Azure LifeCycle Solution - 8-week implementation

Understanding the Azure Cloud Journey

No two cloud journeys are identical, with many choices depending on compliance obligations, risk profile, existing infrastructure investment, availability of skills, and organisational culture.
Data^#3’s cloud experts recognise that understanding your organisation’s cloud profile is essential to knowing what workloads to move to the cloud, which cloud service is right, and when to transition. Our consultants are also skilled at managing the human change that comes with adopting new cloud workloads. Data^#3 uses a five-step approach to assess your application inventory prior to considering workload transition to Azure.

The five Rs are:

• Rehost
• Refactor
• Rearchitect
• Rebuild or
• Replace

If you’re already using Azure, it’s most likely that you’ve used the rehost approach with your IaaS lift-and-shift into Azure. The next step in your cloud adoption is to modernise your rehosted applications.

The Five Stage Pathway to Cloud Success

Our validated five-stage pathway to cloud success has been proven to successfully guide and support customers throughout their Azure journey. The Five Stage Pathway to Cloud Success begins with a clear plan – covering everything from network connectivity to security. From there,, it moves into migrating and optimising workloads for cloud; and then taps into advanced services to drive true digital innovation. At every stage, we draw on our deep expertise and advanced Azure certifications. Aligned to Microsoft’s Well Architected Framework and the Cloud Adoption Framework (CAF) methodologies, we are able to offer proven, consistent and repeatable services at all stages and a predictable price model.

Why Data#3?

• Australian Azure Centre of Excellence – Dedicated team driving cloud best practices
• One of the Largest Azure Engineering Teams in Australia – Deep technical expertise
• 100% Audited Onshore Operations – Australian-based, security cleared
• Complete End-to-End Cloud Management – From migration to ongoing optimization