IFM Brasil Case: QlikView App Optimization

Case of QlikView Application Development for Data Maintenance and Standardization for More Efficient Analyses.

Cadastra

Marketing Team

May 12, 2023

IFM Brasil started in 1997 and over the past 20 years has established itself as a robust company with over 70 employees. The company is one of the largest manufacturers of sensors and industrial controllers located in countries of significant importance in the market. IFM's global reach ensures that customers can rely on local support worldwide.

The financial sector of IFM faced significant challenges in standardizing and organizing the company's information and data. Consequently, the company sought our Data & Analytics team to develop a solution capable of addressing issues such as extensive data models, repeated tables, lack of documentation, and process failures.


Through data standardization and organization, we initiated a QlikView application optimization project.

To address the challenges presented by IFM Brasil, our Data & Analytics team started the project by mapping the key issues and bottlenecks for the QlikView application. After a 3-week immersion with the client, we gained an understanding of their activities and created a solution incorporating best practices for the finance team.

Our main objective in this challenge was to instill a data culture within the department, focusing on the optimal use and handling of all data within the company. The result was to ensure that each team member could have a comprehensive and more analytical view of the company through the developed application.

The project included comprehensive documentation, enabling IFM Brasil to train new team members in a standardized and highly accurate manner within the new model.

Project Architecture

Results

The delivery of the QlikView application yielded significant gains for IFM Brasil through the maintenance of data models, making the company's analyses more efficient through the standardization of the entire data extraction and processing process.

  • Precision in data extraction and transformation processes;

  • Efficiency with the new documentation for data analysis.