Conducting a feasibility study is crucial before embarking on any cloud migration project. This helps set clear goals, identify potential issues, and determine if the move makes strategic and financial sense for your organization.
A feasibility study examines all aspects of a planned migration to gauge readiness, like technical requirements, business needs, costs, and risks. Key reasons these studies are vital include:
With a feasibility assessment, companies can avoid undergoing failed migrations, which saves money, time, and resources. Studies enable informed decisions on whether a cloud move is strategically aligned and primed for success.
When evaluating moving data to the cloud, focus areas should include:
This analysis highlights any data challenges to address, helps forecast ongoing costs, and enables designing the optimal cloud data architecture. Evaluating different database and storage options from prospective cloud providers at this stage is critical.
The on-premises IT landscape impacts cloud readiness. Considerations when assessing local workload include:
This analysis quantifies migration effort, risk potential, and required cloud resources. Some workloads will only be feasibly moved with application changes, for example.
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Evaluating IaaS, PaaS, and SaaS models based on application needs is critical. Considerations include:
Aligning apps and data to the optimal cloud delivery model enables efficiency, cost, and capability optimization during and after migration.
Data location has compliance and privacy implications. Cloud design considerations include:
Planning the right data architecture strategy ensures regulatory obligations are met cost-effectively as data moves to the cloud.
Many applications get migrated gradually via the hybrid cloud. This provides options including:
The optimal balance between private and public clouds depends on cost, risk, and application factors that should be modeled out.
Cloud migration tools are critical in ensuring a smooth and successful transition. However, not all provide the same level of reliability. When evaluating cloud migration tools, focus on automation, analytics, compliance, and integration capabilities.
Cloud management platforms (CMPs) can significantly increase the reliability of cloud migrations by providing automation, insight, and control across cloud environments. CMPs help coordinate tasks, ensure compliance, optimize spending, and give visibility into the migration process.
For example, CMP automation ensures consistent provisioning and configuration of resources, reducing the risk of errors. Analytics provide actionable insights to refine the migration. Governance guardrails enforce best practices for security and compliance. Together, these CMP capabilities drive migration success.
For organizations running SAP systems, SAP offers robust tools to help migrate those workloads to the cloud while enabling continuity of operations. SAP’s cloud migration tools provide detailed assessments, automated transformations, and advanced data protection.
SAP leverages “lift and shift” methodologies to simplify migrating complex legacy systems. Testing capabilities validate end-to-end functionality after migration. With migration experience across over 10,000 projects, SAP tools instill confidence.
While features and capabilities provide a baseline for reliability, the true test comes in real-world usage across the actual migration process. Thorough testing builds confidence that the tools will perform reliably at scale.
Testing should cover the entire migration journey: assessment, wave planning, pre-migration testing, pilot migrations, scaling to total production, and post-migration optimization. At each stage, robust validation ensures workloads, data, and business processes function per expectations after migrating to the cloud environment.
Cloud migration feasibility is only half of the task of managing data in the cloud. Measurable success is an excellent way to find out whether or not your cloud solution is working for you.
A critical success metric is aligning the cloud deployment approach and migration objectives or targets. For example, a goal to achieve agility and innovation is better matched to containers and microservices in the public cloud versus “lift and shift” migration to infrastructure-as-a-service.
Continuous tracking of leading indicators like application refactoring, deviation from initial cloud design, and use of cloud-native services gauges the fitness between deployment and target. Course correcting keeps the migration on a path to delivering strategic business value.
Comparing ongoing cloud costs versus on-premises spending provides one measurable indicator of a successful migration. Significant yet unexpected cost overruns signal poor execution.
Beyond cost reduction, success should be measured by cost confidence, the accuracy of TCO projections throughout the different migration stages. With reasonable cost controls, organizations can reliably forecast and manage cloud spending.
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