Analytics Modernisation and Migration is the shift to an integrated data system, with big data technologies and infrastructure, to provide real-time advanced data analysis and insights.

We help your organization identify, prioritize, and transform the traditional data workloads, and modernize the data analytics and AI fabric using customised unified data infrastructure architecture.

iAM&M Best Practice Overview

• Client shifting their workloads to AWS or GCP or Azure 
• Client shifting traditional database & ETL to cloud data warehouse and inbuilt ETL or customized ETL using Python and data pipelines. This provides data to be remotely updated & synced, share data easily, saving costs on local IT infrastructure.
• Client shifting to fully automation platform / batch or streaming or machine learning operations, artificial intelligence operations, and development operations. This increases efficiency of data processing
• Client shifting to new BI platform that has embedded feature of analytics
• Scalability, Automation & Data Security  

 

Our Approach

• Identify, purview, streamline, and migrate key workloads
• Augment your technology estate to modernize your platform
• Ensure smooth running of analytics workloads requiring high compute or storage resources without disrupting your business operations
• Align flexible deployment options —on-premises, in the cloud or hybrid—with your strategy (e.g., cloud-first)
• Incorporate advanced analytics technology to optimize total cost of ownership (TCO) and the agility of your future environment
• Lower administration costs through managed services and automation
• Dramatically reduce storage costs so you can afford to acquire and leverage new data sources
• Adopt data operations, machine learning operations, artificial intelligence operations, and development operations for better efficiency