- It has a visual interface that allows me to see the structure and logic of my models, the relationships and dependencies between variables and calculations. - It supports intelligent arrays, which let me manipulate multidimensional data without worrying about syntax or indexing errors. - It allows me to create and reuse user-defined functions that can be stored in libraries and used by my team members for their models. - It offers quasi-Monte Carlo simulation sampling methods (such as Latin hypercube and Sobol sequences) to generate accurate results with fewer samples for some of my models. - It has a robust implementation of the Metalog distribution, which lets me fit data accurately and with ease. - It supports various sensitivity analysis functions, allowing me to examine the effect of input variables on output variables. - It offers batch processing so my models can run as part of an automated workflow that integrates other technologies like Python or R for pre or post-data processing. - It allows me to publish models to the Analytica Cloud Platform, allowing users to interact with my models using a web browser.

The joint powers of graphical modeling, advanced computation, stats, and simplicity of setting up the tool and new models.

Analytica is intuitive, user-friendly, convinient and always provides most accurate results. I love how easy it is to review results of analysis tools which includes interactive maps, graphs and tables. Analytica provides data modelling features which allow you to predict outcomes and make decisions based on the data history. It is easy to use and it is a real-time solution that simply connects to various large data sources and has a lot of functions available to use. The visual model is amazing.