Self-service analytics is about empowering users to explore and visualize data without relying on a data team, reducing bottlenecks, and fostering a data-driven culture. A self-service analytics platform should support both technical and non-technical users, providing curated dashboards, drill-down capabilities, and exploration tools. When done well, it fosters faster decision-making, healthier data literacy, better resource utilization, and an empowered organization. However, poor connection to data sources, missing or inconsistent data models, defaulting to BI tools that aren't built for exploration, no plan for data governance, skipping documentation and data lineage, and not supporting advanced workflows can lead to chaos. Foundational practices include building clean, reusable data models, integrating trusted data sources, centralizing access, promoting user experience, creating clear documentation, and making space for iteration. Intentional implementation of features like AI-powered tools can streamline analysis, reduce repetitive tasks, and surface insights faster without compromising the rigor of workflows. Ultimately, self-service analytics aims to make analytics part of everyone's daily workflow, empowering stakeholders while giving data teams the space to focus on strategy, modeling, and proactive insight generation.