Analytics and BI Enablement on AWS

Sigma Software, as an Advanced AWS Partner, provides comprehensive Data Analytics and Business Intelligence services to help companies enhance their data capabilities, gain valuable insights from their data assets, better understand the organization's data assets, and gain comprehensive experience when establishing and tracking KPI and custom reports. Aligning with AWS Data Analytics Lens and AWS Well-Architecture Framework, Sigma Software will help customers to implement:

- Cloud-based analytics delivery Provide full assessment and implementation for the full analytical cycle: from getting familiar with data to valuable insights Design and implement scalable analytical architectures for projects of any size using AWS data-related services like AWS Database Migration Service, AWS Lambda, Amazon DynamoDB, Amazon Redshift, AWS Glue - BI Delivery Design and development of rich dashboards and reporting using AWS QuickSight or similar BI tools like Tableau, PowerBI in your AWS cloud environment. Structure and optimize data assets to deliver efficient performance tracking and KPI monitoring Set up and configure user-friendly BI environments for self-service access and reporting - Analytics Migration Help and support with the transition from legacy analytics tools to modern visualization platforms using AWS Database Migration Service or AWS Application Migration Service Migration and modernization of data models and reporting logic for a better user experience, and achieving business value leveraging AWS-native applications like Amazon Kinesis, AWS Glue, AWS EMR Implementation from scratch and integration of existing BI solutions into cloud-based environments with Amazon QuickSight - Advanced Analytics Develop and execute ad-hoc data analysis to address urgent data requests Perform deep-dive analytics to uncover trends and inefficiencies within organizational data using Machine Learning models on Amazon SageMaker Design and deliver analytical solutions for real-time and scenario planning using Amazon Kinesis Data Analytics or Amazon MKS - Data for AI/ML Describe, prepare, and clean data assets against business rules for AI/ML workflows Introduce validation and quality frameworks to increase data consistency and value, and detect data inconsistencies Explore and profile data assets to assess readiness for AI/ML workflows with orchestration services like AWS StepFunctions or Amazon MWAA