??Optimize BI queries and data models to reduce processing time. ??Use indexing and partitioning to enhance database performance. ??Implement real-time monitoring tools to track system health. ??Regularly clean and optimize data sources to avoid redundancy. ??Leverage caching to minimize query load on databases. ??Distribute workloads efficiently across servers and clusters. ??Collaborate with IT teams to ensure infrastructure scalability. ??Automate maintenance tasks like indexing and query optimization.

When juggling multiple BI system performance issues, the key is to stay organized and tackle the most critical ones first. Start by figuring out which issues have the biggest impact and handle those right away. Then, dig into the root cause using monitoring tools, work closely with the right teams, and apply smart, data-driven fixes. To keep things running smoothly in the long run, automate what you can, optimize queries, and set up proactive monitoring to catch problems before they escalate.

follow these structured steps: 1. Prioritize Issues by Impact – Identify which issues affect critical reports, dashboards, or data processing the most and tackle them first. 2. Analyze System Logs & Queries – Check logs, slow-running queries, and system metrics to pinpoint bottlenecks. 3. Optimize Queries & Indexing 4. Monitor Real-Time Performance – Use monitoring tools like Power BI Performance Analyzer, SQL Profiler, or server logs to track live performance. 5. Implement Caching & Aggregations – Store precomputed results for commonly used queries to reduce processing load. 6. Reduce Data Load – Minimize unnecessary data pulls by filtering and aggregating data at the source before fetching it. Cheers ! ??

- Socialize BI internally. - Communicate within the teams in the company and identify deficiencies. - implement BI on low hanging fruit first, so you get employees to understand the benefits

To resolve BI performance issues effectively, follow a structured approach: 1. Identify & Prioritize: Monitor system performance, categorize issues, and address critical business reports first. 2. Optimize Queries & Data Processing: Use indexing, partitioning, query tuning, and ETL optimization to improve efficiency. 3. Improve Infrastructure & Scaling: Assess hardware, implement caching, and scale resources as needed. 4. Enhance Data Warehouse Architecture: Apply best data modeling practices, optimize refresh cycles, and leverage a lakehouse approach. 5. Governance & Continuous Improvement: Establish BI governance, train users, and continuously monitor for enhancements.