Highlights
- Moving data operations to cloud offers enterprises the opportunity to combine native AI and data services to optimize for customer-centricity and create a Machine FirstTM data architecture on cloud.
- TCS solutions bring a Machine First approach to data ingestion and archival
- In AWS environments, Amazon SageMaker can be used to build, train, and deploy machine learning models that identify data that is to be migrated.
In this article
Need for speed
Enterprises now need to work with modern digital levers such as social, cloud, mobile and analytics to reach new levels of data efficiency.
To gain more insights from multiple channels of data, they need to shift from traditional databases to cloud-native ones. Data stored across siloes in legacy enterprise platforms have steep license costs. Legacy data platforms are slow, and enterprises find it difficult to ingest, process and analyze data in real time, and miss time-sensitive customer interactions and engagement.
Traditional databases lack the flexibility to run analytics on new data types at the speed demanded by hyper-interactive customers. Lack of agility in maintaining legacy databases hinder business operations and have pivoted organizations toward cloud-based solutions. According to Gartner, 75% of all databases will be deployed on a cloud platform by 2022, and only 5% will be considered for repatriation to on-premises. Customers across industries are now looking to enhance business use cases with better data insights by leveraging AI to accelerate data operations for accelerated business decision making.
Why Machine First
Enterprises need to adopt cloud-based data platforms to meet the new world needs of cognitive analytics and insights, data monetization, real-time processing, next-gen visualization, self-service, guided analytics and enterprise data security. Moving data operations to cloud offers enterprises the opportunity to combine native AI and data services to optimize for customer-centricity and create a Machine FirstTM data architecture on cloud.
AI and machine learning in data modernization
Enterprises that treat data as a strategic asset can take full advantage of the promise of AI and machine learning. In order to organize and analyze data in a cost-effective manner, enterprises must first migrate legacy, on-premises data warehouses to cloud such as AWS cloud, leveraging AI and machine-learning capabilities. There are four aspects that we use to realize the AI-ML advantage.
Automation is key: TCS takes a Machine First approach to build data lakes rapidly to ensure return on investment. Right automation initiatives ensure data is holistic, accurate and as near real-time as possible. The right data architecture for data lakes encourages democratization of data to be used across a partner ecosystem. In this approach, automation comes first at all stages of the data modernization journey.
Archival of data lakes: TCS helps enterprises leverage AWS platform to archive data lake provisioning, and AWS RedShift to migrate enterprise data warehouses and jump start the database migration process.
Build cloud-compatible databases: TCS data estate solution accelerates provisioning and processing of complex incoming data. Database refactoring changes the table structure of database schema to improve the consistency and usage of the value stored in the database. This approach is to make database compatible with the latest cloud platforms, ensuring scalability and flexibility without changing semantics. TCS solutions bring a Machine First approach to data ingestion and archival, covering data transformation or processing, semantic analytic layer, data migration and data validation.
Phased migration: TCS takes a phased migration approach that leads to efficient, optimized and cost-effective solution. In AWS environments, Amazon SageMaker can be used to build, train, and deploy machine learning models that identify data that is to be migrated. AWS Glue is used to create a data catalog for the migrated data. Finally, Amazon Athena and Amazon QuickSight are used for business analytics and visualization.
The compound effect
Enterprises can build a data analysis layer on top of an enterprise data warehouse (EDW) such as AWS Redshift to draw real-time business insights. AWS provides high flexibility in deployment options and target architectures for database workloads. Amazon Redshift ML helps embed predictions like fraud detection, risk indexing, and customer churn directly in queries and reports. Engineers can make use of SQL function to apply ML models to your data in queries, reports and dashboards.
A new generation of business intelligence and machine learning services built on AWS along with AI-ML enables industry-best data performance. Out-of-the-box TCS accelerators have enabled a quick start for new apps in customer environments by providing ready-to-use engines that are pre-trained with relevant schemas. These, in turn, will help AI-related data implementations ensuring improved time to market.
