This is the first step in the cloud adoption journey, which should ideally start with a well-documented cloud migration strategy that is aligned with the overall business strategy and goals of the organization. This step starts with assessing the complete IT environment to identify all applications and data assets that need to be moved to the cloud. It is critical to validate if indeed there is a cost or performance benefit for each of these assets when moved to the cloud. This validation will determine the order of priority for asset migration to the cloud. At the end of this step, a roadmap for migrating each identified workload should be developed. It is recommended to start with a few test workloads to experiment, learn and optimize within the selected cloud environment, before migrating business-critical applications (e.g. ERP or CRM) to the cloud

This is typically the second step in the cloud adoption journey, and it is where business-critical applications and related data assets are moved (modernized) to the cloud environment. Each application will need to be separately assessed for all inter-dependencies with databases and other applications and services. Moving an application to the cloud will require a certain level of refactoring and/or rearchitecting work. There will be cases when it would make sense to build a completely new cloud-native applications using microservices architecture to take full advantage of the cloud computing environment. This phase requires skilled resources from the application developer and cloud practitioners – or architects – working together. It will be important to consider the cost benefits of leveraging emerging cloud computing technologies such as containers and serverless deployment models at this phase.

Data modernization is a central pillar of Digital Transformation. Organizations today capture vast amounts of data and information from multiple sources both internally and externally, including but not limited to e-mail, social media, IoT devices and multiple other customer touchpoints. Harnessing the power of this v data is critical to building business competitiveness. CIOs are faced with requests from their business line managers for fast, accurate and timely information that can enable insights and better decision making. 

A key value of migrating and modernizing the data platform is to provide seamless and real-time access to the data to derive useful intelligence by leveraging the many BI and Analytics tools available on the cloud today. The process of modernizing the data platform involves three key steps; starting with an assessment to understand the available data within the organization and agree on a modernization strategy aligned with the business strategy. The next step involves building a roadmap for data migration starting with the least critical to the most business-critical data workloads. The final step is to implement data migration to the selected cloud vendor and leverage the vast range of available BI and Analytics tools to extract intelligence and insights to support faster and superior business decision making

The AI practice focuses on three areas (a) Reasoning: Building solutions that can learn and deliver intelligent conclusions from structured and unstructured data; (b) Understanding: Building solutions that interpret the meaning of data from text, voice, images and video, and (c) Interacting: Building solutions that interact with people in natural ways.

There are several pre-built AI APIs and Cognitive Services available today that can be integrated into existing applications to deliver capabilities around reasoning, understanding and interacting. For more complex needs, we can build custom models that apply machine learning and deep learning algorithms, which use customer data to train the model. Leveraging and integrating Artificial Intelligence to drive better customer engagement and product innovation needs a well-executed cloud strategy aligned with the overall business strategy of the organization, underpinned by a modern data platform.