To improve data analysis, many companies are looking for the right methods for decision making. Reporting tools like Microsoft Power BI provide simple and accessible solutions in different areas. If you are using reporting tools like Power BI, you will surely see all the features and updates to save you time and optimize your reports.

Customer satisfaction is a real concern for many companies. In order to gain insight into customer data sources and better target marketing campaigns, CRM teams are increasingly relying on data analysis methods. Dynamics 365 Customer Insights provides solutions based on Artificial Intelligence to help companies understand the data and the customer journey.

Given the strategic nature of data, data governance is essential to strengthen the security of an organisation. Compliance and usage planning drives business results. In the face of these challenges, line-level security in Power BI is a solution for managing roles and access to data.

Filtering is one of the most common methods of data modelling. Without specific filtering, viewing a multi-page report can be difficult to manage. Using DAX filters, especially for financial statements, simplifies data sharing and visualisation.

An interactive dashboard allows users to monitor their progress in real time. Depending on the level of data visualisation, key performance indicators (KPIs) can communicate information about trends, relationships and gaps between information. In Power BI Service, a KPI dashboard can be created for continuous monitoring of your goals.

For all ERP systems, the management and export of data to other applications requires specific governance. In Dynamics 365 Finance and Operations (D365 FinOps), data can be quickly exported and stored in a DataLake before being enhanced. Export to Azure Datalake is one of the recommended steps in the implementation of a BI architecture.

From a strategic point of view, data is the central element of an organization: more data collected means more processing and storage space. Choosing a data lake allows to centralize all data before enriching and structuring it. In the implementation of a modern BI architecture, the transition from a data lake to a data warehouse is the second step before data visualization.