How can I analyze unstructured data effectively?

To analyze unstructured data, first, clean and preprocess it using techniques like tokenization, stopword removal, and text embedding for text data, or OCR for images. Convert it into a structured format using NLP, machine learning, or clustering techniques. Store processed data in AWS S3, Snowflake, for easy retrieval. Use visualization tools like Power BI or Qlik Sense to derive insights. Automate workflows with Python and deploy models using Flask or FastAPI for real-time analysis.

How do you optimize performance in large Power BI reports?

Use aggregations, DAX optimizations, star schema modeling, removing unnecessary columns, using measures over calculated columns, and DirectQuery mode when needed.

How do we ensure data accuracy and integrity in a BI system?

Implement data validation rules, ETL checks, referential integrity constraints, automated audits, and reconciliation processes to ensure data consistency across systems.

How do we handle real-time data analytics in BI?

Use streaming data sources (Kafka, Azure Stream Analytics), DirectQuery in Power BI, incremental refresh, and in-memory processing for real-time insights.

How do you handle KPI tracking across multiple business units?

Use standardized metrics, centralized data warehouses, role-based access, and dynamic filtering in BI tools like Power BI and Tableau for customized reporting.

How do you implement security and access control in a BI environment?

Use row-level security (RLS), column-level security, data masking, OAuth authentication, and access control policies to restrict sensitive data based on roles.

How do you treat outliers in a dataset?

Detect outliers using Z-score, IQR (Interquartile Range), or Isolation Forests. Handle them by removal, transformation (log/square root), or capping (winsorization) depending on the business case.

How do you handle seasonality and trends in time-series forecasting?

Use decomposition techniques to separate trend, seasonality, and residual components. Models like ARIMA, SARIMA, and Prophet help capture patterns. Feature engineering, such as lag variables and rolling averages, improves prediction accuracy.

How do you determine the most important features in a predictive model?

Use feature importance techniques like SHAP values, Permutation Importance, Recursive Feature Elimination (RFE), or LASSO regression to analyze feature contributions and eliminate redundant ones.

How do you handle imbalanced datasets in classification problems?

Use SMOTE (Synthetic Minority Over-sampling), undersampling, cost-sensitive learning, or adjusting class weights in models like XGBoost or Random Forest to improve classification accuracy.