Tag: MSP Partner Spotlight

Customer experience is at its best when a customer perceives the experience offered is unique and aligns to their preferences. The need to engage, at a very personal level, becomes key. Learn how Capgemini’s data and analytics practice implements customer intelligence platforms on AWS to help companies build a unified data hub. This enables customer data to be converted into insights that can be used for reporting and building AI/ML predictive analytics capabilities.

There is a huge opportunity to move mainframe applications to the cloud, allowing organizations to leverage benefits like cost reduction, improved agility, and the ability to unlock data to drive insights. Learn how the Atos SyntBots solution accelerates the testing of mainframe migration projects. We’ll also explore how establishing data and functional equivalence between the mainframe and migrated systems is made comprehensive and accelerated using this approach.

To help businesses get the most out of their customer data, CRM tools are used widely to manage data throughout the customer journey, and the exchange of data between two CRMs should be seamless and glitch-free. To better optimize and automate CRM integration with the ACE Pipeline Manager, learn how various AWS services support Labra OppSync from Ibexlabs and help AWS Partners manage their customer opportunities.

As the world continues to respond to COVID-19, Capgemini is working to find ways to prepare our communities for such pandemics in the future. During this extraordinary time, digital solutions have played a crucial role in enabling employees to work remotely. Learn how the prototype showcased in this post was built by Capgemini and enables an organization to keep track of employee movements within the premises, predict employee sentiment, and help the facilities management team monitor and utilize resources efficiently.

For many companies, budget and time constraints, along with shifting workplace dynamics and lack of skilled developers, prevent them from taking the leap in modernizing their legacy applications. Learn how Wipro is taking a step forward to address these challenges and deliver cloud transformation at scale with the Cloud Native Acceleration Platform (CNAP). It provides self-service and enhanced developer experience to help build both greenfield cloud-native applications and modernize legacy applications.

A fundamental component of the Operational Excellence pillar of the AWS Well-Architected Framework is evolving your environment, incorporating continuous improvement over time. Moving to the cloud should be transformational for a business, by adding agility, and making it easier to innovate. Learn how DXC Technology, an AWS Premier Consulting Partner, integrated their ServiceNow instances with AWS Support so they could accelerate ticket resolution via automating ticket processing.

This post describes a solution created by IBM during the migration of a big data and analytics platform for one of the top 10 banks worldwide. The primary drivers were cost efficiency, business agility, and performance. The “pet to cattle” concept was applied to this solution to transform the legacy high availability disaster recovery solution to a more robust and cost-effective cattle-based solution through the use of AWS-native services.

Enterprise data warehouses are complex and consist of database objects that need to be modified to reflect the changing needs of business, data analytics, and machine learning teams. In this post, learn about an approach to managing the evolution of enterprise-scale data warehouses based on the experience of Deloitte’s Data and AI global practice teams. The declarative tool developed by Deloitte that can automatically generate DDL statements to align Amazon Redshift’s state to an approved baseline configuration.

Building effective machine learning models requires storing and managing historical data, but conventional databases can quickly become a nightmare to regulate. Queries start taking too long, for example, slowing down business decisions. Learn how to use Amazon Redshift ML and Query Editor V2 to create, train, and apply ML models to predict diabetes cases for a sample diabetes dataset. You can follow a similar approach to address other use cases such as customer churn prediction and fraud detection.

Organizations the world over rely on the Exadata database platform to operate and manage Oracle databases, simplify data management, and ensure performance-intensive systems are running as expected. While Exadata workloads perform well on premises, customers are looking to achieve the scalability, flexibility, and cost benefits of AWS. Learn how Navisite tests the performance of Exadata workloads once they have been migrated to AWS using Oracle Real Application Testing (RAT).