Technology
Azure Data Factory
Type
Data Warehouse
Industry
Energy
What did they need?
To structure scattered manual information, represented by over 500 Excel files.
To integrate independent and unrelated processes, also known as data silos.
To correct inadequate practices in field naming within datasets.
To eliminate duplicates and null records in the datasets.
How did we support and what was achieved?
- The elimination of duplicate and null data has significantly improved data reliability, providing a solid foundation for informed decision-making.
- Integration and correlation of data across all areas have enabled more effective governance over the company’s information, improving coordination and alignment of processes.
- Customized relational models for each area have optimized responsiveness to specific business needs, allowing for more accurate and detailed analysis.
- Availability of all information for different areas has facilitated collaboration and knowledge sharing among departments, promoting a culture of teamwork and operational efficiency.