Power BI

The CEO needed centralized access to information without having to apply multiple filters to find what he required.

There were no integrated databases to enable cross-referencing of information across different dashboards, making data consolidation difficult.

Data from various existing dashboards was downloaded in CSV format and a unified report was built as a proof of concept to display consolidated information.

This project set a precedent for the creation of cloud-based databases, laying the foundation for the implementation of Microsoft Fabric in the company.

Through this project, I assumed a data engineer role, demonstrating the feasibility of centralizing information for analysis and decision-making.

Provided the CEO with a clearer and more centralized view of apartment rental information.

Demonstrated the value of consolidating data and facilitated the approval of investments in cloud data infrastructure.

Laid the foundation for the future adoption of advanced data analysis and management tools, optimizing the company’s information strategy.

P&L, SG&A, and other figures were downloaded from SAP ECC into Excel files, creating manual processes for review and analysis.

The Finance team created their forecasts (FCST) using a scheme called “1+11, 2+10, 3+9”, where the first number represented actuals and the second the forecast, which complicated data handling.

Each department used its own catalog for financial reporting categories, causing inconsistencies.

Data review required each team to download and analyze files in Excel manually, making the process inefficient and error-prone.

Standardized and unified the financial reporting catalog in collaboration with the Finance department.

Created business rules to properly classify amounts and resolve inconsistencies coming from SAP.

Designed templates for Finance teams to align FCST and plan data, enabling direct loading into Power BI.

Automated the “1+11, 2+10, 3+9” forecast scheme dynamically within Power BI, depending on the month of the year.

Reduced manual work and errors by consolidating and classifying data automatically.

Provided unified access to P&L, SG&A, forecast, and plan data for all areas.

Enabled dynamic, real-time visualization, improving efficiency in financial review and decision-making.

Apartment price information (rent, insurance, services, etc.) was spread across multiple Excel files, requiring manual calculations to provide final pricing to prospects.

Price distribution needed monthly monitoring and trend analysis by building, apartment type, and time period.

Inventory required visibility into price trends and occupancy status at the apartment level.

Extracted apartment status data from SAP through a data cube connection enabled by the SAP team.

Built two standardized Excel databases for teams to input data consistently.

Developed a catalog of buildings and apartments, aligning SAP records with the pricing area’s database for proper identification and tracking.

Improved customer service by providing two price options immediately.

Real-time visibility of vacant apartment status from SAP.

Supported price adjustment and occupancy strategies with data-driven decisions.

Centralized and standardized data, reducing manual errors and streamlining internal processes.

An existing Power BI report on rent and occupancy was not efficient for management use.

Managers had to apply multiple filters to find the desired insights, slowing decision-making.

Tested Power BI Services’ Scorecard feature to calculate KPIs automatically and present data visually.

Designed a simplified Power BI report with only the essential information, making it clear and accessible for management.

Faster access to key rent and occupancy information.

Improved decision-making at the management level through clearer visualizations.

Reduced manual steps and filtering, optimizing management’s time.

All data was originally handled through Excel downloads from the Clientify CRM.

The Sales team often left fields incomplete or filled them inconsistently, limiting data reliability.

Connected Power BI directly to the Clientify API, automating and improving data extraction.

Standardized and cleaned data in Power Query, correcting inconsistencies and aligning with the Sales team to ensure proper field completion.

Created KPIs such as tenant age, gender, number and type of pets, and customer nationality and origin maps, providing detailed strategic insights.

Improved data quality and reliability for analysis.

Generated impactful visual insights, with nationality and origin maps highly valued by the CEO.

Enabled better-informed decisions on customer segmentation, marketing, and commercial strategy.

Sales reports were created in Excel from SAP BI downloads, requiring extensive manual cleaning.

No standardized catalog existed across countries, complicating regional analysis.

Unified sales catalog across countries (Guatemala, El Salvador, Puerto Rico) with consistent classification.

Migrated reports to Power BI for each country, storing historical data in SharePoint for traceability.

Designed new KPIs tailored to each country’s needs, enhancing the depth of analysis. 

Significantly reduced time spent cleaning and preparing data.

Standardized sales information across countries, enabling more reliable regional/global analysis.

Improved decision-making with dynamic Power BI reports and new metrics.

This was my first full Power BI project, developed during a DatData course on Udemy.

It served as a practical introduction to report building, covering data modeling, visualization, and dashboard design.

The report consolidated example data from the course and allowed real-time interaction with visuals.

Unlike other projects in my portfolio (shown only as screenshots), this dashboard remained interactive, demonstrating actual Power BI functionality.

Fundamentals of data modeling and table relationships.

Creation of dynamic visualizations and user-friendly dashboards.

First complete experience across the data cycle: connection, transformation, and presentation in Power BI.