Among system integrators that support large enterprises and government departments on their journey to the cloud, we have joined the herd in launching our Migration Factory practice. We are only in some ways “with them” – especially in the sense that we help our customers with their journey to the cloud with all the benefits of a factory:
- Standard Processes
- Assembly Line Efficiency
- Lowest possible Cost of Production
- Highest possible Quality Output
The similarities with other services firms stops there. Our approach is differentiated by a few key factors:
- Firstly, we do not have a large team waiting to be put to work to support the cloud migration factory. All of our highly trained data engineers, data scientists, and project managers can support our most sophisticated projects and our migration. We combine expertise with automation to deliver results faster. E.g. we have migration utilities built for specific databases and data warehouses
- Second, we believe workload, database, and data warehouse migration projects must start with a purpose. The area we specialize in is data democratization. We help build a source of intelligence for enterprises to make informed decisions. As a result, not only do our cloud migration projects have a utilitarian purpose of lowering infrastructure or licensing costs, they are designed to deliver innovative power in the form of smarter workflows and democratizing data across organizations immediately.
Moving to the cloud cannot be driven simply by cost. This is a critical need, but why else? Is it scale up and scale down agility? Is it integration with 3rd party apps and services to support new offerings or to improve customer engagement? For every cloud migration project, there is a business story, the one we hear often is users craving for data agility. We start with that business story and connect the data , integration, analytics, and machine learning into one homogeneous system.
Google Cloud’s “cloud maturity” model is a good place to start:
From Lift and Shift, to refactor to rebuild, SpringML provides a data-driven approach. With our knowledge of Google Cloud, we make digital transformation initiatives such as building a modern data platform for AI- Powered applications a reality very quickly.
Working with SpringML and Google Cloud allows new approaches to prove during a migration project. Best practices defined pre-COVID are less relevant. We need to create new people skills, process, and technology workflows for the new normal. Automation First, DevOps built- in. Scale, quality, and security, built- in. All while showing the value of why the process is being done in the first place.
Take, for example, our work with Iron Mountain. They have a momentous task of moving hundreds of workloads and databases to the cloud. How did we eat this elephant and provide significant business value from the very first sprint? We started by understanding what was most important to the business – Operations dashboards across all 200 business operations and across more than 50 countries. We started there with a data lake that we quickly built and then created Looker Dashboards off this newly formed data lake in BigQuery. Following a six week project to implement, the executive roadshow to educate on the dashboards took nearly as long but was key to supporting a widespread move to the cloud. The dashboards fundamentally changed what senior leadership thought was possible in such a short period of time. We start with the highest business value project and deliver iterative results with each project sprint.