations down to “broken servers,” when the real issue was the foundation. Helping the president of the company see this issue helped us close the deal. So, here’s the takeaway... Before you dive into code fixes, pause and ask: Do people actually trust these numbers? If they don’t, no amount of faster queries will solve the real problem. Build trust by: 1. Defining consistent metrics and ownership 2. Establishing lightweight data governance (even a small team can make a big difference) 3. Validating data end-to-end to ensure accuracy Fix the foundation first, and the rest will follow. ♻️ Share if you know a data leader who needs to address the trust gap. Follow me for more on building data strategies that drive real business impact.

Don’t just “lift and shift” your data. It’s tempting, I know. You’re moving systems, launching new software, migrating to the cloud… and someone says, “Let’s just move the data across and clean it later.” 🚨 Red flag alert! 🚨 That’s like packing up a messy house without decluttering. You’re not just moving; you’re dragging all the problems with you. Duplicates, typos, misclassified suppliers… all the gremlins come too. 👉 Clean before you shift 👉 Organise as you go 👉 Start your new system the right way Put that data COAT on, and keep it on, and make sure your data is Consistent, Organised, Accurate and Trustworthy. Otherwise? You’re paying good money to carry chaos into your shiny new tech.

Before you roll out AI-driven products or analytics, ensure your data is rigorously classified, secured, quality assured, and well understood. That’s non‑negotiable for innovation that won’t collapse under weak foundations. The smartest organizations I see aren’t just investing in models—they’re building trust fabrics across their data ecosystems: real-time lineage, quality scoring, policy enforcement, and business-aligned data ownership. Not for compliance but for resilience, velocity, and impact. This is the new battleground for differentiation. It’s time to stop treating data like exhaust and start treating it like infrastructure. Because the foundation we build today will determine whether AI becomes a force multiplier—or a massive liability. #DataGovernance #DataSecurity #AILeadership #DSPM #DigitalTrust https://lnkd.in/gsYiWU-w