Data Modernization by Minsait

Data Modernization in the cloud can be an effective strategy to optimize data management in business environments. Data cloud migration can enable faster access, scalability, greater flexibility, and advanced analytics capabilities, which can improve decision-making and operational efficiency.

Additionally, modernizing data in the cloud can enable the adoption of emerging technologies such as artificial intelligence and machine learning to obtain valuable insights and predictive analytics from data, driving innovation and competitive advantage in the market. However, it is important to consider aspects of security, privacy, and regulatory compliance when migrating data to the cloud, as well as account for associated costs and proper planning to ensure a successful implementation.

That's why, through the partnership between Minsait and AWS, we assist our clients in the challenge of data modernization in the cloud through the following tracks:

1- Strategy: We design a strategy that aligns business and technology, sharing efforts for a common goal

2- Data Foundations:
We enable the best possible environment over AWS to support the client's business cases through the Well Architected Framework methodology

3- Automation & Optimization: Automation of the entire data lifecycle, ingestion and AI, with DataOps & MLOps using services like CodePipeline, CloudFormation, Lambda, Cloudwatch, etc. Cloud cost optimization (FinOps)

4- Governance: We guarantee data quality, security and accessibility through the transformation of organization, processes and culture through services like Glue or Lake Formation

5- Migration (Data Switching): We take our customers to the cloud (Redshift, Aurora, etc) to capitalize on the time-to-market, pay-per-use and unlimited processing advantages of the Cloud

6- Monetization (Use Case): We bring intelligence closer to the business areas in an "understandable" way through analytical models and visualization of business metrics using Quicksight or/and Sagemaker.

Examples:

• Fraud Detection
• Predictive analysis of credit risk
• Personalized marketing
• Next best action
• Demand prediction
• Optimizing Supply Chain
• Automation of accounting processes