Cloud migration is one of the most critical decisions facing technology organizations today. Yet many migrations fail not from technical issues, but from inadequate planning and analysis. The difference between a successful migration and a costly disaster often comes down to one thing: data-driven decision making.
With Sourcetable's cloud migration analysis capabilities, you can model scenarios, assess risks, and track outcomes with the precision that enterprise migrations demand. No more guesswork – just clear, actionable insights that drive successful technology transitions.
Comprehensive analysis transforms risky migrations into strategic advantages
Model TCO scenarios across multiple cloud providers and deployment strategies. Identify hidden costs before they impact your budget.
Quantify technical, operational, and business risks. Create mitigation strategies based on data, not assumptions.
Build realistic migration schedules with dependency mapping and resource allocation. Prevent delays with predictive modeling.
Predict application performance in cloud environments. Optimize configurations before deployment.
Monitor regulatory requirements and security standards throughout the migration process.
Measure migration success with clear metrics. Demonstrate business value to stakeholders.
See how organizations use data-driven analysis to execute successful cloud migrations
A major financial services company used migration analysis to move 200+ applications to cloud infrastructure. By modeling workload dependencies and cost scenarios, they reduced migration time by 40% and avoided $2M in unexpected costs.
A healthcare technology firm analyzed vendor lock-in risks across three cloud providers. Their analysis revealed optimal workload distribution, reducing overall costs by 25% while improving disaster recovery capabilities.
A manufacturing company analyzed the migration of 15-year-old ERP systems to modern cloud platforms. The analysis identified which systems to migrate, which to replace, and which to retire, saving $800K in unnecessary upgrades.
A rapidly growing tech startup used migration analysis to plan their infrastructure scaling. By modeling growth scenarios and cost projections, they chose a hybrid approach that reduced costs by 60% during their expansion phase.
A systematic approach to analyzing and planning your cloud migration strategy
Catalog your current infrastructure, applications, and dependencies. Import data from various sources to build a comprehensive inventory.
Create detailed TCO models comparing on-premises costs with cloud alternatives. Factor in migration expenses, training, and operational changes.
Identify potential migration risks using historical data and industry benchmarks. Quantify probability and impact for each risk factor.
Model different migration approaches: lift-and-shift, re-platforming, or complete re-architecture. Compare outcomes across scenarios.
Build realistic migration schedules with dependency mapping. Account for testing, training, and rollback procedures.
Define KPIs for migration success. Set up dashboards to track progress and measure outcomes against original projections.
One of the most critical aspects of cloud migration analysis is understanding the true cost implications. Here's how a typical analysis breaks down costs across different scenarios:
Successful migrations require systematic risk evaluation. A comprehensive framework typically includes:
Understanding how applications will perform in the cloud is crucial for migration success. Analysis typically covers:
The timeline varies based on infrastructure complexity, but most analyses take 2-6 weeks. Simple environments with fewer than 50 applications can be analyzed in 1-2 weeks, while complex enterprise environments with hundreds of applications may require 6-8 weeks for thorough analysis.
Key data sources include infrastructure monitoring tools, application performance data, cost and billing information, network topology maps, security audit reports, and compliance documentation. The more comprehensive your data, the more accurate your migration planning will be.
With proper analysis, cost projections typically achieve 85-95% accuracy for the first year. Accuracy depends on the quality of usage data, understanding of application scaling patterns, and consideration of hidden costs like data transfer and compliance requirements.
Start with critical applications that represent 80% of your infrastructure costs and business impact. However, comprehensive analysis of all applications often reveals unexpected dependencies and optimization opportunities that can significantly improve migration outcomes.
Legacy applications require special consideration. Analysis should evaluate three options: migrate as-is (lift-and-shift), modernize during migration (re-platform), or replace entirely (re-architect). Each approach has different costs, risks, and benefits that must be carefully weighed.
Migration analysis sometimes reveals that staying on-premises is more cost-effective in the short term. However, consider long-term factors like hardware refresh cycles, scalability needs, disaster recovery costs, and the strategic value of cloud capabilities. The analysis should inform your decision timeline, not just the final destination.
Training costs are often underestimated in migration planning. Include costs for cloud certifications, external training programs, consulting during transition periods, and potential salary adjustments for new skills. These investments typically pay for themselves within 12-18 months through improved operational efficiency.
Absolutely. Analysis can compare costs, performance, and capabilities across different cloud providers. Consider factors like regional availability, compliance certifications, service portfolios, and pricing models. Multi-cloud strategies often emerge from thorough vendor analysis.
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