Cases de Sucesso
ST IT Cloud
Projetos de Sucesso com Nossos Clientes
DESAFIOS E SOLUÇÕES
Setor de Tecnologia
Estruturação, Centralização e Desenvolvimento de Dashboards e Relatórios utilizando Amazon QuickSight
Technology
Data Analytics / Amazon QuickSight / Machine Learning
Challenge
Establish a centralized and standardized process for the development of more complex dashboards and reports, aiming to unify the tools and platforms used and provide real-time access to the results for consumption.
Solution
Implementamos novos processos, realizamos migração e centralização dos dashboards e relatórios, além de construir novos novos painéis usando algortimos de Machine Learning incorporados na ferramenta Amazon QuickSight.
These actions have significantly contributed to improving the quality and speed of the information made available for business decisions.
- Cost reduction
- Amazon QuickSight como ferramenta central
- Embedded Dashboards - Integrated into the client portal
- Centralization of visualization dashboards and reports
- Forecasting charts
- Anomaly detection
Setor Seguros e Educação
Development of Machine Learning for Automatic Document Analysis and Data Extraction
Technology
Machine Learning / Cloud / AWS / Amazon SageMaker / Amazon Textract / Amazon Rekognition / Amazon Comprehend
Challenge
Perform enhancements in the process of manual data analysis and availability in the system. Optimize the time for evaluation and validation of documents more efficiently.
Solution
Developed a highly efficient model capable of analyzing, validating, classifying, enhancing, cropping, and automatically extracting text from images, resulting in a significant reduction in the time and effort required to perform these analyses, as well as improving the quality of the results.
- Security
- Approximately 90% reduction in analysis time
- Time and cost optimization of the operation
- Automation through API
- Increased reliability
- Reduction or elimination of inconsistencies in the analysis
Setor Agrícola
Deployment of a Serverless Data Lake on AWS
Technology
Data Analytics / Cloud / AWS / Data Lake / AWS Glue / Amazon RedShift / Amazon Comprehend
Challenge
Reducing costs in the cloud, creating a suitable and scalable data environment to meet the company's needs, implementing more effective data governance, and centralizing partner data.
Solution
Deployment of a serverless Data Lake on the AWS cloud platform, with simplified integrations and centralization of data from various sources and partners through APIs.
- Security
- 60% reduction in cloud costs
- Effective data governance
- Highly scalable environment
- Near real-time dashboards
- Data ingestion automation
- On-demand storage and processing
- Data prepared for Machine Learning
- Integration with partners via API
- Data centralization
Setor Educação
Technology
Machine Learning / Cloud / AWS / Amazon SageMaker / AWS Glue / Step Functions / Amazon Neptune
Challenge
Development of a course recommendation engine, through the analysis of data from the institution's student database.
The data necessary for the development of this project was provided by the educational institution, with the support of public data.
Solution
Development of a course recommendation engine, through data analysis. Tables of cities, municipalities, IBGE, areas of knowledge, correspondence between areas and courses, and year of completion were considered. To facilitate the creation and execution of graph-based applications, we used Neptune.
Setor Energia
Technology
Machine Learning / Cloud / AWS / Amazon SageMaker / AWS Data Migration Service (DMS) / AWS Glue / AWS Athena / Amazon QuickSight
Challenge
Identify new patterns of consumption variation, categorize consumers, automate risk analysis, and expand the product portfolio.
Solution
We developed Machine Learning models to identify new consumption variation profiles, categorize consumers, automate risk analysis, and expand the product portfolio. We used public data from CCEE (Electric Energy Trading Chamber) to classify consumers' consumption variation profiles based on their CNPJ (Brazilian corporate tax identification). Then, we applied metrics provided by the client to establish rewards, discounts, or carry out targeted marketing actions for these customers.
Setor Educação
Technology
Machine Learning / Cloud / AWS / Amazon SageMaker / AWS Glue / Step Functions / Amazon Neptune
Challenge
The objective of this project was to detect the likelihood of student dropout through the analysis of various available data.
Solution
We used Machine Learning model training techniques to assign each student a probability of dropout score based on their profile. The data used was provided by the educational institution and included financial information, attendance records (entry and exit times), and the student's academic history at the institution.
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