The modernization of existing data storage systems is carried out by evaluating and migrating to modern, scalable cloud architectures. This includes the implementation of Data Lakes and Data Warehouses with the purpose of optimizing processing capacity and ensuring a flexible and powerful data foundation for the development of advanced AI models.

This service focuses on implementing Business Intelligence (BI) platforms for the interpretation of historical data. Custom dashboards and reports are created to transform large data sets into key metrics and clear visualizations, thus facilitating performance monitoring and informed operational decision-making.

Data Engineering and Quality (DataOps) is dedicated to building and maintaining robust, automated data pipelines. The primary goal is to ensure the efficient ingestion, cleaning, transformation, and availability of data, applying the DataOps methodology to guarantee the maximum quality and reliability required by all Data Analytics processes.

By using Machine Learning and AI techniques, this service builds predictive models that anticipate future trends and operational outcomes. Additionally, prescriptive analytics is employed to recommend the optimal actions based on projections, resulting in a maximization of the strategic impact of the information.

The service specializes in developing technical solutions for the capture and immediate processing of massive data streams generated at high velocity. This capability is essential for applications that demand instant decisions, such as streaming analytics or the detection of operational anomalies in real-time.

Data Governance and Strategy involves defining policies, procedures, and standards for the integral management of the organization's data assets. Its purpose is to ensure regulatory compliance, privacy, and security, while also defining a strategy to maximize the value of data as a business resource.