Description

Introduction

Sisense is an advanced business intelligence platform designed to transform complex data into actionable insights. It allows users to connect multiple data sources, build scalable data models, and create interactive dashboards.

Beyond basic reporting, it supports advanced analytics capabilities such as predictive modeling, trend analysis, and custom calculations. Moreover, with its Elasticube engine and in-chip technology, Sisense delivers high-performance analytics even on large and complex datasets. As a result, organizations can move from simple reporting to deeper, insight-driven decision-making.

Learner Prerequisites

• Strong understanding of business intelligence concepts
• Good knowledge of SQL and relational databases
• Familiarity with data modeling and ETL concepts
• Basic experience with dashboards and visualization tools
• Understanding of statistical or analytical concepts (preferred)
• Exposure to cloud or enterprise data systems is helpful

Table of Contents

1. Introduction to Advanced Analytics in Sisense

1.1 Overview of advanced analytics concepts
1.2 Role of Sisense in modern data analytics
1.3 Transition from basic to advanced BI analytics
1.4 Key use cases in advanced analytics
1.5 Importance of data-driven decision-making
1.6 Analytics capabilities in Sisense ecosystem

2. Advanced Data Modeling Techniques

2.1 Designing scalable and efficient data models
2.2 Handling complex relationships and joins
2.3 Using aggregated and calculated tables
2.4 Managing large and multi-source datasets
2.5 Optimizing schema for advanced analytics
2.6 Best practices for model governance

3. Advanced Calculations and Expressions

3.1 Introduction to complex calculations in Sisense
3.2 Using custom formulas and measures
3.3 Working with time-based calculations
3.4 Conditional logic and advanced expressions
3.5 Handling KPIs and business metrics
3.6 Optimizing calculation performance

4. Predictive and Trend Analysis

4.1 Overview of predictive analytics concepts
4.2 Identifying patterns and trends in data
4.3 Using historical data for forecasting
4.4 Implementing basic predictive techniques
4.5 Visualizing trends and forecasts
4.6 Business applications of predictive insights

5. Advanced Dashboard Design and Visualization

5.1 Designing interactive and dynamic dashboards
5.2 Using advanced widgets and filters
5.3 Enhancing user experience with visual storytelling
5.4 Optimizing dashboards for large datasets
5.5 Implementing drill-down and drill-through analysis
5.6 Best practices for executive dashboards

6. Performance and Optimization for Advanced Analytics

6.1 Optimizing advanced queries and calculations
6.2 Managing performance for complex dashboards
6.3 Reducing data load and processing time
6.4 Monitoring analytics performance metrics
6.5 Handling scalability challenges
6.6 Continuous optimization strategies

Conclusion

Advanced analytics in Sisense enables organizations to move beyond basic reporting into deeper, insight-driven analysis. It helps uncover hidden patterns and predict future trends, thereby improving strategic decision-making.Furthermore, by using advanced data modeling, calculations, and visualization techniques, users can extract more value from their data. In addition, optimized performance ensures smooth handling of complex datasets.

Overall, it empowers businesses to build intelligent, scalable, and future-ready analytics solutions.