How trusted data improves project execution

At Colgate, we once planned a migration using the same process we'd used three times before. It blew up on us. Why? Because the system had 20% more load, 50% more users — and we didn't find out until go-live. That's when I stopped trusting assumptions and started trusting live data. I've been doing SAP migrations for 25 years now. The players change. The destinations change. But the problem stays the same: How do you move critical business systems without breaking everything? Most companies still do assessments with spreadsheets. Think about that for a second. You're planning a $10 million project that could shut down your entire supply chain... based on static documents. What typically happens: Three system integrators bid for your assessment. They all ask for the same information. Inventory lists, performance reports, connection diagrams. They disappear for two months analyzing your data. They come back with proposals that look roughly similar. But by the time they present to management, your system has changed. You've added users, rolled out features, grown your database by 30%. The data they analyzed is stale. So you get proposals based on yesterday's reality for tomorrow's migration. I've seen Fortune 100 companies go dark for days because of this disconnect. Imagine Colgate's entire supply chain stopped. No toothpaste on Walmart shelves. No products moving through distribution. All because we trusted old data instead of live data. The solution isn't complicated: Create a live data room for your migration assessment. Not spreadsheets. Not PDFs. A connected dashboard that updates daily. When vendors need information, they access the platform. When analysts need metrics, they run reports. When timelines change, everyone sees current data. This approach cuts assessment time in half and reduces migration risk by 80%. Because the biggest risk in any migration isn't technical complexity. It's operating on outdated assumptions. Your business doesn't stand still for six months while you plan a migration. Neither should your data. Have you experienced a migration based on stale data? What happened?

Uncomfortable truth: Most project delays are preventable. The real culprit? An unrealistic initial forecast built on hope, not data. Dates established at the beginning carry a great deal of weight. Here’s how to avoid that trap before your project even begins: A project is only as strong as its initial forecast. No amount of technology, clever methodology, or last-minute heroics can save a schedule that was doomed from the start. The key to success lies in building a rock-solid foundation—one grounded in reality, not wishful thinking. Here’s how you can get it right from day one: ✅ Commit to accuracy. Start with data, not guesswork. Use reliable historical data, performance metrics, and forecasting tools to create a realistic first forecast completion date. ✅ Align your team. A misaligned team is a recipe for disaster. Get every stakeholder on the same page about timelines, constraints, and deliverables. Shared ownership equals smoother execution. ✅ Plan for reality—not hope. Hope is not a strategy. Your schedule must reflect actual productivity rates, potential risks, and real-world constraints. The bottom line? Successful projects don't happen by accident-they're built with care, foresight, and collaboration. P.S. If this resonates with you, share ♻️ to help others avoid costly scheduling mistakes.

It’s rewarding to see a nearly 200-year-old institution reimagine itself for the AI era. During a recent conversation with the team at Citizens Bank, I was impressed by how they're shifting data management from a back-office function into a strategic competitive advantage. This example also reminded me that the most powerful transformations happen when you build on a foundation of trust.     Citizens took a bold approach with their master data management (MDM) modernization. They moved from batch processing that took days to near real-time data synchronization across their 1,000+ branches in 14 states. Using Informatica's Intelligent Data Management Cloud (IDMC) platform on Amazon Web Services (AWS), they've reduced data onboarding time by approximately 85% and transformed MDM into what they call a "Tier 1" operational asset, meaning it’s always available, accurate and ready to power customer interactions.    The results speak volumes. What used to take three days — even something as simple as updating a customer's phone number — now happens instantly. Their contact center call volumes decreased, their mobile experience became seamless and every customer interaction now draws from a single, trusted source of truth.    What I find particularly compelling is how Anand Vijai M R and his team built flexibility into their architecture while maintaining consistency across every customer touchpoint. The cloud-native approach freed their teams from infrastructure complexities so they could focus on what truly matters: ensuring data accuracy and powering AI use cases. With CLAIRE as an AI copilot, they've democratized access to trusted data across the organization.    This is the transformation I'm seeing across industries. Organizations that treat data as a strategic platform are building sustainable competitive advantages in the AI era. For a bank with roots dating back to 1828, Citizens proves that innovation and tradition can coexist harmoniously. https://lnkd.in/gZCua-M9  

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

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.