Advanced Analytics deals with the automatic communication of significant patterns in structured and unstructured data. It goes beyond Business Intelligence by using sophisticated modeling techniques to predict future events or discover patterns. Advanced analysis uses formulas and mathematical and statistical algorithms to generate new information, recognize patterns and also predict results and their respective probabilities. The questions addressed through advanced analysis are segmentation, association, classification, correlation analysis and forecasting.

Understanding your audience and your expectations is crucial to offer the best personalized experiences. Must be able to show interesting insights and key customer characteristics to quickly test experiences and direct them to the right clients. We help organizations to become companies driven by analysis. Our proven experience and extensive experience allow you to monetize data using the three pillars that support the elements of the process, people and technology in your organization.



These pillars also make up three different services
  • Information without borders.
  • General analysis.
  • Progressive organization.
Key benefits of Advanced Analytics
  • Ability for true visibility of the end-to-end supply chain.
  • Possibility of optimizing decision making and manual execution compared to the automatic marketing speed.
  • Lower inventory costs and higher revenues.

Advanced analysis includes components

Data storage
  • Most advanced analytics architectures include Data storage.
  • Data scientists typically need to explore the data to identify its predictive features and the statistical relationships between them and the values they predict.
Batch processing
  • Batch data processing is an efficient way of processing high volumes of data is where a group of transactions is collected over a period of time.
  • Data is collected, entered, processed and then the batch results are produced. Batch processing requires separate programs for input, process and output. An example is payroll and billing systems.
Ingestion of messages in real time
  • Real time data processing involves a continual input, process and output of data. Data must be processed in a small time period (or near real time).
  • Radar systems, customer services and bank ATMs are examples.
Flow processing
  • Stream processing analyzes and performs actions on real-time data though the use of continuous queries.
  • Streaming Analytics connects to external data sources, enabling applications to integrate certain data into the application flow, or to update an external database with processed information.
Analytical data store
  • In a big data architecture, there is often a need for an analytical data store that serves processed data in a structured format that can be queried using analytical tools.
  • It support querying of both hot-path & cold-path data are collectively referred to as the serving layer, or data serving storage.