Data Collection & Extraction: Querying databases, APIs, and data warehouses (e.g., using SQL) to pull relevant datasets. Data Cleaning & Wrangling: Removing duplicates, handling missing values, and formatting raw data for analysis. Exploratory Data Analysis: Identifying trends, anomalies, and correlations using statistical methods and tools like Python or R. Dashboarding & Visualization: Building interactive reports and tracking tools using platforms like Tableau, Power BI, or Looker. Stakeholder Reporting: Translating complex technical findings into clear, actionable business recommendations for non-technical teams. Performance Monitoring: Establishing key performance indicators (KPIs) and tracking business metrics over time. Process Optimization: Partnering with data engineers and business units to streamline data pipelines and query performance.