Information-based decision making depends on trust in managed data. This must be achieved by ensuring the end-to-end of the process, from intake to exploitation. It is essential to apply appropriate practices and technologies to the source and types of information that the organization handles, ensuring the necessary transformation, quality and delivery, supporting data generation cases in real time, micro batch or batch.

Information-based decision making depends on trust in managed data. This must be achieved by ensuring the end-to-end of the process, from intake to exploitation.
The storage and processing of data is a fundamental step to ensure the delivery of information for decision support in a timely manner, with distributed processing tools and practices, data management in Data Lake or traditional Datawarehousing.

Information-based decision making depends on trust in managed data. This must be achieved by ensuring the end-to-end of the process, from intake to exploitation.
Analysis is the culminating point in information-based decision making, it is what allows supporting the generation of findings that enable the evolution of the business, through dashboards or indicators that maximize the user experience, the analysis and the exploration, as well as the generation of business alarms or continuous learning with machine learning practices.

Governance is of utmost importance in the creation and execution of a data-based analytical program that enables information-based decision making.
An important part of the governance model is the definition of roles and responsibilities associated with information management throughout its life cycle, from creation to exploitation, ensuring the quality, quantity and time of the data that support decision-making in the organization.