1. The most insidious tech debt I’ve faced is hidden, undocumented data pipelines - especially legacy ETL jobs built by long-gone teams. These cause major business risks: data outages, inconsistent metrics, and inability to trust analytics or AI outputs. The lesson: invest early in documentation, observability, and data lineage tools. Don’t accept “it works for now” - tech debt compounds, and AI models amplify the consequences of bad data. 2. A widely accepted best practice is “centralize all data before enabling analytics.” In reality, this delays value and frustrates business users. Instead, I advocate for a federated approach: enable domain teams to own and serve data products, with strong governance and interoperability standards. This speeds up delivery and makes AI adoption more scalable and robust. 3. My top strategic advice: modernization is as much about people and process as technology. Secure strong executive sponsorship, prioritize change management, and align incentives across IT, data, and business teams. Modern tools and AI will fail if organizational silos and misaligned KPIs persist. 4. AI is a double-edged sword. It helps surface hidden tech debt e.g., with anomaly detection and automated lineage mapping, but it also creates new debt - like model drift, opaque decisioning, and rapidly evolving toolchains. AI’s appetite for high-quality, well-governed data exposes every weakness in your platform. Plan for continuous investment in data quality and model monitoring. 5. Counterintuitive truth: More data isn’t always better. Uncontrolled data growth increases costs, complexity, and risk. Ruthlessly prioritize data quality over quantity, and regularly deprecate unused or low-value datasets. This discipline is critical for sustainable AI and analytics success.